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11/17/2025 |
5:30 PM – 6:30 PM |
Room 13
Poster Session 2
Presentation Type: Posters
In Silico Evaluation of an Agentic AI Care Management System
Poster Number: P01
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Chronic Care Management, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Agentic AI may transform patient engagement and clinical decision support in care management. This study evaluates its use in a care management system. In a controlled simulation, AI agents interacted across clinical scenarios, achieving mean performance scores of 7.5-8.06 out of 10 across various clinical scenarios and social needs variations. The results indicate that an agentic system may be capable of providing a high-quality care management support.
Speaker:
J. Marc Overhage, MD, PhD
The Overhage Group
Author:
J. Marc Overhage, MD, PhD - The Overhage Group;
Poster Number: P01
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Chronic Care Management, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Agentic AI may transform patient engagement and clinical decision support in care management. This study evaluates its use in a care management system. In a controlled simulation, AI agents interacted across clinical scenarios, achieving mean performance scores of 7.5-8.06 out of 10 across various clinical scenarios and social needs variations. The results indicate that an agentic system may be capable of providing a high-quality care management support.
Speaker:
J. Marc Overhage, MD, PhD
The Overhage Group
Author:
J. Marc Overhage, MD, PhD - The Overhage Group;
J. Marc
Overhage,
MD, PhD - The Overhage Group
ICD-Guided Graph Learning with LLMs for Self-Supervised Multimodal EHR Data Retrieval
Poster Number: P02
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Clinical Decision Support, Large Language Models (LLMs), Machine Learning, Information Retrieval
Working Group: Natural Language Processing Working Group
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
We propose a framework for similar EHR retrieval by integrating tabular data, clinical text, and ICD codes. ICD-GNN captures ICD code representations for self-supervised pairing, while LLaMA fuses table and text. By leveraging contrastive learning, our approach enhances semantic representation while reducing dependence on labeled data. Experiments on MIMIC-III show significant improvements in retrieval and zero-shot ICD coding, demonstrating scalable multimodal synergy for clinical decision support.
Speaker:
Xiang Li, PhD
Massachusetts General Hospital and Harvard Medical School
Authors:
Yiyang Zhang, MS - South China University of Technology; Wenxiong Liao, PhD - South China University of Technology; Zhe Fang, PhD - Harvard T.H. Chan School of Public Health; Hui Ren, MD PhD MPH - MGH; Fang Zeng, PhD - Massachusetts General Hospital and Harvard Medical School; Hongmin Cai, PhD - South China University of Technology; Xiang Li, PhD - Massachusetts General Hospital and Harvard Medical School;
Poster Number: P02
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Clinical Decision Support, Large Language Models (LLMs), Machine Learning, Information Retrieval
Working Group: Natural Language Processing Working Group
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
We propose a framework for similar EHR retrieval by integrating tabular data, clinical text, and ICD codes. ICD-GNN captures ICD code representations for self-supervised pairing, while LLaMA fuses table and text. By leveraging contrastive learning, our approach enhances semantic representation while reducing dependence on labeled data. Experiments on MIMIC-III show significant improvements in retrieval and zero-shot ICD coding, demonstrating scalable multimodal synergy for clinical decision support.
Speaker:
Xiang Li, PhD
Massachusetts General Hospital and Harvard Medical School
Authors:
Yiyang Zhang, MS - South China University of Technology; Wenxiong Liao, PhD - South China University of Technology; Zhe Fang, PhD - Harvard T.H. Chan School of Public Health; Hui Ren, MD PhD MPH - MGH; Fang Zeng, PhD - Massachusetts General Hospital and Harvard Medical School; Hongmin Cai, PhD - South China University of Technology; Xiang Li, PhD - Massachusetts General Hospital and Harvard Medical School;
Xiang
Li,
PhD - Massachusetts General Hospital and Harvard Medical School
RDMA: Agent-Driven Rare Disease Data Discovery
Poster Number: P03
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Data Mining, Large Language Models (LLMs), Information Retrieval
Primary Track: Foundations
Rare diseases affect 1 in 10 Americans and require accurate diagnosis. While large language models show promise, API costs and privacy concerns limit their practical use. Additionally, existing datasets are limited and may not represent real patient populations. To assist in the construction of new rare disease datasets, we present our efficient Rare Disease Mining Agents (RDMA) framework, a cost-effective local solution that runs on consumer hardware while maintaining performance comparable to larger models.
Speaker:
Adam Cross, MD
University of Illinois-Chicago
Authors:
John Wu, B.S - University of Illinois at Urbana-Champaign; Adam Cross, MD - University of Illinois-Chicago; Jimeng Sun - University of Illinois at Urbana Champaign;
Poster Number: P03
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Data Mining, Large Language Models (LLMs), Information Retrieval
Primary Track: Foundations
Rare diseases affect 1 in 10 Americans and require accurate diagnosis. While large language models show promise, API costs and privacy concerns limit their practical use. Additionally, existing datasets are limited and may not represent real patient populations. To assist in the construction of new rare disease datasets, we present our efficient Rare Disease Mining Agents (RDMA) framework, a cost-effective local solution that runs on consumer hardware while maintaining performance comparable to larger models.
Speaker:
Adam Cross, MD
University of Illinois-Chicago
Authors:
John Wu, B.S - University of Illinois at Urbana-Champaign; Adam Cross, MD - University of Illinois-Chicago; Jimeng Sun - University of Illinois at Urbana Champaign;
Adam
Cross,
MD - University of Illinois-Chicago
Integrating AI Asthma Ascertainment Algorithms to Different Institutions: Challenges, Solutions, and Best Practices
Poster Number: P04
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Data Sharing, Data Standards, Clinical Decision Support, Fairness and elimination of bias, Real-World Evidence Generation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Asthma is a prevalent, heterogeneous condition; increased morbidity and mortality accompany delayed diagnoses. Diagnostic natural language processing (NLP) algorithms developed at Mayo Clinic have demonstrated high accuracy. However, the operationalized steps and challenges of implementing NLP algorithms in other large institutions are not yet well-established. This study explores these steps and challenges based on real-world experience integrating NLP asthma ascertainment algorithms between Mayo Clinic and Texas Children’s Hospital.
Speaker:
Jon Schening, MD
Baylor College of Medicine
Authors:
Jon Schening, MD - Baylor College of Medicine; Meera R. Gupta, MD - Baylor College of Medicine; Carla Davis, MD - Howard University College of Medicine; Shannon Collinson Niestrawski, PhD - Texas Children's Hospital; Bhakti Bavare, BS - Texas Children's Hospital; Sujan Kumar Mutyala, BS - Texas Children's Hospital; Elham Sagheb Hossein Pour, Master of Science - Mayo Clinic; Chung-Il Wi, MD - Mayo Clinic Minnesota; Young Juhn, M.D., M.P.H. - Mayo Clinic; Sunghwan Sohn, PhD - Mayo Clinic;
Poster Number: P04
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Data Sharing, Data Standards, Clinical Decision Support, Fairness and elimination of bias, Real-World Evidence Generation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Asthma is a prevalent, heterogeneous condition; increased morbidity and mortality accompany delayed diagnoses. Diagnostic natural language processing (NLP) algorithms developed at Mayo Clinic have demonstrated high accuracy. However, the operationalized steps and challenges of implementing NLP algorithms in other large institutions are not yet well-established. This study explores these steps and challenges based on real-world experience integrating NLP asthma ascertainment algorithms between Mayo Clinic and Texas Children’s Hospital.
Speaker:
Jon Schening, MD
Baylor College of Medicine
Authors:
Jon Schening, MD - Baylor College of Medicine; Meera R. Gupta, MD - Baylor College of Medicine; Carla Davis, MD - Howard University College of Medicine; Shannon Collinson Niestrawski, PhD - Texas Children's Hospital; Bhakti Bavare, BS - Texas Children's Hospital; Sujan Kumar Mutyala, BS - Texas Children's Hospital; Elham Sagheb Hossein Pour, Master of Science - Mayo Clinic; Chung-Il Wi, MD - Mayo Clinic Minnesota; Young Juhn, M.D., M.P.H. - Mayo Clinic; Sunghwan Sohn, PhD - Mayo Clinic;
Jon
Schening,
MD - Baylor College of Medicine
Evaluating AI-Driven Automation for Synthetic FHIR Data Generation
Poster Number: P05
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Data Standards, Patient / Person Generated Health Data (Patient Reported Outcomes), Evaluation, Workflow, Education and Training, Large Language Models (LLMs), User-centered Design Methods
Primary Track: Applications
Programmatic Theme: Clinical Informatics
For the first time, AI-driven automation is evaluated using OpenAI's Operator, with a focus on generating Synthea-based FHIR data. Operator, employed in a zero-shot context with Google Colab, was assessed for its ability to clone repositories, install dependencies, and generate synthetic clinical datasets. Foundational tasks were automated successfully, yet challenges remain in advanced configuration and error recovery. The evaluation offers insights for integrating automated AI solutions into research workflows.
Speaker:
Tia Pope, Ph.D. Candidate
North Carolina A&T State University
Author:
Ahmad Patooghy, Ph.D. - North Carolina A&T State University;
Poster Number: P05
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Data Standards, Patient / Person Generated Health Data (Patient Reported Outcomes), Evaluation, Workflow, Education and Training, Large Language Models (LLMs), User-centered Design Methods
Primary Track: Applications
Programmatic Theme: Clinical Informatics
For the first time, AI-driven automation is evaluated using OpenAI's Operator, with a focus on generating Synthea-based FHIR data. Operator, employed in a zero-shot context with Google Colab, was assessed for its ability to clone repositories, install dependencies, and generate synthetic clinical datasets. Foundational tasks were automated successfully, yet challenges remain in advanced configuration and error recovery. The evaluation offers insights for integrating automated AI solutions into research workflows.
Speaker:
Tia Pope, Ph.D. Candidate
North Carolina A&T State University
Author:
Ahmad Patooghy, Ph.D. - North Carolina A&T State University;
Tia
Pope,
Ph.D. Candidate - North Carolina A&T State University
A Systematic Approach for Identifying, Prioritizing, and Measuring the Impact of Generative AI Use Cases Using the AMA’s Return on Health Framework
Poster Number: P06
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Data Standards, Diversity, Equity, Inclusion, and Accessibility, Informatics Implementation, Large Language Models (LLMs), Surveys and Needs Analysis, Real-World Evidence Generation
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
This research applies the AMA Return on Health framework to evaluate ten GenAI use cases across provider and payer settings. Each use case was assessed for feasibility, value, and multidimensional impact across six domains. Findings highlight the importance of prioritizing AI initiatives not just by ROI, but by outcomes, equity, and experience—providing a practical, structured approach for responsible and effective GenAI adoption in healthcare.
Speaker:
Ashish Atreja, MD
UC Davis
Author:
Dharan Sankar Jaisankar, MS - GenServe.AI;
Poster Number: P06
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Data Standards, Diversity, Equity, Inclusion, and Accessibility, Informatics Implementation, Large Language Models (LLMs), Surveys and Needs Analysis, Real-World Evidence Generation
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
This research applies the AMA Return on Health framework to evaluate ten GenAI use cases across provider and payer settings. Each use case was assessed for feasibility, value, and multidimensional impact across six domains. Findings highlight the importance of prioritizing AI initiatives not just by ROI, but by outcomes, equity, and experience—providing a practical, structured approach for responsible and effective GenAI adoption in healthcare.
Speaker:
Ashish Atreja, MD
UC Davis
Author:
Dharan Sankar Jaisankar, MS - GenServe.AI;
Ashish
Atreja,
MD - UC Davis
Explainable AI for Automatic Detection and Localization of Shoulder Joint in MRIs
Poster Number: P07
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Deep Learning, Imaging Informatics
Primary Track: Applications
Programmatic Theme: Clinical Informatics
We developed an AI-based pipeline for automated shoulder joint detection and localization in MRIs. Three separate YOLOv11 models were trained on sagittal, coronal, and axial views, achieving near-perfect precision and recall. EigenCAM visualizations confirmed that the models focused on key anatomical regions, enhancing interpretability. This pipeline shows promise for improving shoulder joint assessment and will be integrated into future work for predicting shoulder instability.
Speaker:
Nickolas Littlefield, MS, Statistics
University of Pittsburgh
Authors:
Nickolas Littlefield, MS, Statistics - University of Pittsburgh; Fengyi Gao, MS - University of Pittsburgh; Michael Kann, BE - University of Pittsburgh; Nicole Myers, BSN - University of Pittsburgh; Qiangqiang Gu, Ph.D. - University of Pittsburgh; Albert Lin, MD - University of Pittsburgh; Ting Cong, MD - University of Pittsburgh; Ahmad P. Tafti, PhD - University of Pittsburgh;
Poster Number: P07
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Deep Learning, Imaging Informatics
Primary Track: Applications
Programmatic Theme: Clinical Informatics
We developed an AI-based pipeline for automated shoulder joint detection and localization in MRIs. Three separate YOLOv11 models were trained on sagittal, coronal, and axial views, achieving near-perfect precision and recall. EigenCAM visualizations confirmed that the models focused on key anatomical regions, enhancing interpretability. This pipeline shows promise for improving shoulder joint assessment and will be integrated into future work for predicting shoulder instability.
Speaker:
Nickolas Littlefield, MS, Statistics
University of Pittsburgh
Authors:
Nickolas Littlefield, MS, Statistics - University of Pittsburgh; Fengyi Gao, MS - University of Pittsburgh; Michael Kann, BE - University of Pittsburgh; Nicole Myers, BSN - University of Pittsburgh; Qiangqiang Gu, Ph.D. - University of Pittsburgh; Albert Lin, MD - University of Pittsburgh; Ting Cong, MD - University of Pittsburgh; Ahmad P. Tafti, PhD - University of Pittsburgh;
Nickolas
Littlefield,
MS, Statistics - University of Pittsburgh
Large Language Model Sycophancy in Simulated Clinical Encounters
Poster Number: P08
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Diagnostic Systems, Patient Engagement and Preferences, Legal, Ethical, Social and Regulatory Issues, Clinical Guidelines, Evaluation, Standards
Primary Track: Applications
Programmatic Theme: Clinical Informatics
LLMs show nearly perfect guideline adherence in single-response tests but demonstrate concerning sycophancy in dialogue. In our experiments, simulated patients obtained unnecessary interventions at high rates (40% for CT scans, 28.2% for antibiotics). Malpractice concerns increased deviations (57.5% for CT scans) while provider fatigue reduced them (15%). These findings reveal critical implementation risks and the need for specialized guardrails when deploying LLM-based clinical systems.
Speaker:
Yi Wang, B.S.
Stanford University
Authors:
Yi Wang, B.S. - Stanford University; Carl Preaiksaitis, MD, MEd - Stanford University; Christian Rose, MD - Stanford University, School of Medicine;
Poster Number: P08
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Diagnostic Systems, Patient Engagement and Preferences, Legal, Ethical, Social and Regulatory Issues, Clinical Guidelines, Evaluation, Standards
Primary Track: Applications
Programmatic Theme: Clinical Informatics
LLMs show nearly perfect guideline adherence in single-response tests but demonstrate concerning sycophancy in dialogue. In our experiments, simulated patients obtained unnecessary interventions at high rates (40% for CT scans, 28.2% for antibiotics). Malpractice concerns increased deviations (57.5% for CT scans) while provider fatigue reduced them (15%). These findings reveal critical implementation risks and the need for specialized guardrails when deploying LLM-based clinical systems.
Speaker:
Yi Wang, B.S.
Stanford University
Authors:
Yi Wang, B.S. - Stanford University; Carl Preaiksaitis, MD, MEd - Stanford University; Christian Rose, MD - Stanford University, School of Medicine;
Yi
Wang,
B.S. - Stanford University
Two-Stage Decoupling Framework for Variable-Length Glaucoma Prognosis
Poster Number: P09
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Diagnostic Systems, Deep Learning
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
Glaucoma is one of the leading causes of irreversible blindness worldwide. Two key challenges remain in glaucoma prediction:1. The rigidity of fixed-length time series design significantly affects model flexibility. 2. The conflict between the large parameter scale of end-to-end models and the limited size of the glaucoma dataset. To address these challenges, we propose a two-stage decoupling framework (TSDF) (Figure 1) for glaucoma prognosis that separates feature representation from temporal aggregation.
Speaker:
Yiran Song, doctor
University of Minnesota
Authors:
Yiran Song, doctor - University of Minnesota; Yikai Zhang, BA - University of Minnesota; Rui Zhang, PhD, FAMIA, FACMI - University of Minnesota, Twin Cities; Mingquan Lin, PhD - University of Minnesota;
Poster Number: P09
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Diagnostic Systems, Deep Learning
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
Glaucoma is one of the leading causes of irreversible blindness worldwide. Two key challenges remain in glaucoma prediction:1. The rigidity of fixed-length time series design significantly affects model flexibility. 2. The conflict between the large parameter scale of end-to-end models and the limited size of the glaucoma dataset. To address these challenges, we propose a two-stage decoupling framework (TSDF) (Figure 1) for glaucoma prognosis that separates feature representation from temporal aggregation.
Speaker:
Yiran Song, doctor
University of Minnesota
Authors:
Yiran Song, doctor - University of Minnesota; Yikai Zhang, BA - University of Minnesota; Rui Zhang, PhD, FAMIA, FACMI - University of Minnesota, Twin Cities; Mingquan Lin, PhD - University of Minnesota;
Yiran
Song,
doctor - University of Minnesota
Artificial Intelligence as Solution for Documentation Burden: A Dutch Nurses’ Viewpoint
Poster Number: P10
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Documentation Burden, Nursing Informatics, Surveys and Needs Analysis
Primary Track: Policy
Programmatic Theme: Clinical Informatics
The burden of documentation among nursing staff has been recognized internationally as a pressing issue. Policymakers, such as the Dutch government, believe artificial intelligence will provide a solution for this documentation burden. However, a first impression tells us that Dutch nursing staff members are divided in their viewpoints on whether artificial intelligence will actually provide a solution.
Speaker:
Kim De Groot, PhD RN
Netherlands Institute for Health Services Research
Authors:
Sofie Noorland, MSc RN - Netherlands Institute for Health Services Research; Anneke Francke, PhD RN - Netherlands Institute for Health Services Research;
Poster Number: P10
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Documentation Burden, Nursing Informatics, Surveys and Needs Analysis
Primary Track: Policy
Programmatic Theme: Clinical Informatics
The burden of documentation among nursing staff has been recognized internationally as a pressing issue. Policymakers, such as the Dutch government, believe artificial intelligence will provide a solution for this documentation burden. However, a first impression tells us that Dutch nursing staff members are divided in their viewpoints on whether artificial intelligence will actually provide a solution.
Speaker:
Kim De Groot, PhD RN
Netherlands Institute for Health Services Research
Authors:
Sofie Noorland, MSc RN - Netherlands Institute for Health Services Research; Anneke Francke, PhD RN - Netherlands Institute for Health Services Research;
Kim
De Groot,
PhD RN - Netherlands Institute for Health Services Research
LLM-as-a-Judge: Automating Clinical Summarization Evaluation Using Large Language Models and a Validated Human Instrument
Poster Number: P11
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Evaluation, Large Language Models (LLMs), Natural Language Processing
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Large Language Models (LLMs) are increasingly used for summarization in the electronic health
record (EHR), and scalable, validated approaches are needed to assess the accuracy and quality of their outputs. We recently developed and validated the Provider Documentation Summarization Quality Instrument (PDSQI-9) as a human evaluation instrument for multi-document LLM-generated clinical summaries using real-world EHR data. However, like other human evaluation frameworks, the PDSQI-9 required substantial human effort posing a significant scalability challenge for health systems. Therefore, we propose "LLM-as-a-Judge," an automated evaluation approach that maintains alignment with human assessments while gaining efficiency with automation.
Speaker:
Emma Croxford, PhD Student
University of Wisconsin Madison
Authors:
Yanjun Gao, PhD - University of Colorado; Elliot First - Epic; Nicholas Pellegrino, BS - Epic Systems; Miranda Schnier, BS - Epic Systems; John Caskey - University of Wisconsin-Madison; Graham Wills, PhD - UW Health; Guanhua Chen, PhD - UW Madison; Dmitriy Dligach, Ph.D. - Loyola University Chicago; Matthew Churpek, MD, MPH, PhD - University of Wisconsin-Madison; Anoop Mayampurath, PhD - University of Wisconsin - Madison; Frank Liao, PhD - University of Wisconsin, Madison - UW Health; Cherodeep Goswami; Karen Wong, MD, MPH, MIDS - Epic Systems; Brian Patterson, MD MPH - University of Wisconsin-Madison; Majid Afshar, MD, MSCR - University of Wisconsin - Madison;
Poster Number: P11
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Evaluation, Large Language Models (LLMs), Natural Language Processing
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Large Language Models (LLMs) are increasingly used for summarization in the electronic health
record (EHR), and scalable, validated approaches are needed to assess the accuracy and quality of their outputs. We recently developed and validated the Provider Documentation Summarization Quality Instrument (PDSQI-9) as a human evaluation instrument for multi-document LLM-generated clinical summaries using real-world EHR data. However, like other human evaluation frameworks, the PDSQI-9 required substantial human effort posing a significant scalability challenge for health systems. Therefore, we propose "LLM-as-a-Judge," an automated evaluation approach that maintains alignment with human assessments while gaining efficiency with automation.
Speaker:
Emma Croxford, PhD Student
University of Wisconsin Madison
Authors:
Yanjun Gao, PhD - University of Colorado; Elliot First - Epic; Nicholas Pellegrino, BS - Epic Systems; Miranda Schnier, BS - Epic Systems; John Caskey - University of Wisconsin-Madison; Graham Wills, PhD - UW Health; Guanhua Chen, PhD - UW Madison; Dmitriy Dligach, Ph.D. - Loyola University Chicago; Matthew Churpek, MD, MPH, PhD - University of Wisconsin-Madison; Anoop Mayampurath, PhD - University of Wisconsin - Madison; Frank Liao, PhD - University of Wisconsin, Madison - UW Health; Cherodeep Goswami; Karen Wong, MD, MPH, MIDS - Epic Systems; Brian Patterson, MD MPH - University of Wisconsin-Madison; Majid Afshar, MD, MSCR - University of Wisconsin - Madison;
Emma
Croxford,
PhD Student - University of Wisconsin Madison
Outpatient Parenteral Antimicrobial Therapy: Rapid Prototyping Leads to Faster Creation of a New Healthcare Delivery Model
Poster Number: P12
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Evaluation, Healthcare Quality, Infectious Diseases and Epidemiology, Patient Safety, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Rapid prototyping of a new healthcare delivery model allows simultaneous refinement of statistical models alongside development of interventions using real-time data. This increases the efficiency of each iteration, tailoring the statistical model to the exact interventions your clinical partners propose; a continuous dry run of the new healthcare delivery model.
Speaker:
Peter Martin, MS
Mayo Clinic
Authors:
Patrick Wilson, MPH - Mayo Clinic; Douglas Challener, MD - Mayo Clinic; Abinash Virk, MD - Mayo Clinic; Christina Rivera, Pharm.D., R.Ph. - Mayo Clinic;
Poster Number: P12
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Evaluation, Healthcare Quality, Infectious Diseases and Epidemiology, Patient Safety, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Rapid prototyping of a new healthcare delivery model allows simultaneous refinement of statistical models alongside development of interventions using real-time data. This increases the efficiency of each iteration, tailoring the statistical model to the exact interventions your clinical partners propose; a continuous dry run of the new healthcare delivery model.
Speaker:
Peter Martin, MS
Mayo Clinic
Authors:
Patrick Wilson, MPH - Mayo Clinic; Douglas Challener, MD - Mayo Clinic; Abinash Virk, MD - Mayo Clinic; Christina Rivera, Pharm.D., R.Ph. - Mayo Clinic;
Peter
Martin,
MS - Mayo Clinic
LongMedQA: A Multimodal Benchmark for Long Context Medical Question Answering with Electronic Health Records
Poster Number: P13
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Critical Care, Evaluation
Primary Track: Applications
With the emergence of large vision-language models (LVLMs), interest in their application to complex, multimodal electronic health records has grown. We introduce LongMedQA, a benchmark of 14,637 ICU records designed to evaluate LVLMs on long-form clinical data. Our findings reveal that current LVLMs face significant limitations in delivering reliable clinical insights, highlighting the need for more generalizable and robust models for real-world medical applications.
Speaker:
Kunyu Yu, M.S.
Duke-NUS Medical School
Authors:
Kunyu Yu, M.S. - Duke-NUS Medical School; Leyuan Yang, Research Assistant - National University of Singapore; Rui Yang, Master - Duke-NUS Medical School; Jingchi Liao, MSc - Duke-NUS Medical School; Xin Li, Master of science - Duke-NUS medical school; Huitao Li, Master - National University of Singapore; Nan Liu, PhD - National University of Singapore;
Poster Number: P13
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Critical Care, Evaluation
Primary Track: Applications
With the emergence of large vision-language models (LVLMs), interest in their application to complex, multimodal electronic health records has grown. We introduce LongMedQA, a benchmark of 14,637 ICU records designed to evaluate LVLMs on long-form clinical data. Our findings reveal that current LVLMs face significant limitations in delivering reliable clinical insights, highlighting the need for more generalizable and robust models for real-world medical applications.
Speaker:
Kunyu Yu, M.S.
Duke-NUS Medical School
Authors:
Kunyu Yu, M.S. - Duke-NUS Medical School; Leyuan Yang, Research Assistant - National University of Singapore; Rui Yang, Master - Duke-NUS Medical School; Jingchi Liao, MSc - Duke-NUS Medical School; Xin Li, Master of science - Duke-NUS medical school; Huitao Li, Master - National University of Singapore; Nan Liu, PhD - National University of Singapore;
Kunyu
Yu,
M.S. - Duke-NUS Medical School
A PubMed Parsing Agent for Detecting University of Kentucky Studies that "Meet People Where They Are"
Poster Number: P14
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Natural Language Processing, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
The University of Kentucky (UK) Center for Applied Artificial Intelligence has developed an artificial intelligence agent that parses the PubMed database for UK research that aligns with the "meeting people where they are" healthcare principle for providing empathetic care to patients in their homes or other preferred locations. This model leverages large language models and chain-of-thought prompting to reason through text to identify study methodologies and verify alignment with the principle.
Speaker:
Emily Collier, MSLS
University of Kentucky
Authors:
Cody Bumgardner, PhD - University of Kentucky; Samuel Armstrong, MS - University of Kentucky; Jeffery Talbert, PhD - University of Kentucky; Noah Perry, B.S. of Computer Science - University of Kentucky;
Poster Number: P14
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Natural Language Processing, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
The University of Kentucky (UK) Center for Applied Artificial Intelligence has developed an artificial intelligence agent that parses the PubMed database for UK research that aligns with the "meeting people where they are" healthcare principle for providing empathetic care to patients in their homes or other preferred locations. This model leverages large language models and chain-of-thought prompting to reason through text to identify study methodologies and verify alignment with the principle.
Speaker:
Emily Collier, MSLS
University of Kentucky
Authors:
Cody Bumgardner, PhD - University of Kentucky; Samuel Armstrong, MS - University of Kentucky; Jeffery Talbert, PhD - University of Kentucky; Noah Perry, B.S. of Computer Science - University of Kentucky;
Emily
Collier,
MSLS - University of Kentucky
Leveraging a Large Language Model to Optimize HIV Care Engagement
Poster Number: P15
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Chronic Care Management, Population Health, Infectious Diseases and Epidemiology
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Retention in care is crucial for patients with HIV, yet identifying patients who are disengaged from care presents challenges due to “silent transfer” between healthcare facilities. We found that combining structured electronic health record data with an LLM’s analysis of clinical notes can improve identification of out of care patients with HIV, allowing HIV care reengagement teams to focus resource-intensive reengagement efforts on the highest priority patients.
Speaker:
Maryam Aziz, MS
Duke University
Authors:
Maryam Aziz, MS - Duke University; Alifia Hasan, MBA - Duke University; Bradley Hintze, PhD - Duke University; Mark Sendak, MD, MPP - Duke Institute for Health Innovation; Suresh Balu, MS, MBA - Duke University; Nwora Lance Okeke, MD, MPH - Duke University; Jeffrey Jenks, MD, MPH - Durham County Department of Public Health; Naseem Alavian, MD, MPH - Duke University;
Poster Number: P15
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Chronic Care Management, Population Health, Infectious Diseases and Epidemiology
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Retention in care is crucial for patients with HIV, yet identifying patients who are disengaged from care presents challenges due to “silent transfer” between healthcare facilities. We found that combining structured electronic health record data with an LLM’s analysis of clinical notes can improve identification of out of care patients with HIV, allowing HIV care reengagement teams to focus resource-intensive reengagement efforts on the highest priority patients.
Speaker:
Maryam Aziz, MS
Duke University
Authors:
Maryam Aziz, MS - Duke University; Alifia Hasan, MBA - Duke University; Bradley Hintze, PhD - Duke University; Mark Sendak, MD, MPP - Duke Institute for Health Innovation; Suresh Balu, MS, MBA - Duke University; Nwora Lance Okeke, MD, MPH - Duke University; Jeffrey Jenks, MD, MPH - Durham County Department of Public Health; Naseem Alavian, MD, MPH - Duke University;
Maryam
Aziz,
MS - Duke University
Predicting Treatment Failure with Sodium-Glucose Cotransporter-2 Inhibitors (SGLT2i) in Patients with Type 2 Diabetes: A Novel Artificial Intelligence and Machine Learning Approach
Poster Number: P16
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Machine Learning, Real-World Evidence Generation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
The rate of treatment failure with sodium-glucose cotransporter-2 inhibitors (SGLT2i) is high among patients with type 2 diabetes (T2D). Accurately predicting SGLT2i treatment failure is critical to improve the clinical management of patients with T2D. This study used four machine learning models—logistic regression, Multilayer Perceptrons, Extreme Gradient Boosting, and Transformer—and found that all performed similarly in predicting SGLT2i treatment failure among patients with T2D.
Speaker:
Xi Tan, PhD PharmD
Novo Nordisk Inc.
Authors:
Doyoung Kwak, MS - Texas A&M University; Xi Tan, PhD PharmD - Novo Nordisk Inc.; Yuanjie Liang, MS - Novo Nordisk Inc.; Yiwen Cao, MS - Novo Nordisk Inc.; Caroline Swift, PhD - Novo Nordisk Inc.; Chalak Muhammad, MD MPH - Novo Nordisk Inc.; Min Ji Kwak, MD, MS, DrPH - University of Texas Health Science Center, McGovern Medical School;
Poster Number: P16
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Machine Learning, Real-World Evidence Generation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
The rate of treatment failure with sodium-glucose cotransporter-2 inhibitors (SGLT2i) is high among patients with type 2 diabetes (T2D). Accurately predicting SGLT2i treatment failure is critical to improve the clinical management of patients with T2D. This study used four machine learning models—logistic regression, Multilayer Perceptrons, Extreme Gradient Boosting, and Transformer—and found that all performed similarly in predicting SGLT2i treatment failure among patients with T2D.
Speaker:
Xi Tan, PhD PharmD
Novo Nordisk Inc.
Authors:
Doyoung Kwak, MS - Texas A&M University; Xi Tan, PhD PharmD - Novo Nordisk Inc.; Yuanjie Liang, MS - Novo Nordisk Inc.; Yiwen Cao, MS - Novo Nordisk Inc.; Caroline Swift, PhD - Novo Nordisk Inc.; Chalak Muhammad, MD MPH - Novo Nordisk Inc.; Min Ji Kwak, MD, MS, DrPH - University of Texas Health Science Center, McGovern Medical School;
Xi
Tan,
PhD PharmD - Novo Nordisk Inc.
A Multimodal Approach for Deep-Learning Classification of Vocal Fold Pathologies in Stroboscopy
Poster Number: P17
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Machine Learning, Cancer Prevention, Diagnostic Systems, Imaging Informatics, Surgery, Natural Language Processing, Deep Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Videolaryngostroboscopy (VLS) enables detailed visualization of vocal fold (VF) dynamics, but clinician interpretation remains subjective. A multimodal artificial intelligence classifier was developed by integrating VLS video, voice audio, and clinicodemographic data to classify healthy vocal folds (HVF), unilateral paralysis (UVFP), and VF lesions. The model achieved 72% accuracy, outperforming image-only (66%) and audio-only (61%) models. Results highlight the potential of multimodal fusion for vocal fold pathology classification and support future validation with larger datasets.
Speaker:
Sruthi Surapaneni, BS
Michigan State University College of Human Medicine
Authors:
Sruthi Surapaneni, BS - Michigan State University College of Human Medicine; Rachel Kuttler, BA - Sean Parker Institute for the Voice, Department of Otolaryngology–Head and Neck Surgery; Sean Setzen, MD - Sean Parker Institute for the Voice, Department of Otolaryngology–Head and Neck Surgery, Weill Cornell Medicine; Yeo Eun Kim, MD - Sean Parker Institute for the Voice, Department of Otolaryngology–Head and Neck Surgery, Weill Cornell Medicine; Peter Yao, MD - Sean Parker Institute for the Voice, Department of Otolaryngology–Head and Neck Surgery, Weill Cornell Medicine; Michael Pitman, MD - The Center for Voice and Swallowing, Department of Otolaryngology–Head and Neck Surgery, Columbia University Irving Medical Center; Lucian Sulica, MD - Sean Parker Institute for the Voice, Department of Otolaryngology–Head and Neck Surgery, Weill Cornell Medicine; Pegah Khosravi, PhD - Englander Institute for Precision Medicine, Weill Cornell Medicine; Anaïs Rameau, MD - Weill Cornell Medical College;
Poster Number: P17
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Machine Learning, Cancer Prevention, Diagnostic Systems, Imaging Informatics, Surgery, Natural Language Processing, Deep Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Videolaryngostroboscopy (VLS) enables detailed visualization of vocal fold (VF) dynamics, but clinician interpretation remains subjective. A multimodal artificial intelligence classifier was developed by integrating VLS video, voice audio, and clinicodemographic data to classify healthy vocal folds (HVF), unilateral paralysis (UVFP), and VF lesions. The model achieved 72% accuracy, outperforming image-only (66%) and audio-only (61%) models. Results highlight the potential of multimodal fusion for vocal fold pathology classification and support future validation with larger datasets.
Speaker:
Sruthi Surapaneni, BS
Michigan State University College of Human Medicine
Authors:
Sruthi Surapaneni, BS - Michigan State University College of Human Medicine; Rachel Kuttler, BA - Sean Parker Institute for the Voice, Department of Otolaryngology–Head and Neck Surgery; Sean Setzen, MD - Sean Parker Institute for the Voice, Department of Otolaryngology–Head and Neck Surgery, Weill Cornell Medicine; Yeo Eun Kim, MD - Sean Parker Institute for the Voice, Department of Otolaryngology–Head and Neck Surgery, Weill Cornell Medicine; Peter Yao, MD - Sean Parker Institute for the Voice, Department of Otolaryngology–Head and Neck Surgery, Weill Cornell Medicine; Michael Pitman, MD - The Center for Voice and Swallowing, Department of Otolaryngology–Head and Neck Surgery, Columbia University Irving Medical Center; Lucian Sulica, MD - Sean Parker Institute for the Voice, Department of Otolaryngology–Head and Neck Surgery, Weill Cornell Medicine; Pegah Khosravi, PhD - Englander Institute for Precision Medicine, Weill Cornell Medicine; Anaïs Rameau, MD - Weill Cornell Medical College;
Sruthi
Surapaneni,
BS - Michigan State University College of Human Medicine
Chronological Age Prediction based on DNA Methylation and Clinical Features
Poster Number: P18
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Omics (genomics, metabolomics, proteomics, transcriptomics, etc.) and Integrative Analyses, Machine Learning, Bioinformatics
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics
We develop an early-fusion chronological age prediction model for cancer patients by integrating DNA methylation and clinical features from The Cancer Genome Atlas. Traditional epigenetic clocks and machine learning models using clinical data showed limited accuracy. Our proposed early-fusion model combines DNA methylation with clinical variables, achieving superior performance. Evaluated on 4,203 patients, our model outperformed existing methods, with a 33% reduction in RMSE and 35% reduction in MAE, offering improved insights into patient health.
Speaker:
Jiaying Lu, PhD
Emory University School of Nursing's Center for Data Science
Authors:
Shuyue Jiang, BS - Emory University; Wenjing Ma, Doctorate - University of Michigan; Runze Yan, PhD - Emory University; Chang Su, PhD - Emory University; Jiaying Lu, PhD - Emory University School of Nursing's Center for Data Science;
Poster Number: P18
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Omics (genomics, metabolomics, proteomics, transcriptomics, etc.) and Integrative Analyses, Machine Learning, Bioinformatics
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics
We develop an early-fusion chronological age prediction model for cancer patients by integrating DNA methylation and clinical features from The Cancer Genome Atlas. Traditional epigenetic clocks and machine learning models using clinical data showed limited accuracy. Our proposed early-fusion model combines DNA methylation with clinical variables, achieving superior performance. Evaluated on 4,203 patients, our model outperformed existing methods, with a 33% reduction in RMSE and 35% reduction in MAE, offering improved insights into patient health.
Speaker:
Jiaying Lu, PhD
Emory University School of Nursing's Center for Data Science
Authors:
Shuyue Jiang, BS - Emory University; Wenjing Ma, Doctorate - University of Michigan; Runze Yan, PhD - Emory University; Chang Su, PhD - Emory University; Jiaying Lu, PhD - Emory University School of Nursing's Center for Data Science;
Jiaying
Lu,
PhD - Emory University School of Nursing's Center for Data Science
Artificial Intelligence in Patient Portals: A Scope Review
Poster Number: P19
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Patient / Person Generated Health Data (Patient Reported Outcomes), Personal Health Informatics, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Patient portals are secure online platforms enabling patients to access personal health information, communicate with care teams, and manage various healthcare needs remotely. With the rise of artificial intelligence (AI), there is growing interest in integrating AI into patient portals for enhanced functionality and improved outcomes. To explore this intersection, we conducted a scoping review following PRISMA guidelines. We searched literature databases (e.g., Ovid MEDLINE, EMBASE, Scopus, Web of Science, CINAHL, ACM Digital Library) for English-language articles from 1974 through mid-2024, identifying 1,413 records (1,278 unique). After screening, 47 articles met inclusion criteria.
Of these, the earliest was published in 2012, with 4–8 new publications each year from 2017 to 2023. Studies predominantly occurred in the United States (93.6%), with a small number from the Middle East. Approximately 60% focused on model development, while the remainder emphasized data mining. Most patient portals (74.5%) were vendor-based rather than standalone, and many (n=31) articles did not specify interface details; when specified, web-based portals were more frequent than mobile apps. Communication features dominated portal functionalities, followed by generic functions and health record access. About two-thirds of articles featured disease-focused analyses or disease-specific data. Though primarily implemented in outpatient settings, some portals addressed inpatient and emergency department contexts. The main clinical aims included patient support and safety or quality improvement.
Unstructured text—spanning portal messages and clinical notes—prevailed as the primary data source, often in datasets of 1K–10K records. AI tasks centered on text classification and text mining, while traditional machine learning and natural language processing remained most prevalent. However, large language models and generative AI are emerging approaches. These findings illuminate AI’s growing role in patient portals and suggest expanding opportunities for advanced analytics to enhance patient engagement, clinical decision-making, and overall healthcare delivery.
Speaker:
Ming Huang, PhD
UTHealth Houston
Authors:
Ming Huang, PhD - UTHealth Houston; Fang Chen, Master - University of Texas Health Science Center at Houston; Liwei Wang, MD, PhD - UTHealth; Xiaoyang Ruan, PhD - The University of Texas Health Science Center at Houston; Qiuhao Lu, Ph.D. - University of Texas Health Science Center at Houston; Jiawei Zhou; jinlian wang, PhD - UTHealth; Andrew Wen, MS - University of Texas Health Sciences Center at Houston; Zehan Li, MS - UTHealth Houston; Rui Li, Phd - UT health; Wanjing Wang, MS - UTHealth Houston; Jungwei Fan, Ph.D. - Mayo Clinic; Sunyang Fu, PhD, MHI - UTHealth; Hongfang Liu, PhD - University of Texas Health Science Center at Houston;
Poster Number: P19
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Patient / Person Generated Health Data (Patient Reported Outcomes), Personal Health Informatics, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Patient portals are secure online platforms enabling patients to access personal health information, communicate with care teams, and manage various healthcare needs remotely. With the rise of artificial intelligence (AI), there is growing interest in integrating AI into patient portals for enhanced functionality and improved outcomes. To explore this intersection, we conducted a scoping review following PRISMA guidelines. We searched literature databases (e.g., Ovid MEDLINE, EMBASE, Scopus, Web of Science, CINAHL, ACM Digital Library) for English-language articles from 1974 through mid-2024, identifying 1,413 records (1,278 unique). After screening, 47 articles met inclusion criteria.
Of these, the earliest was published in 2012, with 4–8 new publications each year from 2017 to 2023. Studies predominantly occurred in the United States (93.6%), with a small number from the Middle East. Approximately 60% focused on model development, while the remainder emphasized data mining. Most patient portals (74.5%) were vendor-based rather than standalone, and many (n=31) articles did not specify interface details; when specified, web-based portals were more frequent than mobile apps. Communication features dominated portal functionalities, followed by generic functions and health record access. About two-thirds of articles featured disease-focused analyses or disease-specific data. Though primarily implemented in outpatient settings, some portals addressed inpatient and emergency department contexts. The main clinical aims included patient support and safety or quality improvement.
Unstructured text—spanning portal messages and clinical notes—prevailed as the primary data source, often in datasets of 1K–10K records. AI tasks centered on text classification and text mining, while traditional machine learning and natural language processing remained most prevalent. However, large language models and generative AI are emerging approaches. These findings illuminate AI’s growing role in patient portals and suggest expanding opportunities for advanced analytics to enhance patient engagement, clinical decision-making, and overall healthcare delivery.
Speaker:
Ming Huang, PhD
UTHealth Houston
Authors:
Ming Huang, PhD - UTHealth Houston; Fang Chen, Master - University of Texas Health Science Center at Houston; Liwei Wang, MD, PhD - UTHealth; Xiaoyang Ruan, PhD - The University of Texas Health Science Center at Houston; Qiuhao Lu, Ph.D. - University of Texas Health Science Center at Houston; Jiawei Zhou; jinlian wang, PhD - UTHealth; Andrew Wen, MS - University of Texas Health Sciences Center at Houston; Zehan Li, MS - UTHealth Houston; Rui Li, Phd - UT health; Wanjing Wang, MS - UTHealth Houston; Jungwei Fan, Ph.D. - Mayo Clinic; Sunyang Fu, PhD, MHI - UTHealth; Hongfang Liu, PhD - University of Texas Health Science Center at Houston;
Ming
Huang,
PhD - UTHealth Houston
The molecular mechanisms of comorbidities between Cardiovascular Disease and Alzheimer’s Disease
Poster Number: P20
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Bioinformatics, Computational Biology, Controlled Terminologies, Ontologies, and Vocabularies
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
Cardiovascular disease (CVD) and Alzheimer’s disease (AD) share genetic comorbidities that may offer targets for precision medicine interventions. Using DiSGeNet and pathway analyses, we identified 54 common genes, notably GSK3B and APP. These genes significantly influence insulin resistance, inflammatory responses, vascular dysfunction, and neurodegeneration pathways, such as Sema3A signaling. Future studies should explore GSK3B and APP inhibitors to potentially mitigate these shared pathological mechanisms.
Speaker:
Avnish Sekharan, BS
Texas A&M University
Authors:
Vinayak Shenoy, PhD - Texas A&M School of Engineering Medicine; Shameer Khader, PhD - Northwell Health; Kamlesh Yadav, PhD - Texas A&M School of Engineering Medicine; Anoop Titus, MD - The Warren Alpert Medical School of Brown University;
Poster Number: P20
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Bioinformatics, Computational Biology, Controlled Terminologies, Ontologies, and Vocabularies
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
Cardiovascular disease (CVD) and Alzheimer’s disease (AD) share genetic comorbidities that may offer targets for precision medicine interventions. Using DiSGeNet and pathway analyses, we identified 54 common genes, notably GSK3B and APP. These genes significantly influence insulin resistance, inflammatory responses, vascular dysfunction, and neurodegeneration pathways, such as Sema3A signaling. Future studies should explore GSK3B and APP inhibitors to potentially mitigate these shared pathological mechanisms.
Speaker:
Avnish Sekharan, BS
Texas A&M University
Authors:
Vinayak Shenoy, PhD - Texas A&M School of Engineering Medicine; Shameer Khader, PhD - Northwell Health; Kamlesh Yadav, PhD - Texas A&M School of Engineering Medicine; Anoop Titus, MD - The Warren Alpert Medical School of Brown University;
Avnish
Sekharan,
BS - Texas A&M University
Thinking, Fast and Slow: DualReasoning Enhances Clinical Knowledge Extraction from Large Language Models
Poster Number: P21
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Bioinformatics, Large Language Models (LLMs), Artificial Intelligence, Natural Language Processing
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
DualReasoning combines slow (Chain-of-Thought) and fast (non-CoT) reasoning to extract disease-drug knowledge from large language models for effective and secure use of medication records. Augmenting clinical AI with this knowledge improves phenotyping performance on the All of Us cohort for Type 2 diabetes (AUC: 0.839 vs. 0.765), breast cancer (AUC: 0.775 vs. 0.741), and hypertension (AUC: 0.882 vs. 0.846), yielding results comparable to or better than those achieved by models that leverage cohort-specific drug-disease associations.
Speaker:
Haining Wang, MA
Indiana University
Authors:
Haining Wang, MA - Indiana University; Chenxi Xiong, MS - Purdue University; Haixu Tang, Dr. - Indiana University Bloomington; Suthat Liangpunsakul, MD - Indiana University School of Medicine; Jing Su, PhD - Indiana University School of Medicine;
Poster Number: P21
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Bioinformatics, Large Language Models (LLMs), Artificial Intelligence, Natural Language Processing
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
DualReasoning combines slow (Chain-of-Thought) and fast (non-CoT) reasoning to extract disease-drug knowledge from large language models for effective and secure use of medication records. Augmenting clinical AI with this knowledge improves phenotyping performance on the All of Us cohort for Type 2 diabetes (AUC: 0.839 vs. 0.765), breast cancer (AUC: 0.775 vs. 0.741), and hypertension (AUC: 0.882 vs. 0.846), yielding results comparable to or better than those achieved by models that leverage cohort-specific drug-disease associations.
Speaker:
Haining Wang, MA
Indiana University
Authors:
Haining Wang, MA - Indiana University; Chenxi Xiong, MS - Purdue University; Haixu Tang, Dr. - Indiana University Bloomington; Suthat Liangpunsakul, MD - Indiana University School of Medicine; Jing Su, PhD - Indiana University School of Medicine;
Haining
Wang,
MA - Indiana University
Using Mendelian randomization and large-scale proteomics data to identify drug targets for lipid-lowering
Poster Number: P22
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Causal Inference, Bioinformatics, Drug Discoveries, Repurposing, and Side-effect
Primary Track: Applications
We present a new Mendelian randomization-based drug-repurposing pipeline, implemented with proteomics data to investigate lipid-lowering drug targets. We regressed LDL-C and triglycerides measurements on the proteins’ polygenic risk scores to identify likely protein-lipid associations. We then used the pipeline with relevant summary statistics from the UKB-PPP and GLGC to identify causal relationships between levels of proteins and lipids. We found 6 proteins with highly significant (p << 0.05) causal estimates for both LDL-C and triglycerides.
Speaker:
Sergio Mundo, PhD
Vanderbilt University Medical Center
Authors:
Sergio Mundo, PhD - Vanderbilt University Medical Center; QiPing Feng, PhD - Vanderbilt University Medical Center; Wei-Qi Wei, MD, PhD - Vanderbilt University Medical Center;
Poster Number: P22
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Causal Inference, Bioinformatics, Drug Discoveries, Repurposing, and Side-effect
Primary Track: Applications
We present a new Mendelian randomization-based drug-repurposing pipeline, implemented with proteomics data to investigate lipid-lowering drug targets. We regressed LDL-C and triglycerides measurements on the proteins’ polygenic risk scores to identify likely protein-lipid associations. We then used the pipeline with relevant summary statistics from the UKB-PPP and GLGC to identify causal relationships between levels of proteins and lipids. We found 6 proteins with highly significant (p << 0.05) causal estimates for both LDL-C and triglycerides.
Speaker:
Sergio Mundo, PhD
Vanderbilt University Medical Center
Authors:
Sergio Mundo, PhD - Vanderbilt University Medical Center; QiPing Feng, PhD - Vanderbilt University Medical Center; Wei-Qi Wei, MD, PhD - Vanderbilt University Medical Center;
Sergio
Mundo,
PhD - Vanderbilt University Medical Center
The Disease-Diagnosis Semantic Gap: Implications for Causal Inference
Poster Number: P23
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Causal Inference, Knowledge Representation and Information Modeling, Information Extraction
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Clinicians diagnose patients using observations on both causes and effects of disease. The influence of disease effects increases diagnostic accuracy but it pradoxycally also creates a semantic gap between the disease and the diagnosis pushing them apart in the data generating process. This gap complicates the estimation of causal effects and the identification of which causes of the diagnosis mirror disease etiology and which reflect disease effects.
Speaker:
Marco Barbero-Mota, BSc, MRes
Vanderbilt University Medical Center
Authors:
Marco Barbero-Mota, BSc, MRes - Vanderbilt University Medical Center; Eric Strobl, MD, PhD - University of Pittsburgh; William Stead, MD - Vanderbilt University Medical Center; Thomas Lasko, MD, PhD - Vanderbilt University Medical Center;
Poster Number: P23
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Causal Inference, Knowledge Representation and Information Modeling, Information Extraction
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Clinicians diagnose patients using observations on both causes and effects of disease. The influence of disease effects increases diagnostic accuracy but it pradoxycally also creates a semantic gap between the disease and the diagnosis pushing them apart in the data generating process. This gap complicates the estimation of causal effects and the identification of which causes of the diagnosis mirror disease etiology and which reflect disease effects.
Speaker:
Marco Barbero-Mota, BSc, MRes
Vanderbilt University Medical Center
Authors:
Marco Barbero-Mota, BSc, MRes - Vanderbilt University Medical Center; Eric Strobl, MD, PhD - University of Pittsburgh; William Stead, MD - Vanderbilt University Medical Center; Thomas Lasko, MD, PhD - Vanderbilt University Medical Center;
Marco
Barbero-Mota,
BSc, MRes - Vanderbilt University Medical Center
Beyond Documentation: Assessment of Large Language Model Performance in Summarizing Cancer Wound Care Nursing Notes
Poster Number: P24
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Large Language Models (LLMs), Nursing Informatics, Artificial Intelligence, Cancer Prevention
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study evaluated the performance of summarization using a large language model (LLM) on unstructured cancer wound nursing records. One hundred records generated based on electronic health records were analyzed using 17 variables. Tree of thought, reasoning and action prompting achieved the highest summarization performance. Wound cleansing showed the best performance, followed by presence of infection, pain, odor, and dressing materials. This study can help reduce documentation burden for nurses caring for cancer wound patients.
Speaker:
Yeonju Kim, MPH, RN
Yonsei University
Authors:
Yeonju Kim, MPH, RN - Yonsei University College of Nursing; Jiin Kim, MPH, RN - Yonsei University College of Nursing; Yeonwoo Kim, BSN, RN - Yonsei University College of Nursing; Mona Choi, PhD - Yonsei University College of Nursing;
Poster Number: P24
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Large Language Models (LLMs), Nursing Informatics, Artificial Intelligence, Cancer Prevention
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study evaluated the performance of summarization using a large language model (LLM) on unstructured cancer wound nursing records. One hundred records generated based on electronic health records were analyzed using 17 variables. Tree of thought, reasoning and action prompting achieved the highest summarization performance. Wound cleansing showed the best performance, followed by presence of infection, pain, odor, and dressing materials. This study can help reduce documentation burden for nurses caring for cancer wound patients.
Speaker:
Yeonju Kim, MPH, RN
Yonsei University
Authors:
Yeonju Kim, MPH, RN - Yonsei University College of Nursing; Jiin Kim, MPH, RN - Yonsei University College of Nursing; Yeonwoo Kim, BSN, RN - Yonsei University College of Nursing; Mona Choi, PhD - Yonsei University College of Nursing;
Yeonju
Kim,
MPH, RN - Yonsei University
Genetic Risk Factors for Kidney Disease: A Statistical Approach to Early Detection
Poster Number: P25
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Machine Learning, Precision Medicine
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Genetic screening can help identify individuals at risk for kidney disease. This study analyzed genetic markers linked to chronic kidney disease (CKD) and evaluated their predictive power. Using statistical models, we assessed 500 participants for variations in APOL1 and UMOD genes. Findings revealed an 82% accuracy in predicting CKD risk, highlighting the potential of genetic screening in early diagnosis and personalized healthcare.
Speaker:
Akshitha G, High School Student
Rock Hill High School
Authors:
Akshitha G, High School Student - Rock Hill High School; Christie Martin, PhD, MPH, RN-BC, LHIT-HP - University of Minnesota School of Nursing;
Poster Number: P25
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Machine Learning, Precision Medicine
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Genetic screening can help identify individuals at risk for kidney disease. This study analyzed genetic markers linked to chronic kidney disease (CKD) and evaluated their predictive power. Using statistical models, we assessed 500 participants for variations in APOL1 and UMOD genes. Findings revealed an 82% accuracy in predicting CKD risk, highlighting the potential of genetic screening in early diagnosis and personalized healthcare.
Speaker:
Akshitha G, High School Student
Rock Hill High School
Authors:
Akshitha G, High School Student - Rock Hill High School; Christie Martin, PhD, MPH, RN-BC, LHIT-HP - University of Minnesota School of Nursing;
Akshitha
G,
High School Student - Rock Hill High School
Theory-Informed Co-Design for a Pragmatic Remote Monitoring Intervention: Insights from Remote Monitoring for Equity in Advancing the Control of Hypertension (REACH) Study
Poster Number: P26
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Qualitative Methods, User-centered Design Methods, Health Equity, Patient / Person Generated Health Data (Patient Reported Outcomes), Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Remote blood pressure (BP) monitoring is underutilized in safety net settings. Using open-ended interviews, we co-designed with patients to refine the REACH intervention, integrating remote BP monitors with support in the San Francisco Health Network. Interviews (n=15) revealed preferences for portable, multi-power devices, cellular syncing, intuitive app interfaces, and multilingual options. Patients valued tracking but required training. Addressing these needs can enhance usability, fostering engagement in hypertension self-management.
Speaker:
Robert Ellis, PhD, MHA
University of California Davis (Center for Healthcare Policy and Research)
Authors:
Robert Ellis, PhD, MHA - University of California Davis (Center for Healthcare Policy and Research); Melissa Gosdin, PhD - University of California Davis (Center for Healthcare Policy and Research); Cindy Kim, MPH - University of California San Francisco; Isabel Luna, BA - University of California San Francisco; Faviola Garcia; Christian Gutierrez, BS - University of California San Francisco; Monica Naranjo, Bachelor's - UCSF; Elaine Khoong, MD, MS - University of California San Francisco; Urmimala Sarkar, MD MPH - UCSF; Courtney Lyles, PhD - UC Davis Center for Healthcare Policy and Research;
Poster Number: P26
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Qualitative Methods, User-centered Design Methods, Health Equity, Patient / Person Generated Health Data (Patient Reported Outcomes), Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Remote blood pressure (BP) monitoring is underutilized in safety net settings. Using open-ended interviews, we co-designed with patients to refine the REACH intervention, integrating remote BP monitors with support in the San Francisco Health Network. Interviews (n=15) revealed preferences for portable, multi-power devices, cellular syncing, intuitive app interfaces, and multilingual options. Patients valued tracking but required training. Addressing these needs can enhance usability, fostering engagement in hypertension self-management.
Speaker:
Robert Ellis, PhD, MHA
University of California Davis (Center for Healthcare Policy and Research)
Authors:
Robert Ellis, PhD, MHA - University of California Davis (Center for Healthcare Policy and Research); Melissa Gosdin, PhD - University of California Davis (Center for Healthcare Policy and Research); Cindy Kim, MPH - University of California San Francisco; Isabel Luna, BA - University of California San Francisco; Faviola Garcia; Christian Gutierrez, BS - University of California San Francisco; Monica Naranjo, Bachelor's - UCSF; Elaine Khoong, MD, MS - University of California San Francisco; Urmimala Sarkar, MD MPH - UCSF; Courtney Lyles, PhD - UC Davis Center for Healthcare Policy and Research;
Robert
Ellis,
PhD, MHA - University of California Davis (Center for Healthcare Policy and Research)
Factors associated with Chronic Pain Clinical Decision Support Use in Primary Care
Poster Number: P27
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Chronic Care Management, Real-World Evidence Generation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study examines factors associated with the use of a clinical decision support (CDS) tool in primary care for chronic pain management. Using data from a pragmatic randomized controlled trial, we analyzed clinician CDS use at 145,511 patient visits. Results showed that CDS use was associated with visits for new patient, attached pain diagnoses, and patients with long-term opioid therapy prescriptions. Findings highlight the importance of clinician experience and patient characteristics in optimizing CDS adoption.
Speaker:
Emma McCord, MPA
Indiana University Richard M. Fairbanks School of Public Health
Authors:
Nate Apathy, PhD - University of Maryland; Justin Blackburn - Regenstrief Institute; Ann M. Holmes, PhD - Indiana University Richard M. Fairbanks School of Public Health; Lindsey Sanner, MPH - Indiana University Richard M. Fairbanks School of Public Health; Christopher Harle, PhD - Indiana University; Olena Mazurenko, MD, PhD - Indiana University Fairbanks School of Public Health;
Poster Number: P27
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Chronic Care Management, Real-World Evidence Generation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study examines factors associated with the use of a clinical decision support (CDS) tool in primary care for chronic pain management. Using data from a pragmatic randomized controlled trial, we analyzed clinician CDS use at 145,511 patient visits. Results showed that CDS use was associated with visits for new patient, attached pain diagnoses, and patients with long-term opioid therapy prescriptions. Findings highlight the importance of clinician experience and patient characteristics in optimizing CDS adoption.
Speaker:
Emma McCord, MPA
Indiana University Richard M. Fairbanks School of Public Health
Authors:
Nate Apathy, PhD - University of Maryland; Justin Blackburn - Regenstrief Institute; Ann M. Holmes, PhD - Indiana University Richard M. Fairbanks School of Public Health; Lindsey Sanner, MPH - Indiana University Richard M. Fairbanks School of Public Health; Christopher Harle, PhD - Indiana University; Olena Mazurenko, MD, PhD - Indiana University Fairbanks School of Public Health;
Emma
McCord,
MPA - Indiana University Richard M. Fairbanks School of Public Health
Variation in Healthcare System Performance in the Leapfrog Group’s 2024 CPOE Evaluation Tool
Poster Number: P28
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Evaluation, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Seven healthcare systems which took the Leapfrog Group's 2024 CPOE Evaluation Tool exhibited variation in performance. Notably, performance in advanced decision support areas had the most variation. For example, System B's mean drug monitoring score was 17.7%, while scores ranged from 0% to 100%. More critically, we found that in System B, 8 hospitals were asked to prescribe a 100 times overdose of digoxin. Of the eight hospitals, seven received an alert while one hospital did not.
Speaker:
Zoe Co, BS
University of Michigan
Authors:
David Classen, MD - University of Utah School of Medicine; Melissa Danforth, BA - The Leapfrog Group; David Bates, MD - Mass General Brigham; Harvard University;
Poster Number: P28
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Evaluation, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Seven healthcare systems which took the Leapfrog Group's 2024 CPOE Evaluation Tool exhibited variation in performance. Notably, performance in advanced decision support areas had the most variation. For example, System B's mean drug monitoring score was 17.7%, while scores ranged from 0% to 100%. More critically, we found that in System B, 8 hospitals were asked to prescribe a 100 times overdose of digoxin. Of the eight hospitals, seven received an alert while one hospital did not.
Speaker:
Zoe Co, BS
University of Michigan
Authors:
David Classen, MD - University of Utah School of Medicine; Melissa Danforth, BA - The Leapfrog Group; David Bates, MD - Mass General Brigham; Harvard University;
Zoe
Co,
BS - University of Michigan
Harnessing the Power of Non-Interruptive Clinical Decision Support to Prevent Safety Events
Poster Number: P29
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Healthcare Quality, Nursing Informatics
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Clinical Decision Support (CDS) is routinely integrated into prevention strategies for safety events despite evidence indicating minimal improvements in healthcare processes and outcomes. Evaluating CDS's impact on rare events is challenging, underscoring the need for effective design and metrics to avoid cognitive overload in clinicians. This study examines the effect of non-interruptive CDS in preventing the risks associated with prolonged naso-jejunal tube placements in a pediatric hospital setting
Speaker:
Sarah Thompson, MSHIMI, BSN, RN
Children's Healthcare of Atlanta
Authors:
Kasey Church, MS, RN,NE-BC - Children's Healthcare of Atlanta; Sara Holley, MSN, RN - Children's Healthcare of Atlanta; Anthony Piazza, MD - Emory University and Children's Healthcare of Atlanta; Swaminathan Kandaswamy, PhD - Emory University School of Medicine; Julia Yarahuan, MD, MBI - Children's Healthcare of Atlanta/Emory University;
Poster Number: P29
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Healthcare Quality, Nursing Informatics
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Clinical Decision Support (CDS) is routinely integrated into prevention strategies for safety events despite evidence indicating minimal improvements in healthcare processes and outcomes. Evaluating CDS's impact on rare events is challenging, underscoring the need for effective design and metrics to avoid cognitive overload in clinicians. This study examines the effect of non-interruptive CDS in preventing the risks associated with prolonged naso-jejunal tube placements in a pediatric hospital setting
Speaker:
Sarah Thompson, MSHIMI, BSN, RN
Children's Healthcare of Atlanta
Authors:
Kasey Church, MS, RN,NE-BC - Children's Healthcare of Atlanta; Sara Holley, MSN, RN - Children's Healthcare of Atlanta; Anthony Piazza, MD - Emory University and Children's Healthcare of Atlanta; Swaminathan Kandaswamy, PhD - Emory University School of Medicine; Julia Yarahuan, MD, MBI - Children's Healthcare of Atlanta/Emory University;
Sarah
Thompson,
MSHIMI, BSN, RN - Children's Healthcare of Atlanta
Implementing Clinical Decision Support to Facilitate Opt-out HIV Testing for Admitted Adolescent Patients
Poster Number: P30
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Infectious Diseases and Epidemiology, Health Equity
Primary Track: Applications
Programmatic Theme: Clinical Informatics
In this pre-post intervention study, we assessed the impact of opt-out HIV testing on screening rates among adolescents admitted to the pediatric hospital medicine (PHM) service. After implementing monthly education and clinical decision support (CDS), HIV testing increased from 10.5% to 14.8%. Ongoing monitoring to assess for special cause variation and user feedback collection will help to further refine the CDS approach to improve HIV screening in inpatient settings.
Speaker:
Gargi Mukherjee, MD
Emory University School of Medicine
Authors:
Gargi Mukherjee, MD - Emory University School of Medicine; Annie Sadler, MD - Emory University; Jordan Bryant, MPH - Emory University; Melissa Cameron, MPH - Emory University; Sandy Francois, MSc - Emory University; Bridget Wynn, MPH - Emory University; Sarah Thompson, RN, BSN, MSHMI - Children's Healthcare of Atlanta; Melissa Popkin, RN - Children's Healthcare of Atlanta; Julia Yarahuan, MD, MBI - Children's Healthcare of Atlanta/Emory University; Swaminathan Kandaswamy, PhD - Emory University School of Medicine; Evan Orenstein, MD - Children's Healthcare of Atlanta; Andres Camacho-Gonzalez, MD - Children's Healthcare of Atlanta/Emory University; Mark Griffiths, MD - Children's Healthcare of Atlanta/Emory University; Lauren Middlebrooks, MD - Children's Healthcare of Atlanta/Emory University; Claudia Morris, MD - Children's Healthcare of Atlanta/Emory University;
Poster Number: P30
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Infectious Diseases and Epidemiology, Health Equity
Primary Track: Applications
Programmatic Theme: Clinical Informatics
In this pre-post intervention study, we assessed the impact of opt-out HIV testing on screening rates among adolescents admitted to the pediatric hospital medicine (PHM) service. After implementing monthly education and clinical decision support (CDS), HIV testing increased from 10.5% to 14.8%. Ongoing monitoring to assess for special cause variation and user feedback collection will help to further refine the CDS approach to improve HIV screening in inpatient settings.
Speaker:
Gargi Mukherjee, MD
Emory University School of Medicine
Authors:
Gargi Mukherjee, MD - Emory University School of Medicine; Annie Sadler, MD - Emory University; Jordan Bryant, MPH - Emory University; Melissa Cameron, MPH - Emory University; Sandy Francois, MSc - Emory University; Bridget Wynn, MPH - Emory University; Sarah Thompson, RN, BSN, MSHMI - Children's Healthcare of Atlanta; Melissa Popkin, RN - Children's Healthcare of Atlanta; Julia Yarahuan, MD, MBI - Children's Healthcare of Atlanta/Emory University; Swaminathan Kandaswamy, PhD - Emory University School of Medicine; Evan Orenstein, MD - Children's Healthcare of Atlanta; Andres Camacho-Gonzalez, MD - Children's Healthcare of Atlanta/Emory University; Mark Griffiths, MD - Children's Healthcare of Atlanta/Emory University; Lauren Middlebrooks, MD - Children's Healthcare of Atlanta/Emory University; Claudia Morris, MD - Children's Healthcare of Atlanta/Emory University;
Gargi
Mukherjee,
MD - Emory University School of Medicine
Standardizing Enterprise Reporting to Improve Health System Performance
Poster Number: P31
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Informatics Implementation, Healthcare Quality, Information Visualization
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Hospitals typically utilize multiple scorecards to track various key performance indicators (KPIs). A proliferation of scorecards and misalignment among these KPIs can create organizational disconnect. Geisinger created a cascading scorecard to address this challenge, translating clearly defined strategic goals into aligned objectives at the hospital, institute, and nursing unit levels.
Speaker:
Michelle Dempsey, BS CPBI
Geisinger
Authors:
Eric Reich, MSHI - Geisinger; Casey Cauthorn, MIE - Geisinger Health System; Welsey Ray, BS - Geisinger Health System; Jason Puckey, MHA - Geisinger Health System; David Vawdrey, PhD - Geisinger;
Poster Number: P31
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Informatics Implementation, Healthcare Quality, Information Visualization
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Hospitals typically utilize multiple scorecards to track various key performance indicators (KPIs). A proliferation of scorecards and misalignment among these KPIs can create organizational disconnect. Geisinger created a cascading scorecard to address this challenge, translating clearly defined strategic goals into aligned objectives at the hospital, institute, and nursing unit levels.
Speaker:
Michelle Dempsey, BS CPBI
Geisinger
Authors:
Eric Reich, MSHI - Geisinger; Casey Cauthorn, MIE - Geisinger Health System; Welsey Ray, BS - Geisinger Health System; Jason Puckey, MHA - Geisinger Health System; David Vawdrey, PhD - Geisinger;
Michelle
Dempsey,
BS CPBI - Geisinger
Needs-Based Clinical Decision Support for Difficult Airway Situational Awareness in Inpatient Providers
Poster Number: P32
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Informatics Implementation, User-centered Design Methods
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Our study investigates the effect of needs-informed CDS tools on situational awareness in patients with difficult airways. We implemented a pre- and post-intervention survey methodology to identify differences in provider perceptions and confidence in the next steps in case of respiratory decompensation. In our pre-intervention surveys, we found a significant difference in provider confidence regarding contingency planning in patients with difficult airways. We plan to collect post-intervention survey results in the coming months.
Speaker:
Harrison Cowart, MD
Emory University
Authors:
Harrison Cowart, MD - Emory University; Melissa Popkin, BSN, RN - Children's Healthcare of Atlanta; Sarah Thompson, MSHIMI, BSN, RN - Children's Healthcare of Atlanta; Swaminathan Kandaswamy, PhD - Emory University School of Medicine; Julia Yarahuan, MD, MBI - Children's Healthcare of Atlanta/Emory University;
Poster Number: P32
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Informatics Implementation, User-centered Design Methods
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Our study investigates the effect of needs-informed CDS tools on situational awareness in patients with difficult airways. We implemented a pre- and post-intervention survey methodology to identify differences in provider perceptions and confidence in the next steps in case of respiratory decompensation. In our pre-intervention surveys, we found a significant difference in provider confidence regarding contingency planning in patients with difficult airways. We plan to collect post-intervention survey results in the coming months.
Speaker:
Harrison Cowart, MD
Emory University
Authors:
Harrison Cowart, MD - Emory University; Melissa Popkin, BSN, RN - Children's Healthcare of Atlanta; Sarah Thompson, MSHIMI, BSN, RN - Children's Healthcare of Atlanta; Swaminathan Kandaswamy, PhD - Emory University School of Medicine; Julia Yarahuan, MD, MBI - Children's Healthcare of Atlanta/Emory University;
Harrison
Cowart,
MD - Emory University
Implementing an Electronic Decision Aid to Reduce Bleeding Risk with Anticoagulants: A Qualitative Study with Patients and Clinicians
Poster Number: P33
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Informatics Implementation, Qualitative Methods, User-centered Design Methods, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This qualitative pre-implementation study explores the integration of DDInteract, an electronic decision aid designed to reduce bleeding risk from anticoagulant and NSAID interactions. Through interviews with 33 clinicians and patients across three academic health centers, key barriers and facilitators were identified, including workflow integration, tool usability, and time constraints. Findings will inform future refinements of DDInteract and guide broader implementation of shared decision-making tools in clinical practice.
Speaker:
Alex Becker, MS
Department of Biomedical Informatics, Vanderbilt University
Authors:
Alex Becker, MS - Department of Biomedical Informatics, Vanderbilt University; Kemberlee Bonnet, MA - Vanderbilt University; Mauli Shah, MPH - Vanderbilt University Medical Center; Elizabeth Dang, BA - University of Tennessee - Health Science Center College of Medicine; Jessica Ancker, MPH, PhD, FACMI - Vanderbilt University Medical Center; Katy Trinkley, PharmD, PhD - University of Colorado; Thomas Reese, PharmD, PhD - Vanderbilt; Daniel Malone, PhD, FAMCP - University of Utah;
Poster Number: P33
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Informatics Implementation, Qualitative Methods, User-centered Design Methods, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This qualitative pre-implementation study explores the integration of DDInteract, an electronic decision aid designed to reduce bleeding risk from anticoagulant and NSAID interactions. Through interviews with 33 clinicians and patients across three academic health centers, key barriers and facilitators were identified, including workflow integration, tool usability, and time constraints. Findings will inform future refinements of DDInteract and guide broader implementation of shared decision-making tools in clinical practice.
Speaker:
Alex Becker, MS
Department of Biomedical Informatics, Vanderbilt University
Authors:
Alex Becker, MS - Department of Biomedical Informatics, Vanderbilt University; Kemberlee Bonnet, MA - Vanderbilt University; Mauli Shah, MPH - Vanderbilt University Medical Center; Elizabeth Dang, BA - University of Tennessee - Health Science Center College of Medicine; Jessica Ancker, MPH, PhD, FACMI - Vanderbilt University Medical Center; Katy Trinkley, PharmD, PhD - University of Colorado; Thomas Reese, PharmD, PhD - Vanderbilt; Daniel Malone, PhD, FAMCP - University of Utah;
Alex
Becker,
MS - Department of Biomedical Informatics, Vanderbilt University
Implementing Nudges to Promote Provider Adoption of Clinical Decision Support: Study Protocol for a Stepped-Wedge Cluster Randomized, Hybrid Type III Trial of an EHR-Agnostic Pulmonary Embolism Risk Tool
Poster Number: P34
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Informatics Implementation, Clinical Guidelines
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
About one-third of the 2.4 million computed tomography (CT) scans ordered yearly to evaluate for pulmonary embolism (PE) in U.S. emergency departments (ED) are avoidable. The study objective will be to implement a tool with a nudge to improve provider adoption of guideline-concordant CT ordering for PE in a multi-site, randomized trial. This trial will advance evidence on behavioral strategies using new technological methods, demonstrating how nudges can improve guideline adherence in the ED.
Speaker:
Sundas Khan, MD
Baylor College of Medicine & Michael E. DeBakey VA Medical Center
Authors:
Ynhi Thomas, MD, MPH, MSc - Baylor College of Medicine; Usman Mir, MBBS, MPH - Baylor College of Medicine; Thomas McGinn, MD MPH - Dignity Health / CommonSpirit Health Corporate; Katherine Dauber-Decker, PhD - Northwell Health; Jeffrey Solomon - Northwell Health; Nidhi Garg, MD - Northwell Health; Michael Diefenbach, PhD - Northwell Health; Nicholas Genes, MD, PhD - NYU Grossman School of Medicine; Gregory Simon, MD - NYU Langone Health; Charles Cleland, PhD - New York University Grossman School of Medicine; Amelia Shunk, MMCi - NYU Grossman School of Medicine; Lynn Xu, MPH - NYU Grossman School of Medicine; Angela Mastrianni, PhD - NYU Langone Health; Yuhan Cui, MS - NYU Langone; Safiya Richardson, MD, MPH - New York University;
Poster Number: P34
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Informatics Implementation, Clinical Guidelines
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
About one-third of the 2.4 million computed tomography (CT) scans ordered yearly to evaluate for pulmonary embolism (PE) in U.S. emergency departments (ED) are avoidable. The study objective will be to implement a tool with a nudge to improve provider adoption of guideline-concordant CT ordering for PE in a multi-site, randomized trial. This trial will advance evidence on behavioral strategies using new technological methods, demonstrating how nudges can improve guideline adherence in the ED.
Speaker:
Sundas Khan, MD
Baylor College of Medicine & Michael E. DeBakey VA Medical Center
Authors:
Ynhi Thomas, MD, MPH, MSc - Baylor College of Medicine; Usman Mir, MBBS, MPH - Baylor College of Medicine; Thomas McGinn, MD MPH - Dignity Health / CommonSpirit Health Corporate; Katherine Dauber-Decker, PhD - Northwell Health; Jeffrey Solomon - Northwell Health; Nidhi Garg, MD - Northwell Health; Michael Diefenbach, PhD - Northwell Health; Nicholas Genes, MD, PhD - NYU Grossman School of Medicine; Gregory Simon, MD - NYU Langone Health; Charles Cleland, PhD - New York University Grossman School of Medicine; Amelia Shunk, MMCi - NYU Grossman School of Medicine; Lynn Xu, MPH - NYU Grossman School of Medicine; Angela Mastrianni, PhD - NYU Langone Health; Yuhan Cui, MS - NYU Langone; Safiya Richardson, MD, MPH - New York University;
Sundas
Khan,
MD - Baylor College of Medicine & Michael E. DeBakey VA Medical Center
Testing a Design Framework for Presenting Machine Learning Predictions to Patients
Poster Number: P35
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Information Visualization, Machine Learning, User-centered Design Methods, Patient / Person Generated Health Data (Patient Reported Outcomes)
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
This study designed and tested a patient-facing app displaying ML-based cardiac decompensation predictions using the HeartLogic Index. High comprehension (80-85%) and appropriate risk perception were observed. The app showed viability in empowering patients with actionable insights, though challenges in understanding thresholds and sensor data were noted. The study highlights the potential for patient-facing ML applications to enhance patient engagement and health literacy.
Speaker:
Afra Shamnath, Master of Public Health
Columbia University
Authors:
Afra Shamnath, Master of Public Health - Columbia University; Meghan Reading Turchioe, PhD, MPH, RN - Columbia University School of Nursing;
Poster Number: P35
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Information Visualization, Machine Learning, User-centered Design Methods, Patient / Person Generated Health Data (Patient Reported Outcomes)
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
This study designed and tested a patient-facing app displaying ML-based cardiac decompensation predictions using the HeartLogic Index. High comprehension (80-85%) and appropriate risk perception were observed. The app showed viability in empowering patients with actionable insights, though challenges in understanding thresholds and sensor data were noted. The study highlights the potential for patient-facing ML applications to enhance patient engagement and health literacy.
Speaker:
Afra Shamnath, Master of Public Health
Columbia University
Authors:
Afra Shamnath, Master of Public Health - Columbia University; Meghan Reading Turchioe, PhD, MPH, RN - Columbia University School of Nursing;
Afra
Shamnath,
Master of Public Health - Columbia University
Leveraging Information Quality, Social Influence and Technical Support to Enhance Health Information Exchange Adoption in Emergency Care Settings
Poster Number: P36
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Interoperability and Health Information Exchange, Information Retrieval, Qualitative Methods
Primary Track: Policy
Programmatic Theme: Clinical Research Informatics
This qualitative study explored emergency clinicians' perceptions of HIE (n=21), identifying factors shaping adoption. Findings revealed that information quality, social influence, and organizational support significantly influence HIE utilization. While clinicians valued comprehensive patient data, they faced challenges with missing or fragmented information. Peer advocacy and organizational support emerged as key drivers of HIE adoption, necessitating targeted strategies to enhance HIE integration within emergency care settings.
Speaker:
Umesh Ghimire, MPH, MS
Indiana University
Authors:
Brian Dixon, MPA, PhD - Regenstrief Institute; Saurabh Rahurkar, DrPH, DDS - The Ohio State University College of Medicine;
Poster Number: P36
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Interoperability and Health Information Exchange, Information Retrieval, Qualitative Methods
Primary Track: Policy
Programmatic Theme: Clinical Research Informatics
This qualitative study explored emergency clinicians' perceptions of HIE (n=21), identifying factors shaping adoption. Findings revealed that information quality, social influence, and organizational support significantly influence HIE utilization. While clinicians valued comprehensive patient data, they faced challenges with missing or fragmented information. Peer advocacy and organizational support emerged as key drivers of HIE adoption, necessitating targeted strategies to enhance HIE integration within emergency care settings.
Speaker:
Umesh Ghimire, MPH, MS
Indiana University
Authors:
Brian Dixon, MPA, PhD - Regenstrief Institute; Saurabh Rahurkar, DrPH, DDS - The Ohio State University College of Medicine;
Umesh
Ghimire,
MPH, MS - Indiana University
Ensemble-Based Narrative Analysis for ADHD Diagnosis: An Integration of LLaMA3, RoBERTa and SVM Classifiers
Poster Number: P37
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Large Language Models (LLMs), Machine Learning, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Attention Deficit/Hyperactivity Disorder (ADHD) diagnosis remains challenging due to symptom heterogeneity. This research presents an ensemble narrative analysis approach integrating LLaMA3, RoBERTa and Support Vector Machines (SVM) to automatically classify transcripts from 441 participants. Using majority voting for classification, the ensemble demonstrated improved diagnostic accuracy, achieving a recall of 91.1% and an F1 score of 71.3%, surpassing individual model performances. The findings reveal the effectiveness of ensemble methodologies, involving large language models and traditional supervised classifiers, in advancing ADHD diagnosis through narrative-based assessments.
Speaker:
Yuxin Zhu, B.S.
Emory University
Authors:
Yuxin Zhu, B.S. - Emory University; Yuting Guo, MS - Emory University; Noah Marchuck, B.S. - Emory University; Abeed Sarker, PhD - Emory University School of Medicine; Yun Wang, PhD - Emory University;
Poster Number: P37
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Large Language Models (LLMs), Machine Learning, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Attention Deficit/Hyperactivity Disorder (ADHD) diagnosis remains challenging due to symptom heterogeneity. This research presents an ensemble narrative analysis approach integrating LLaMA3, RoBERTa and Support Vector Machines (SVM) to automatically classify transcripts from 441 participants. Using majority voting for classification, the ensemble demonstrated improved diagnostic accuracy, achieving a recall of 91.1% and an F1 score of 71.3%, surpassing individual model performances. The findings reveal the effectiveness of ensemble methodologies, involving large language models and traditional supervised classifiers, in advancing ADHD diagnosis through narrative-based assessments.
Speaker:
Yuxin Zhu, B.S.
Emory University
Authors:
Yuxin Zhu, B.S. - Emory University; Yuting Guo, MS - Emory University; Noah Marchuck, B.S. - Emory University; Abeed Sarker, PhD - Emory University School of Medicine; Yun Wang, PhD - Emory University;
Yuxin
Zhu,
B.S. - Emory University
Using Large Language Models for Predicting ALSFRS-R Sub-item Scores From Clinician-Annotated ALS Patient Data
Poster Number: P38
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Large Language Models (LLMs), Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Large Language Models (LLM) present opportunities for automatically abstract ALSFRS-R sub-item scores from clinical notes. This research evaluates the accuracy of ALSFRS-R score predictions made by ChatBots against professional clinician predictions. The accuracy rates around 90% were recorded by ChatGPT-4o/mini and DeepSeek v3 yet the LLaMA plain and RAG-assisted models demonstrated inferior performance. The findings show that precision in prediction demands both formal clinical involvement and specialized domain adjustment (LLM fine-tuning) to reach maximum accuracy.
Speaker:
Brian Crum, MD
Mayo Clinic
Authors:
Omer Aydin, PhD in Computer Engineering - Manisa Celal Bayar University; Fatih Safa Erenay, PhD - University of Waterloo; Brian Crum, MD - Mayo Clinic; Kal Pasupathy, PhD - The University of Illinois Chicago;
Poster Number: P38
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Large Language Models (LLMs), Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Large Language Models (LLM) present opportunities for automatically abstract ALSFRS-R sub-item scores from clinical notes. This research evaluates the accuracy of ALSFRS-R score predictions made by ChatBots against professional clinician predictions. The accuracy rates around 90% were recorded by ChatGPT-4o/mini and DeepSeek v3 yet the LLaMA plain and RAG-assisted models demonstrated inferior performance. The findings show that precision in prediction demands both formal clinical involvement and specialized domain adjustment (LLM fine-tuning) to reach maximum accuracy.
Speaker:
Brian Crum, MD
Mayo Clinic
Authors:
Omer Aydin, PhD in Computer Engineering - Manisa Celal Bayar University; Fatih Safa Erenay, PhD - University of Waterloo; Brian Crum, MD - Mayo Clinic; Kal Pasupathy, PhD - The University of Illinois Chicago;
Brian
Crum,
MD - Mayo Clinic
Evaluation of an Ensemble Machine Learning Model for Predicting Continuous Renal Replacement Therapy Initiation in a Pediatric and Cardiac ICU
Poster Number: P39
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Machine Learning, Critical Care, Pediatrics
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Using data from the electronic medical record we evaluated a CRRT prediction model in PICU (n=3,683) and CICU (n=1,106). The model achieved high recall (PICU: 80.95%, CICU: 68.75%) but low precision (PICU: 13.60%, CICU: 4.37%), with AUC-ROC of 94.66% (PICU) and 75.88% (CICU). Key challenges included temporal misalignment (62% false negative cases post-CRRT) and early data gaps. Findings emphasize real-time data integration and unit-specific customization for actionable critical care decision-making.
Speaker:
Qingyang Li, M.S.
Seattle Childrens Hospital
Authors:
Qingyang Li, M.S. - Seattle Childrens Hospital; Vijayaraghavan Makkakode, MCA - Seattle Children's Hospital; Satish Dandayudhapani, M.S. - Seattle Children's Hospital; Kin Vong, B.S. - Seattle Children's Hospital; Hillary Bourdrez, MBA - Seattle Children's Hospital; Jordan Symons, MD - Seattle Children's Hospital; Lincoln Smith, MD - Seattle Children's Hospital; Mark Wainwright, Phd MD - Seattle Children's Hospital;
Poster Number: P39
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Machine Learning, Critical Care, Pediatrics
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Using data from the electronic medical record we evaluated a CRRT prediction model in PICU (n=3,683) and CICU (n=1,106). The model achieved high recall (PICU: 80.95%, CICU: 68.75%) but low precision (PICU: 13.60%, CICU: 4.37%), with AUC-ROC of 94.66% (PICU) and 75.88% (CICU). Key challenges included temporal misalignment (62% false negative cases post-CRRT) and early data gaps. Findings emphasize real-time data integration and unit-specific customization for actionable critical care decision-making.
Speaker:
Qingyang Li, M.S.
Seattle Childrens Hospital
Authors:
Qingyang Li, M.S. - Seattle Childrens Hospital; Vijayaraghavan Makkakode, MCA - Seattle Children's Hospital; Satish Dandayudhapani, M.S. - Seattle Children's Hospital; Kin Vong, B.S. - Seattle Children's Hospital; Hillary Bourdrez, MBA - Seattle Children's Hospital; Jordan Symons, MD - Seattle Children's Hospital; Lincoln Smith, MD - Seattle Children's Hospital; Mark Wainwright, Phd MD - Seattle Children's Hospital;
Qingyang
Li,
M.S. - Seattle Childrens Hospital
Real-World Clinical Pathway Implementation of Pediatric Guidelines Through Scalable Digital Integration
Poster Number: P40
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Guidelines, Clinical Decision Support, Governance, Informatics Implementation, Pediatrics, Patient Safety, Critical Care, Healthcare Quality
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Clinical pathways help frontline teams deliver high-quality, evidence-based care by translating guidelines into local workflows and algorithms. Their development and integration require significant effort and resources which can be a barrier for many health systems. We describe our implementation efforts using a phased approach, starting with simple, low technology resources and progressing to advanced digital tools utilizing the AgileMD platform.
Speaker:
Courtney Titus, MPAS PA-C
Johns Hopkins All Children's Hospital
Author:
Zhen Lin, PhD RN - Johns Hopkins All Children's Hospital;
Poster Number: P40
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Guidelines, Clinical Decision Support, Governance, Informatics Implementation, Pediatrics, Patient Safety, Critical Care, Healthcare Quality
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Clinical pathways help frontline teams deliver high-quality, evidence-based care by translating guidelines into local workflows and algorithms. Their development and integration require significant effort and resources which can be a barrier for many health systems. We describe our implementation efforts using a phased approach, starting with simple, low technology resources and progressing to advanced digital tools utilizing the AgileMD platform.
Speaker:
Courtney Titus, MPAS PA-C
Johns Hopkins All Children's Hospital
Author:
Zhen Lin, PhD RN - Johns Hopkins All Children's Hospital;
Courtney
Titus,
MPAS PA-C - Johns Hopkins All Children's Hospital
Preventing Surgical Site Infections
Poster Number: P41
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Guidelines, Patient Safety, Healthcare Economics/Cost of Care
Primary Track: Applications
Preventing Surgical Site Infections Through
Best-practice Monitoring and Outcomes Evaluation
Grant C Walter BSBS, Michelle Dempsey CPBI,
Rachel Van Loan BS, CLSSMBB, Anthony Petrick MD, Eric S. Reich MSHI,
David K. Vawdrey PhD, Biplab S. Bhattacharya, PhD
Geisinger Health System, Danville, PA
Introduction
Surgical Site Infections (SSIs) are a frequent, preventable complication post-surgery, costing the U.S. healthcare system $3.3 billion annually.1 Most SSIs can be prevented if appropriate strategies are implemented.2 At Geisinger, a multidisciplinary team has implemented evidence-based measures to track and reduce SSIs using an interactive dashboard. These measures include preoperative, intraoperative, and postoperative strategies such as aseptic techniques, antibiotic prophylaxis, and wound care. This abstract presents Geisinger’s best practices in SSI prevention and the importance of following a multidisciplinary approach to achieve optimal patient and financial outcomes.
Methods
From 2022 to 2024, our organization experienced a concerning increase in SSIs, particularly involving gastrointestinal procedures. In response, a multidisciplinary task force investigated causes and implemented reduction measures. In collaboration with infection control experts, evidence-based best-practice elements (BPEs) were developed for preoperative, intraoperative, and postoperative care phases. These included chlorhexidine gluconate (CHG) preparation, Staphylococcus aureus screening, the use of wound protectors, and glucose monitoring. We employed a user-centered design approach to scope, identify metrics, assemble key stakeholder teams, and develop and deploy an SSI prevention dashboard.
Results
The SSI Prevention Dashboard shown here was deployed in early 2025. It enables clinical teams to monitor adherence to these BPEs, enabling continuous tracking and targeted interventions.
Speaker:
Grant Walter, BS
Geisinger Health System
Authors:
Michelle Dempsey, BS CPBI - Geisinger; Biplab S Bhattacharya, PhD - Geisinger Health System; David Vawdrey, PhD - Geisinger; Eric Reich, MSHI - Geisinger;
Poster Number: P41
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Clinical Guidelines, Patient Safety, Healthcare Economics/Cost of Care
Primary Track: Applications
Preventing Surgical Site Infections Through
Best-practice Monitoring and Outcomes Evaluation
Grant C Walter BSBS, Michelle Dempsey CPBI,
Rachel Van Loan BS, CLSSMBB, Anthony Petrick MD, Eric S. Reich MSHI,
David K. Vawdrey PhD, Biplab S. Bhattacharya, PhD
Geisinger Health System, Danville, PA
Introduction
Surgical Site Infections (SSIs) are a frequent, preventable complication post-surgery, costing the U.S. healthcare system $3.3 billion annually.1 Most SSIs can be prevented if appropriate strategies are implemented.2 At Geisinger, a multidisciplinary team has implemented evidence-based measures to track and reduce SSIs using an interactive dashboard. These measures include preoperative, intraoperative, and postoperative strategies such as aseptic techniques, antibiotic prophylaxis, and wound care. This abstract presents Geisinger’s best practices in SSI prevention and the importance of following a multidisciplinary approach to achieve optimal patient and financial outcomes.
Methods
From 2022 to 2024, our organization experienced a concerning increase in SSIs, particularly involving gastrointestinal procedures. In response, a multidisciplinary task force investigated causes and implemented reduction measures. In collaboration with infection control experts, evidence-based best-practice elements (BPEs) were developed for preoperative, intraoperative, and postoperative care phases. These included chlorhexidine gluconate (CHG) preparation, Staphylococcus aureus screening, the use of wound protectors, and glucose monitoring. We employed a user-centered design approach to scope, identify metrics, assemble key stakeholder teams, and develop and deploy an SSI prevention dashboard.
Results
The SSI Prevention Dashboard shown here was deployed in early 2025. It enables clinical teams to monitor adherence to these BPEs, enabling continuous tracking and targeted interventions.
Speaker:
Grant Walter, BS
Geisinger Health System
Authors:
Michelle Dempsey, BS CPBI - Geisinger; Biplab S Bhattacharya, PhD - Geisinger Health System; David Vawdrey, PhD - Geisinger; Eric Reich, MSHI - Geisinger;
Grant
Walter,
BS - Geisinger Health System
Enhancing Missense Variant Classification in Predicted Intrinsically Disordered Regions
Poster Number: P42
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Computational Biology, Bioinformatics, Machine Learning, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics
Missense variant classification in intrinsically disordered regions (IDRs) remains a major challenge in genomic interpretation. Over 25% of known pathogenic variants localize to IDRs where traditional in silico predictors, trained primarily on structured protein domains, show reduced accuracy. These models often rely on evolutionary conservation and structural assumptions that are less applicable to the dynamic and heterogeneous nature of IDRs. To address this gap, we developed a machine learning framework that integrates features predictive of globular IDR conformation, phase separation propensity, and protein embeddings derived from a protein language model for both wild-type and mutant IDR sequences. Using AlphaFold-RSA predictions to define IDRs and ClinVar annotations as ground truth, we trained and optimized a gradient boosting model on 2,104 curated missense variants across 290 genes. Our model achieved an AUC of 0.85 and an accuracy of 0.90 on a hold-out test set, outperforming existing unsupervised in silico missense predictors in IDRs. Among these, ESM1b emerged as the strongest standalone in silico predictor not trained on ClinVar, with an AUC of 0.845 (95% CI: 0.843–0.848), whereas EVE showed mediocre performance with an AUC=0.713 (95% CI: 0.710-0.716). Integrating EVE or ESM1b scores into our model further improved performance, raising the AUC to 0.88 and 0.904, and accuracy to 0.909 and 0.921, respectively. These enhancements underscore the additive value of disorder-specific features in enhancing existing predictive models. Our approach bridges a critical gap in variant interpretation, demonstrating robust classification of missense variants in IDRs and complementing existing tools trained on structured domains.
Speaker:
Rohan Gnanaolivu, Masters
Mayo Clinic
Authors:
Steven Hart, PhD - Mayo Clinic; Rohan Gnanaolivu, Masters - Mayo Clinic;
Poster Number: P42
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Computational Biology, Bioinformatics, Machine Learning, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics
Missense variant classification in intrinsically disordered regions (IDRs) remains a major challenge in genomic interpretation. Over 25% of known pathogenic variants localize to IDRs where traditional in silico predictors, trained primarily on structured protein domains, show reduced accuracy. These models often rely on evolutionary conservation and structural assumptions that are less applicable to the dynamic and heterogeneous nature of IDRs. To address this gap, we developed a machine learning framework that integrates features predictive of globular IDR conformation, phase separation propensity, and protein embeddings derived from a protein language model for both wild-type and mutant IDR sequences. Using AlphaFold-RSA predictions to define IDRs and ClinVar annotations as ground truth, we trained and optimized a gradient boosting model on 2,104 curated missense variants across 290 genes. Our model achieved an AUC of 0.85 and an accuracy of 0.90 on a hold-out test set, outperforming existing unsupervised in silico missense predictors in IDRs. Among these, ESM1b emerged as the strongest standalone in silico predictor not trained on ClinVar, with an AUC of 0.845 (95% CI: 0.843–0.848), whereas EVE showed mediocre performance with an AUC=0.713 (95% CI: 0.710-0.716). Integrating EVE or ESM1b scores into our model further improved performance, raising the AUC to 0.88 and 0.904, and accuracy to 0.909 and 0.921, respectively. These enhancements underscore the additive value of disorder-specific features in enhancing existing predictive models. Our approach bridges a critical gap in variant interpretation, demonstrating robust classification of missense variants in IDRs and complementing existing tools trained on structured domains.
Speaker:
Rohan Gnanaolivu, Masters
Mayo Clinic
Authors:
Steven Hart, PhD - Mayo Clinic; Rohan Gnanaolivu, Masters - Mayo Clinic;
Rohan
Gnanaolivu,
Masters - Mayo Clinic
An AI Semantic Mapping Tool in a Knowledge Management System
Poster Number: P43
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Controlled Terminologies, Ontologies, and Vocabularies, Standards, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Informatics
We implemented an AI-powered semantic mapping tool inside a Knowledge Management System (KMS) to improve terminology alignment in healthcare data. Leveraging on SapBERT, the tool enhances mapping of laboratory and clinical observations to Logical Observation Identifiers Names and Codes (LOINC). This innovation boosts interoperability while reducing manual curation.
Speaker:
Ali Daowd, MD, PhD
Semedy, Inc.
Authors:
Marcelo Fiszman, MD, Ph.D. FACMI, DipIBLM - Semedy Inc; Kai Großjohann, MSc - Semedy, Inc.; Roberto Rocha, MD, PhD, FACMI - Semedy, Inc.;
Poster Number: P43
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Controlled Terminologies, Ontologies, and Vocabularies, Standards, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Informatics
We implemented an AI-powered semantic mapping tool inside a Knowledge Management System (KMS) to improve terminology alignment in healthcare data. Leveraging on SapBERT, the tool enhances mapping of laboratory and clinical observations to Logical Observation Identifiers Names and Codes (LOINC). This innovation boosts interoperability while reducing manual curation.
Speaker:
Ali Daowd, MD, PhD
Semedy, Inc.
Authors:
Marcelo Fiszman, MD, Ph.D. FACMI, DipIBLM - Semedy Inc; Kai Großjohann, MSc - Semedy, Inc.; Roberto Rocha, MD, PhD, FACMI - Semedy, Inc.;
Ali
Daowd,
MD, PhD - Semedy, Inc.
Machine learning composite variable for pre-operative volume status in orthotopic liver transplant predicts postoperative length of stay
Poster Number: P44
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Critical Care, Machine Learning, Quantitative Methods
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Among patients undergoing orthotopic liver transplantation (OLT), pre-operative volume overload is associated with increased perioperative morbidity and mortality. We used principal component analysis and exploratory factor analysis to generate a composite measure of volume status that is predictive of in-hospital length of stay. Poor performance of individual predictors illuminates key limitations of current clinical practice and need for new approaches.
Speaker:
Caitlin Coombes, MD
Stanford Health Care
Authors:
Alexandra Ruan, MD - Stanford University School of Medicine; Seshadri Mudumbai, MD - Stanford University; Marianne Chen, MD - Stanford University School of Medicine; Amy Kloosterboer, MD - Stanford University School of Medicine;
Poster Number: P44
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Critical Care, Machine Learning, Quantitative Methods
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Among patients undergoing orthotopic liver transplantation (OLT), pre-operative volume overload is associated with increased perioperative morbidity and mortality. We used principal component analysis and exploratory factor analysis to generate a composite measure of volume status that is predictive of in-hospital length of stay. Poor performance of individual predictors illuminates key limitations of current clinical practice and need for new approaches.
Speaker:
Caitlin Coombes, MD
Stanford Health Care
Authors:
Alexandra Ruan, MD - Stanford University School of Medicine; Seshadri Mudumbai, MD - Stanford University; Marianne Chen, MD - Stanford University School of Medicine; Amy Kloosterboer, MD - Stanford University School of Medicine;
Caitlin
Coombes,
MD - Stanford Health Care
A Bayesian-Driven Two-Compartment Model Algorithm for Vancomycin AUC24 Calculation
Poster Number: P45
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Critical Care, Personal Health Informatics, Precision Medicine, Drug Discoveries, Repurposing, and Side-effect
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
We present a novel algorithm for accurate estimation of vancomycin AUC24 using a Bayesian-driven, personalized two-compartment pharmacokinetic model. Validated against expert-annotated MIMIC data, the algorithm outperformed the traditional trapezoidal approach when benchmarked against a leading commercial model, achieving an RMSE of 29.81 and R² of 0.94. A freely available web application can retrospectively calculate AUC24 from historical data, which lays the foundations for the development of future AI models based on AUC-guided dosing.
Speaker:
Dhruvin Patel, Computer Science
Loyola University Chicago
Authors:
Dhruvin Patel, Computer Science - Loyola University Chicago; Nazanin Azarvash, BS - Loyola University Chicago; Samie Tootooni, PhD - Loyola University Chicago;
Poster Number: P45
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Critical Care, Personal Health Informatics, Precision Medicine, Drug Discoveries, Repurposing, and Side-effect
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
We present a novel algorithm for accurate estimation of vancomycin AUC24 using a Bayesian-driven, personalized two-compartment pharmacokinetic model. Validated against expert-annotated MIMIC data, the algorithm outperformed the traditional trapezoidal approach when benchmarked against a leading commercial model, achieving an RMSE of 29.81 and R² of 0.94. A freely available web application can retrospectively calculate AUC24 from historical data, which lays the foundations for the development of future AI models based on AUC-guided dosing.
Speaker:
Dhruvin Patel, Computer Science
Loyola University Chicago
Authors:
Dhruvin Patel, Computer Science - Loyola University Chicago; Nazanin Azarvash, BS - Loyola University Chicago; Samie Tootooni, PhD - Loyola University Chicago;
Dhruvin
Patel,
Computer Science - Loyola University Chicago
Data-Driven Depression Subtypes and Heart Failure Risk Analysis
Poster Number: P46
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Mining, Quantitative Methods, Bioinformatics, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This study identified optimal depression subgroups via K-means clustering with silhouette scores on five depression indicators and assessed heart failure (HF) risk using UK Biobank EHRs data. Kaplan-Meier and Cox models showed individuals with severe, persistent depression (Cluster 0) had higher HF risk before age 80 than those with milder symptoms (Cluster 1). This data-driven approach in a large cohort supports early HF risk stratification in depression subtypes, confirming and extending findings from clinical practice.
Speaker:
yining qian, master of science
yale school of public health
Authors:
yining qian, master of science - yale school of public health; Xiayuan Huang, PhD. - Yale University; ruoxuan li, master of science - yale school of public health;
Poster Number: P46
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Mining, Quantitative Methods, Bioinformatics, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This study identified optimal depression subgroups via K-means clustering with silhouette scores on five depression indicators and assessed heart failure (HF) risk using UK Biobank EHRs data. Kaplan-Meier and Cox models showed individuals with severe, persistent depression (Cluster 0) had higher HF risk before age 80 than those with milder symptoms (Cluster 1). This data-driven approach in a large cohort supports early HF risk stratification in depression subtypes, confirming and extending findings from clinical practice.
Speaker:
yining qian, master of science
yale school of public health
Authors:
yining qian, master of science - yale school of public health; Xiayuan Huang, PhD. - Yale University; ruoxuan li, master of science - yale school of public health;
yining
qian,
master of science - yale school of public health
Integrating eMerge Clinical Data into i2b2: A portal for AnVIL integration
Poster Number: P47
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Sharing, Data transformation/ETL, Informatics Implementation, Patient / Person Generated Health Data (Patient Reported Outcomes)
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
We introduce a research portal designed for eMERGE 4 that integrates REDCap-stored clinical data into the i2b2 Common Data Model. The portal leverages’ i2b2’s advanced cohort exploration and tailored data exports. By integrating i2b2’s cohort-building capabilities with the Analysis, Visualization, and Informatics Lab-space (AnVIL) platform, this portal facilitates efficient data analysis within a scalable cloud-based environment. This initiative enhances clinical-genomic research by enabling seamless access to eMERGE data for over 15,000 researchers.
Speaker:
Jeffrey Klann, PhD
Massachusetts General Hospital
Authors:
Jeffrey Klann, PhD - Massachusetts General Hospital; Victor Castro, MS - Mass General Brigham; Kavishwar Wagholikar, MD, PhD - Harvard Medical School /MGH; Shawn Murphy, MD, Ph.D. - Massachusetts General Hospital;
Poster Number: P47
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Sharing, Data transformation/ETL, Informatics Implementation, Patient / Person Generated Health Data (Patient Reported Outcomes)
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
We introduce a research portal designed for eMERGE 4 that integrates REDCap-stored clinical data into the i2b2 Common Data Model. The portal leverages’ i2b2’s advanced cohort exploration and tailored data exports. By integrating i2b2’s cohort-building capabilities with the Analysis, Visualization, and Informatics Lab-space (AnVIL) platform, this portal facilitates efficient data analysis within a scalable cloud-based environment. This initiative enhances clinical-genomic research by enabling seamless access to eMERGE data for over 15,000 researchers.
Speaker:
Jeffrey Klann, PhD
Massachusetts General Hospital
Authors:
Jeffrey Klann, PhD - Massachusetts General Hospital; Victor Castro, MS - Mass General Brigham; Kavishwar Wagholikar, MD, PhD - Harvard Medical School /MGH; Shawn Murphy, MD, Ph.D. - Massachusetts General Hospital;
Jeffrey
Klann,
PhD - Massachusetts General Hospital
Disaster-Ready Hemodialysis Information Sharing: What to Share and How to Communicate
Poster Number: P48
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Sharing, Interoperability and Health Information Exchange, Patient Engagement and Preferences, Public Health
Primary Track: Foundations
Programmatic Theme: Public Health Informatics
Ensuring dialysis continuity during disasters requires effective information sharing. This study surveyed 71 dialysis facilities across two Japanese prefectures to identify essential data: dry weight, anticoagulants, dialyzers, infections, and allergies. Both healthcare professionals and patients preferred IT-based solutions, particularly regional medical networks. Findings emphasize the need for a standardized digital system to enhance disaster preparedness, improve accessibility, and ensure seamless dialysis care during emergencies.
Speaker:
Masaharu Nakayama, MD, PhD, FAMIA
Tohoku University
Authors:
Keisuke Ido, MPH - Tohoku University Hospital; Tadashi Ishii, MD, PhD - Tohoku University Hospital; Mariko Miyazaki, MD, PhD - Tohoku University Graduate School of Medicine;
Poster Number: P48
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Sharing, Interoperability and Health Information Exchange, Patient Engagement and Preferences, Public Health
Primary Track: Foundations
Programmatic Theme: Public Health Informatics
Ensuring dialysis continuity during disasters requires effective information sharing. This study surveyed 71 dialysis facilities across two Japanese prefectures to identify essential data: dry weight, anticoagulants, dialyzers, infections, and allergies. Both healthcare professionals and patients preferred IT-based solutions, particularly regional medical networks. Findings emphasize the need for a standardized digital system to enhance disaster preparedness, improve accessibility, and ensure seamless dialysis care during emergencies.
Speaker:
Masaharu Nakayama, MD, PhD, FAMIA
Tohoku University
Authors:
Keisuke Ido, MPH - Tohoku University Hospital; Tadashi Ishii, MD, PhD - Tohoku University Hospital; Mariko Miyazaki, MD, PhD - Tohoku University Graduate School of Medicine;
Masaharu
Nakayama,
MD, PhD, FAMIA - Tohoku University
Distributed Ledger for Tracking and Crediting Biomedical Research Data
Poster Number: P49
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Sharing, Interoperability and Health Information Exchange, Privacy and Security
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Cross-institutional biomedical data sharing is critical to encourage collaboration and facilitate research findings. This study aims to design a distributed ledger to track and credit biomedical data. We developed a smart contract consisting of 23 functions and built a graphical user interface to allow users to submit datasets, cite other datasets, and query existing ones. Our results demonstrate the feasibility of tracking and crediting dataset records in an immutable, transparent, and decentralized way.
Speaker:
Lana Kareem, Bachelor's of Computer Science & Engineering
Yale School of Medicine
Authors:
Lana Kareem, Bachelor's of Computer Science & Engineering - Yale School of Medicine; Chi Wing Ng, Bachelor's of Science - Yale School of Medicine; Pritham Ram, Bachelor of Science - Yale School of Medicine; Hua Xu, Ph.D - Yale University; Lucila Ohno-Machado, MD, PhD - Yale School of Medicine; Tsung-Ting Kuo, PhD - Yale University;
Poster Number: P49
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Sharing, Interoperability and Health Information Exchange, Privacy and Security
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Cross-institutional biomedical data sharing is critical to encourage collaboration and facilitate research findings. This study aims to design a distributed ledger to track and credit biomedical data. We developed a smart contract consisting of 23 functions and built a graphical user interface to allow users to submit datasets, cite other datasets, and query existing ones. Our results demonstrate the feasibility of tracking and crediting dataset records in an immutable, transparent, and decentralized way.
Speaker:
Lana Kareem, Bachelor's of Computer Science & Engineering
Yale School of Medicine
Authors:
Lana Kareem, Bachelor's of Computer Science & Engineering - Yale School of Medicine; Chi Wing Ng, Bachelor's of Science - Yale School of Medicine; Pritham Ram, Bachelor of Science - Yale School of Medicine; Hua Xu, Ph.D - Yale University; Lucila Ohno-Machado, MD, PhD - Yale School of Medicine; Tsung-Ting Kuo, PhD - Yale University;
Lana
Kareem,
Bachelor's of Computer Science & Engineering - Yale School of Medicine
Research in the Age of Artificial Intelligence: Integrating Participant Perspectives with Current Research Innovations to Foster Patient-Centric Practices
Poster Number: P50
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Sharing, Patient Engagement and Preferences, Artificial Intelligence
Primary Track: Policy
Programmatic Theme: Clinical Research Informatics
This study evaluated public perspectives on AI-driven research processes. Participants (n = 30) felt more trust in researchers not using AI to maintain the security of their data compared to researchers using AI. Participants were significantly less willing to share omics, imaging, phenotype, and social/behavioral data with researchers using AI. Ethical concerns and desire for human professionals impacted trust.
Speaker:
Sarah Eslami, Bachelors of Science
Columbia University
Authors:
Sarah Eslami, Bachelors of Science - Columbia University; Stephanie Nino de Rivera, BA - Columbia University; Ruth Masterson Creber, PhD, MSc, RN - Columbia University;
Poster Number: P50
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Sharing, Patient Engagement and Preferences, Artificial Intelligence
Primary Track: Policy
Programmatic Theme: Clinical Research Informatics
This study evaluated public perspectives on AI-driven research processes. Participants (n = 30) felt more trust in researchers not using AI to maintain the security of their data compared to researchers using AI. Participants were significantly less willing to share omics, imaging, phenotype, and social/behavioral data with researchers using AI. Ethical concerns and desire for human professionals impacted trust.
Speaker:
Sarah Eslami, Bachelors of Science
Columbia University
Authors:
Sarah Eslami, Bachelors of Science - Columbia University; Stephanie Nino de Rivera, BA - Columbia University; Ruth Masterson Creber, PhD, MSc, RN - Columbia University;
Sarah
Eslami,
Bachelors of Science - Columbia University
Visualising Health Data in FHIR Format for Decision Making: Developing A Dashboard Application for Disaster Management in Indonesia
Poster Number: P51
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Standards, Delivering Health Information and Knowledge to the Public, Information Visualization, Interoperability and Health Information Exchange
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This study shows the development of a dashboard application to visualize the data based on the FHIR resources and the World Health Organization’s Emergency Medical Teams (WHO EMT MDS) daily reporting form. The application is built using Javascript and React library and connected to the HAPI FHIR server. By visualizing the health data collected during the acute phase, we aim to help government agencies in the decision-making process in disaster management.
Speaker:
Hiro Putra Faisal, MD
Tohoku University
Authors:
Hiro Putra Faisal, MD - Tohoku University; Masaharu Nakayama, MD, PhD, FAMIA - Tohoku University;
Poster Number: P51
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Standards, Delivering Health Information and Knowledge to the Public, Information Visualization, Interoperability and Health Information Exchange
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This study shows the development of a dashboard application to visualize the data based on the FHIR resources and the World Health Organization’s Emergency Medical Teams (WHO EMT MDS) daily reporting form. The application is built using Javascript and React library and connected to the HAPI FHIR server. By visualizing the health data collected during the acute phase, we aim to help government agencies in the decision-making process in disaster management.
Speaker:
Hiro Putra Faisal, MD
Tohoku University
Authors:
Hiro Putra Faisal, MD - Tohoku University; Masaharu Nakayama, MD, PhD, FAMIA - Tohoku University;
Hiro Putra
Faisal,
MD - Tohoku University
From Text to Codes: Optimizing ICD-10 Coding Workflow with GPT-driven Retrieval-Augmented Generation
Poster Number: P52
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Standards, Documentation Burden, Data transformation/ETL, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Medical coding is essential for documentation, billing, and research but remains manual and error-prone. This study employs LLMs with RAG to automate ICD-10 coding by extracting diagnoses, retrieving relevant codes, and refining selections. Evaluated on MIMIC-IV discharge summaries, it achieved a 70% exact match rate on clinical narratives, demonstrating its potential to improve coding efficiency.
Speaker:
Kanishka Angirish, Masters in Health Informatics
Indiana University, Indianapolis
Authors:
Zhen Hou, MS - Indiana University; Yan Zhuang, Ph.D. - Indiana University;
Poster Number: P52
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Standards, Documentation Burden, Data transformation/ETL, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Medical coding is essential for documentation, billing, and research but remains manual and error-prone. This study employs LLMs with RAG to automate ICD-10 coding by extracting diagnoses, retrieving relevant codes, and refining selections. Evaluated on MIMIC-IV discharge summaries, it achieved a 70% exact match rate on clinical narratives, demonstrating its potential to improve coding efficiency.
Speaker:
Kanishka Angirish, Masters in Health Informatics
Indiana University, Indianapolis
Authors:
Zhen Hou, MS - Indiana University; Yan Zhuang, Ph.D. - Indiana University;
Kanishka
Angirish,
Masters in Health Informatics - Indiana University, Indianapolis
Data Without Boundaries: Resilient EMR Systems for Low-Resource Clinics
Poster Number: P53
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Transformation/ETL, Informatics Implementation, Global Health, Mobile Health, Data Modernization, Health Equity, User-centered Design Methods, Administrative Systems
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Many healthcare clinics lack reliable electricity and internet, limiting their ability to collect and manage structured health data. We present an offline-first EMR system, piloted in a rural Haitian clinic, that uses a low-power Raspberry Pi and Bluetooth Low Energy to function without constant connectivity. This scalable approach empowers low-resource clinics to manage their own data and lays the groundwork for including historically overlooked communities in global health data systems for the first time.
Speaker:
Hossein Khoshhal, M.S. Public Policy & Management - Data Analytics
Carnegie Mellon University
Authors:
Masahiko Shinjo, M.S. Public Policy & Management - Data Analytics - Carnegie Mellon University; Riddhima Singh, M.S. Public Policy & Management - Data Analytics - Carnegie Mellon University; Anahita Subramanya, M.S. Healthcare Analytics and I.T. - Carnegie Mellon University; Michael McCarthy, M.S. Information Science - Carnegie Mellon University;
Poster Number: P53
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Transformation/ETL, Informatics Implementation, Global Health, Mobile Health, Data Modernization, Health Equity, User-centered Design Methods, Administrative Systems
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Many healthcare clinics lack reliable electricity and internet, limiting their ability to collect and manage structured health data. We present an offline-first EMR system, piloted in a rural Haitian clinic, that uses a low-power Raspberry Pi and Bluetooth Low Energy to function without constant connectivity. This scalable approach empowers low-resource clinics to manage their own data and lays the groundwork for including historically overlooked communities in global health data systems for the first time.
Speaker:
Hossein Khoshhal, M.S. Public Policy & Management - Data Analytics
Carnegie Mellon University
Authors:
Masahiko Shinjo, M.S. Public Policy & Management - Data Analytics - Carnegie Mellon University; Riddhima Singh, M.S. Public Policy & Management - Data Analytics - Carnegie Mellon University; Anahita Subramanya, M.S. Healthcare Analytics and I.T. - Carnegie Mellon University; Michael McCarthy, M.S. Information Science - Carnegie Mellon University;
Hossein
Khoshhal,
M.S. Public Policy & Management - Data Analytics - Carnegie Mellon University
A Simple Pipeline to Facilitate Toxicology Report Data Transformation
Poster Number: P54
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Transformation/ETL, Information Extraction, Data Mining
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
The complex formatting of toxicology reports can hinder automated analysis of component values. We developed a process to convert procedure exports into a more accessible table format. We then used the pipeline in a proof-of-concept study, which included 74,805 toxicology screens containing 484,246 components. Requiring only existing functions, this technique can facilitate preprocessing of toxicology report data for multiple use cases in clinical summary reporting and research.
Speaker:
Donielle Beiler, BS
Geisinger Health System
Authors:
Donielle Beiler, B.S. - Geisinger; Vanessa Troiani, PhD - Geisinger;
Poster Number: P54
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Transformation/ETL, Information Extraction, Data Mining
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
The complex formatting of toxicology reports can hinder automated analysis of component values. We developed a process to convert procedure exports into a more accessible table format. We then used the pipeline in a proof-of-concept study, which included 74,805 toxicology screens containing 484,246 components. Requiring only existing functions, this technique can facilitate preprocessing of toxicology report data for multiple use cases in clinical summary reporting and research.
Speaker:
Donielle Beiler, BS
Geisinger Health System
Authors:
Donielle Beiler, B.S. - Geisinger; Vanessa Troiani, PhD - Geisinger;
Donielle
Beiler,
BS - Geisinger Health System
Enabling Scalable Predictive Monitoring and Alarm Analytics via a Real-Time Platform for Processing Continuous Cardiorespiratory Monitoring Data
Poster Number: P55
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Transformation/ETL, Information Visualization, Administrative Systems, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
A significant challenge in using cardiorespiratory monitoring data for AI and machine learning (ML) applications is the development of platforms capable of ingesting, normalizing, and delivering live patient-centric data to analytics applications in real time. We integrated a real-time, ML-enhanced alarm and vital sign application—into a scalable, vendor-agnostic digital health platform to enable hospital-wide deployment. The system is used to evaluate the clinical workflow impacts of alarms and reduce the number of false alarms.
Speaker:
Delgersuren Bold, MS
Nell Hodgson Woodruff School of Nursing, Emory University
Authors:
Delgersuren Bold, MS - Nell Hodgson Woodruff School of Nursing, Emory University; Justin Long, MD, MS, FAAP - Children's Healthcare of Atlanta; Michael Fundora, MD - Children’s Healthcare of Atlanta, Emory University; Mohamed Elmahdy, PhD - Nihon Kohden Digital Health Solutions; Abel Lin, BS - Nihon Kohden Digital Health Solutions; Timothy Ruchti, PHD - Nihon Kohden Digital Health Solutions; Xiao Hu, PhD - Emory University;
Poster Number: P55
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Data Transformation/ETL, Information Visualization, Administrative Systems, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
A significant challenge in using cardiorespiratory monitoring data for AI and machine learning (ML) applications is the development of platforms capable of ingesting, normalizing, and delivering live patient-centric data to analytics applications in real time. We integrated a real-time, ML-enhanced alarm and vital sign application—into a scalable, vendor-agnostic digital health platform to enable hospital-wide deployment. The system is used to evaluate the clinical workflow impacts of alarms and reduce the number of false alarms.
Speaker:
Delgersuren Bold, MS
Nell Hodgson Woodruff School of Nursing, Emory University
Authors:
Delgersuren Bold, MS - Nell Hodgson Woodruff School of Nursing, Emory University; Justin Long, MD, MS, FAAP - Children's Healthcare of Atlanta; Michael Fundora, MD - Children’s Healthcare of Atlanta, Emory University; Mohamed Elmahdy, PhD - Nihon Kohden Digital Health Solutions; Abel Lin, BS - Nihon Kohden Digital Health Solutions; Timothy Ruchti, PHD - Nihon Kohden Digital Health Solutions; Xiao Hu, PhD - Emory University;
Delgersuren
Bold,
MS - Nell Hodgson Woodruff School of Nursing, Emory University
The Impact of Graphical Complexity in VR on Task Performance: Insights from a Preliminary Study
Poster Number: P56
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Disability, Accessibility, and Human Function, Human-computer Interaction, Quantitative Methods, Real-World Evidence Generation, Usability
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
VR is utilized in motor therapy, but cognitive impairments hinder task performance. Understanding the influence of graphical complexity on task performance is crucial but underexplored. To address this gap, we developed VR involving gross motor arm exercises and two graphical complexity levels. In healthy young adults, goal-oriented action and movement data from VR usage revealed differences by complexity, supporting the importance of tailoring VR graphics based on cognitive capacity.
Speaker:
Jonathan Liu, BS
Indiana University Indianapolis
Authors:
Jonathan Liu, BS - Indiana University Indianapolis; Fanny D'Souza, BS - Indiana University Indianapolis; Samuel Brunes, MS - Indiana University Indianapolis; Jacob Gibson, MS - Indiana University Indianapolis; Eun Jin Paek, PhD - The University of Tennessee Health Science Center; Hee Tae Jung, PhD - Indiana University Indianapolis;
Poster Number: P56
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Disability, Accessibility, and Human Function, Human-computer Interaction, Quantitative Methods, Real-World Evidence Generation, Usability
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
VR is utilized in motor therapy, but cognitive impairments hinder task performance. Understanding the influence of graphical complexity on task performance is crucial but underexplored. To address this gap, we developed VR involving gross motor arm exercises and two graphical complexity levels. In healthy young adults, goal-oriented action and movement data from VR usage revealed differences by complexity, supporting the importance of tailoring VR graphics based on cognitive capacity.
Speaker:
Jonathan Liu, BS
Indiana University Indianapolis
Authors:
Jonathan Liu, BS - Indiana University Indianapolis; Fanny D'Souza, BS - Indiana University Indianapolis; Samuel Brunes, MS - Indiana University Indianapolis; Jacob Gibson, MS - Indiana University Indianapolis; Eun Jin Paek, PhD - The University of Tennessee Health Science Center; Hee Tae Jung, PhD - Indiana University Indianapolis;
Jonathan
Liu,
BS - Indiana University Indianapolis
FairFML: A Unified Approach to Algorithmic Fair Federated Learning with Applications to Reducing Gender Disparities in Cardiac Arrest Outcomes
Poster Number: P57
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Diversity, Equity, Inclusion, and Accessibility (DEIA), Fairness and elimination of bias, Machine Learning, Artificial Intelligence, Privacy and Security
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Mitigating algorithmic disparities in healthcare is essential for ensuring fairness in predictive models. We propose Fair Federated Machine Learning (FairFML), a model-agnostic framework that enhances fairness in cross-institutional collaborations while preserving patient privacy. Validated on a real-world case study for gender disparities in cardiac arrest outcomes, FairFML improves fairness by up to 65% without compromising predictive performance. Its adaptability makes it a robust solution for developing equitable federated learning models across diverse clinical applications.
Speaker:
Siqi Li, Bachelor of Science
Duke-NUS Medical School
Authors:
Siqi Li, Bachelor of Science - Duke-NUS Medical School; Qiming Wu, MSc - Duke NUS Medical School; Xin Li, Master of science - Duke-NUS medical school; Di Miao, Master - Duke-NUS; Chuan Hong, PhD - Duke University; Yilin Ning, PhD; Yuqing Shang, MS - National University of Singapore; Nan Liu, PhD - National University of Singapore;
Poster Number: P57
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Diversity, Equity, Inclusion, and Accessibility (DEIA), Fairness and elimination of bias, Machine Learning, Artificial Intelligence, Privacy and Security
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Mitigating algorithmic disparities in healthcare is essential for ensuring fairness in predictive models. We propose Fair Federated Machine Learning (FairFML), a model-agnostic framework that enhances fairness in cross-institutional collaborations while preserving patient privacy. Validated on a real-world case study for gender disparities in cardiac arrest outcomes, FairFML improves fairness by up to 65% without compromising predictive performance. Its adaptability makes it a robust solution for developing equitable federated learning models across diverse clinical applications.
Speaker:
Siqi Li, Bachelor of Science
Duke-NUS Medical School
Authors:
Siqi Li, Bachelor of Science - Duke-NUS Medical School; Qiming Wu, MSc - Duke NUS Medical School; Xin Li, Master of science - Duke-NUS medical school; Di Miao, Master - Duke-NUS; Chuan Hong, PhD - Duke University; Yilin Ning, PhD; Yuqing Shang, MS - National University of Singapore; Nan Liu, PhD - National University of Singapore;
Siqi
Li,
Bachelor of Science - Duke-NUS Medical School
Tackling Gender and Ethnicity Bias in Privacy-Preserving Clinical Decision- Making Across Hospital Collaborations
Poster Number: P58
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Diversity, Equity, Inclusion, and Accessibility (DEIA), Fairness and elimination of bias, Artificial Intelligence, Machine Learning, Privacy and Security
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Privacy-preserving machine learning is essential in healthcare, where data-sharing restrictions limit model development. Federated learning (FL) enables collaborative model training without exposing patient data but often overlooks fairness, leading to algorithmic bias across demographic groups. Traditional fairness approaches typically address a single binary attribute, limiting their applicability in real-world settings where multiple sensitive factors, such as gender and ethnicity, influence outcomes. This study introduces a fairness-aware FL framework that enforces group fairness constraints across multiple non-binary sensitive attributes while addressing class imbalance through an oversampling module. Using data from the Resuscitation Outcomes Consortium Registry, we applied this approach to predict neurological outcomes in emergency cardiac arrest patients. Our results show that the method enhances fairness across race and sex subgroups, improving fairness metrics by up to 55% compared to a central model while maintaining predictive accuracy with an AUROC drop of no more than 0.02. By mitigating bias in FL-based clinical models, this framework supports equitable decision-making in multi-institutional collaborations and is well-suited for real-world applications in diverse and imbalanced populations.
Speaker:
Siqi Li, Bachelor of Science
Duke-NUS Medical School
Authors:
Siqi Li, Bachelor of Science - Duke-NUS Medical School; Qiming Wu, MSc - Duke NUS Medical School; Doudou Zhou, PhD - National University of Singapore; Jingchi Liao, MSc - Duke-NUS Medical School; Chuan Hong, PhD - Duke University; Marcus Ong, MBBS - Duke-NUS Medical School; Nan Liu, PhD - National University of Singapore;
Poster Number: P58
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Diversity, Equity, Inclusion, and Accessibility (DEIA), Fairness and elimination of bias, Artificial Intelligence, Machine Learning, Privacy and Security
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Privacy-preserving machine learning is essential in healthcare, where data-sharing restrictions limit model development. Federated learning (FL) enables collaborative model training without exposing patient data but often overlooks fairness, leading to algorithmic bias across demographic groups. Traditional fairness approaches typically address a single binary attribute, limiting their applicability in real-world settings where multiple sensitive factors, such as gender and ethnicity, influence outcomes. This study introduces a fairness-aware FL framework that enforces group fairness constraints across multiple non-binary sensitive attributes while addressing class imbalance through an oversampling module. Using data from the Resuscitation Outcomes Consortium Registry, we applied this approach to predict neurological outcomes in emergency cardiac arrest patients. Our results show that the method enhances fairness across race and sex subgroups, improving fairness metrics by up to 55% compared to a central model while maintaining predictive accuracy with an AUROC drop of no more than 0.02. By mitigating bias in FL-based clinical models, this framework supports equitable decision-making in multi-institutional collaborations and is well-suited for real-world applications in diverse and imbalanced populations.
Speaker:
Siqi Li, Bachelor of Science
Duke-NUS Medical School
Authors:
Siqi Li, Bachelor of Science - Duke-NUS Medical School; Qiming Wu, MSc - Duke NUS Medical School; Doudou Zhou, PhD - National University of Singapore; Jingchi Liao, MSc - Duke-NUS Medical School; Chuan Hong, PhD - Duke University; Marcus Ong, MBBS - Duke-NUS Medical School; Nan Liu, PhD - National University of Singapore;
Siqi
Li,
Bachelor of Science - Duke-NUS Medical School
An approach to explore an open-source language models’ representation of interprofessionality.
Poster Number: P59
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Documentation Burden, Healthcare Quality, Nursing Informatics, Artificial Intelligence, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
We evaluated the feasibility of using open-source large language models (LMs) to generate French-language summaries of nursing notes tailored to specific healthcare professionals. In a retrospective mixed-methods study, 500 summaries were generated using various prompting strategies. While 10% were deemed production-ready, many summaries exhibited substance or format issues. Despite these limitations, open-source LMs show promise for enhancing clinical communication and interprofessional collaboration. Future work will assess improved prompting strategies and further explore multilingual models.
Speaker:
Frédéric Baroz, MD, MSc, PhDc
McGill University
Authors:
Frédéric Baroz, MD, MSc, PhDc - McGill University; Katherine Blondon, MD, PhD - Hopitaux Universitaires Geneve;
Poster Number: P59
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Documentation Burden, Healthcare Quality, Nursing Informatics, Artificial Intelligence, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
We evaluated the feasibility of using open-source large language models (LMs) to generate French-language summaries of nursing notes tailored to specific healthcare professionals. In a retrospective mixed-methods study, 500 summaries were generated using various prompting strategies. While 10% were deemed production-ready, many summaries exhibited substance or format issues. Despite these limitations, open-source LMs show promise for enhancing clinical communication and interprofessional collaboration. Future work will assess improved prompting strategies and further explore multilingual models.
Speaker:
Frédéric Baroz, MD, MSc, PhDc
McGill University
Authors:
Frédéric Baroz, MD, MSc, PhDc - McGill University; Katherine Blondon, MD, PhD - Hopitaux Universitaires Geneve;
Frédéric
Baroz,
MD, MSc, PhDc - McGill University
Automatically Generating Patient-specific Clinical Questions from Patient Messages to Relieve Clinician Burden
Poster Number: P60
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Documentation Burden, Natural Language Processing, Artificial Intelligence, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Patient portal messages—often long and laden with unnecessary details—increasingly burden clinicians. However, existing summarization research predominantly addresses general health queries. We address this gap by curating a dataset of realistic patient portal messages with clinician-rewritten versions and employing two large language models to automatically generate clinician questions. Both automatic and manual evaluations demonstrated that most model-generated questions met quality standards, with error analysis revealing the need for clinical safeguards in real-world deployment.
Speaker:
Sarvesh Soni, PhD
National Library of Medicine (NLM)
Author:
Dina Demner-Fushman, MD - National Library of Medicine;
Poster Number: P60
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Documentation Burden, Natural Language Processing, Artificial Intelligence, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Patient portal messages—often long and laden with unnecessary details—increasingly burden clinicians. However, existing summarization research predominantly addresses general health queries. We address this gap by curating a dataset of realistic patient portal messages with clinician-rewritten versions and employing two large language models to automatically generate clinician questions. Both automatic and manual evaluations demonstrated that most model-generated questions met quality standards, with error analysis revealing the need for clinical safeguards in real-world deployment.
Speaker:
Sarvesh Soni, PhD
National Library of Medicine (NLM)
Author:
Dina Demner-Fushman, MD - National Library of Medicine;
Sarvesh
Soni,
PhD - National Library of Medicine (NLM)
Mapping Health Informatics Education and Industry Job Competencies in the United States: Literature Trends and A Case Study
Poster Number: P61
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Education and Training, Curriculum Development, Workforce Development
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
This study took a mixed-method approach to investigate the essential competencies required by health informatics industry jobs in the United States (US). The findings were used to inform and update the curriculum of a US institutional health informatics program.
Speaker:
Fei Yu, PhD
UNC at Chapel Hill
Authors:
Fei Yu, PhD - UNC at Chapel Hill; Jenny Kaselak, MEd - UNC at Chapel Hill; David Gotz - University of North Carolina;
Poster Number: P61
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Education and Training, Curriculum Development, Workforce Development
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
This study took a mixed-method approach to investigate the essential competencies required by health informatics industry jobs in the United States (US). The findings were used to inform and update the curriculum of a US institutional health informatics program.
Speaker:
Fei Yu, PhD
UNC at Chapel Hill
Authors:
Fei Yu, PhD - UNC at Chapel Hill; Jenny Kaselak, MEd - UNC at Chapel Hill; David Gotz - University of North Carolina;
Fei
Yu,
PhD - UNC at Chapel Hill
GenAI vs. Human Reviewers in Classifying Alzheimer’s Disease and Related Dementia (ADRD) Literature: A Comparative Study
Poster Number: P62
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Education and Training, Evaluation, Artificial Intelligence, Teaching Innovation
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
This study compares generative AI (GenAI) and human reviewers in classifying Alzheimer’s disease and related dementia (ADRD) literature into screening, diagnosis, and treatment categories. Initially, GenAI outperformed humans in accuracy (0.8182 vs. 0.6061) and congruency. After providing guidelines, human performance improved significantly (16% accuracy increase, 2.14-fold congruency improvement), while GenAI’s performance slightly declined. Findings underscore GenAI’s potential in medical classification but highlight the critical role of human expertise guided by structured frameworks.
Speaker:
Duo Wei, PhD
Stockton University
Authors:
Duo Wei, PhD - Stockton University; Riya Goyal, BS - Stockton University; Tasnim Raisa, BS - Stockton University; Jeannine Elmasri, BS - Stockton University; Jessica Fleck, PhD - Stockton University;
Poster Number: P62
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Education and Training, Evaluation, Artificial Intelligence, Teaching Innovation
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
This study compares generative AI (GenAI) and human reviewers in classifying Alzheimer’s disease and related dementia (ADRD) literature into screening, diagnosis, and treatment categories. Initially, GenAI outperformed humans in accuracy (0.8182 vs. 0.6061) and congruency. After providing guidelines, human performance improved significantly (16% accuracy increase, 2.14-fold congruency improvement), while GenAI’s performance slightly declined. Findings underscore GenAI’s potential in medical classification but highlight the critical role of human expertise guided by structured frameworks.
Speaker:
Duo Wei, PhD
Stockton University
Authors:
Duo Wei, PhD - Stockton University; Riya Goyal, BS - Stockton University; Tasnim Raisa, BS - Stockton University; Jeannine Elmasri, BS - Stockton University; Jessica Fleck, PhD - Stockton University;
Duo
Wei,
PhD - Stockton University
Exploring Institutional Policies for Generative AI Integration in Health Informatics Education: A Structural and Thematic Analysis
Poster Number: P63
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Education and Training, Policy, Artificial Intelligence
Primary Track: Policy
Programmatic Theme: Academic Informatics / LIEAF
This study explores how health informatics (HI) programs are integrating Generative AI into HI education by analyzing their corresponding institutional guidelines/policies to inform a framework for guidelines on AI use in HI education. Preliminary findings have identified six initial themes and show a wide variation in resource depth and theme coverage. Ongoing work focuses on expanding the analysis to Academic Forum member institutions and performing inductive coding to identify best practices and ethical gaps.
Speaker:
Shivani Gaikwad, Bsc. Biotechnology
Indiana University Purdue University
Authors:
Uloma Odigbo, Undergraduate Student - University of South Florida; Neha Vooppala, Bsc in Pharmacy - Indiana University Purdue University; Josette Jones, RN, PhD - Indiana University; Saptarshi Purkayastha, PhD - Indiana University, Luddy School of Informatics, Computing and Engineering; Christina Eldredge, MD, PhD, FAMIA - University of South Florida;
Poster Number: P63
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Education and Training, Policy, Artificial Intelligence
Primary Track: Policy
Programmatic Theme: Academic Informatics / LIEAF
This study explores how health informatics (HI) programs are integrating Generative AI into HI education by analyzing their corresponding institutional guidelines/policies to inform a framework for guidelines on AI use in HI education. Preliminary findings have identified six initial themes and show a wide variation in resource depth and theme coverage. Ongoing work focuses on expanding the analysis to Academic Forum member institutions and performing inductive coding to identify best practices and ethical gaps.
Speaker:
Shivani Gaikwad, Bsc. Biotechnology
Indiana University Purdue University
Authors:
Uloma Odigbo, Undergraduate Student - University of South Florida; Neha Vooppala, Bsc in Pharmacy - Indiana University Purdue University; Josette Jones, RN, PhD - Indiana University; Saptarshi Purkayastha, PhD - Indiana University, Luddy School of Informatics, Computing and Engineering; Christina Eldredge, MD, PhD, FAMIA - University of South Florida;
Shivani
Gaikwad,
Bsc. Biotechnology - Indiana University Purdue University
Project EMMA: Transforming Emergency Medicine Resident Evaluation Through Mobile Technology
Poster Number: P64
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Education and Training, Workforce Development, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
Accurate assessment of resident physician performance is difficult to standardize and maintaining engagement can be challenging. In order to produce actionable feedback for both learning and certifying ACGME milestones for a given program, we created a cross-collaboration between multi-skilled teams to create a mobile app which significantly increased participation and engagement for assessment of resident physicians in an Emergency Medicine Program. A turnkey UI and back-end architecture allows for both bespoke elements and portability.
Speaker:
Shiv Dua, MD, MS
Allegheny General Hospital
Authors:
Shiv Dua, MD, MS - Allegheny General Hospital; Robert Sobehart, MD - Allegheny General Hospital; Aleta Mizner, DO - Allegheny General Hospital; Siva Komaragiri, MS - Carnegie Mellon University; Rema Padman, PhD - Carnegie Mellon University;
Poster Number: P64
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Education and Training, Workforce Development, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
Accurate assessment of resident physician performance is difficult to standardize and maintaining engagement can be challenging. In order to produce actionable feedback for both learning and certifying ACGME milestones for a given program, we created a cross-collaboration between multi-skilled teams to create a mobile app which significantly increased participation and engagement for assessment of resident physicians in an Emergency Medicine Program. A turnkey UI and back-end architecture allows for both bespoke elements and portability.
Speaker:
Shiv Dua, MD, MS
Allegheny General Hospital
Authors:
Shiv Dua, MD, MS - Allegheny General Hospital; Robert Sobehart, MD - Allegheny General Hospital; Aleta Mizner, DO - Allegheny General Hospital; Siva Komaragiri, MS - Carnegie Mellon University; Rema Padman, PhD - Carnegie Mellon University;
Shiv
Dua,
MD, MS - Allegheny General Hospital
Environmental Impact on Transitioning to a Lower Carbon Footprint Asthma Inhalers
Poster Number: P65
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Environmental Health and Climate Informatics, Population Health, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
In the United States, the health care sector is responsible for 8.5 percent of the country’s greenhouse gas emissions, a share that has only increased over the past several years. Commonly prescribed asthma inhaler metered dose inhalers (MDIs) uses a potent greenhouse gas in its propellent. This study estimated the total carbon footprint of inhalers prescribed at a chest clinic and explored potential reductions in emissions through switching inhalers to a lower carbon footprint option.
Speaker:
Haseena Rajeevan, PhD
Biomedical Informatics and Data Science, Yale University
Author:
Geoffrey Chupp, MD - Pulmonary, Critical Care and Sleep Medicine, Yale University;
Poster Number: P65
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Environmental Health and Climate Informatics, Population Health, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
In the United States, the health care sector is responsible for 8.5 percent of the country’s greenhouse gas emissions, a share that has only increased over the past several years. Commonly prescribed asthma inhaler metered dose inhalers (MDIs) uses a potent greenhouse gas in its propellent. This study estimated the total carbon footprint of inhalers prescribed at a chest clinic and explored potential reductions in emissions through switching inhalers to a lower carbon footprint option.
Speaker:
Haseena Rajeevan, PhD
Biomedical Informatics and Data Science, Yale University
Author:
Geoffrey Chupp, MD - Pulmonary, Critical Care and Sleep Medicine, Yale University;
Haseena
Rajeevan,
PhD - Biomedical Informatics and Data Science, Yale University
Trends in adopting the national environmental exposure assessment note template in the Veterans Affairs Health System
Poster Number: P66
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Environmental Health and Climate Informatics, Public Health, Documentation Burden
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Environmental exposures to toxic chemicals and substances can negatively influence human health. To enable the systematic assessment of military environmental exposures, the Department of Veterans Affairs (VA) rolled out a national note template, “Military Exposure Assessment”, for documenting environmental exposures in October 2024. We analyzed trends in adoption of the national note template in 170 Medical Centers across the US. We also characterized the trends among patients with a neoplasm diagnosis within VA.
Speaker:
Lu He, PhD
University of Wisconsin-Milwaukee
Authors:
Manijeh Berenji, MD MPH - UC Irvine School of Medicine/Joe C Wen School of Population and Public Health (at UC Irvine); VA Long Beach Healthcare System; Suzanne Tamang, PhD - Stanford University; Helen Ma - VA Long Beach;
Poster Number: P66
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Environmental Health and Climate Informatics, Public Health, Documentation Burden
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Environmental exposures to toxic chemicals and substances can negatively influence human health. To enable the systematic assessment of military environmental exposures, the Department of Veterans Affairs (VA) rolled out a national note template, “Military Exposure Assessment”, for documenting environmental exposures in October 2024. We analyzed trends in adoption of the national note template in 170 Medical Centers across the US. We also characterized the trends among patients with a neoplasm diagnosis within VA.
Speaker:
Lu He, PhD
University of Wisconsin-Milwaukee
Authors:
Manijeh Berenji, MD MPH - UC Irvine School of Medicine/Joe C Wen School of Population and Public Health (at UC Irvine); VA Long Beach Healthcare System; Suzanne Tamang, PhD - Stanford University; Helen Ma - VA Long Beach;
Lu
He,
PhD - University of Wisconsin-Milwaukee
Role of Digital Healthcare Technologies in Reducing Carbon Footprints: A Scoping Review of Environmental Impact
Poster Number: P67
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Environmental Health and Climate Informatics, Real-World Evidence Generation, Telemedicine, Healthcare Quality, Transitions of Care
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This scoping review explored the environmental impact of Digital Health Technologies in reducing carbon footprint (CF) in health and social care. We analysed 47 studies from the Web of Science and Scopus databases. Results include study characteristics, study types, environmental impact, CF measurement, outcomes, and strengths/limitations of CF measurement methods. Most studies are small-scale, focusing on transportation. Broader studies with comprehensive GHG assessments and validated CF measurement systems are recommended.
Speaker:
Sarath Rathnayake, RN, BScN, MScN, PhD
University of Bradford, United Kingdom
Authors:
Sarath Rathnayake, RN, BScN, MScN, PhD - University of Bradford, United Kingdom; Natasha Alvarado, BA, MA, PhD - University of Bradford, United Kingdom; Hadiza Ismaila, BSc, MSc, PhD - Bradford University, United Kingdom; Veronica Parisi, BA, MA - University of Bradford, United Kingdom; Chinasa Odo, BSc, MSc, PhD - University of Bradford, United Kingdom; Rebecca Randell, PhD - University of Bradford;
Poster Number: P67
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Environmental Health and Climate Informatics, Real-World Evidence Generation, Telemedicine, Healthcare Quality, Transitions of Care
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This scoping review explored the environmental impact of Digital Health Technologies in reducing carbon footprint (CF) in health and social care. We analysed 47 studies from the Web of Science and Scopus databases. Results include study characteristics, study types, environmental impact, CF measurement, outcomes, and strengths/limitations of CF measurement methods. Most studies are small-scale, focusing on transportation. Broader studies with comprehensive GHG assessments and validated CF measurement systems are recommended.
Speaker:
Sarath Rathnayake, RN, BScN, MScN, PhD
University of Bradford, United Kingdom
Authors:
Sarath Rathnayake, RN, BScN, MScN, PhD - University of Bradford, United Kingdom; Natasha Alvarado, BA, MA, PhD - University of Bradford, United Kingdom; Hadiza Ismaila, BSc, MSc, PhD - Bradford University, United Kingdom; Veronica Parisi, BA, MA - University of Bradford, United Kingdom; Chinasa Odo, BSc, MSc, PhD - University of Bradford, United Kingdom; Rebecca Randell, PhD - University of Bradford;
Sarath
Rathnayake,
RN, BScN, MScN, PhD - University of Bradford, United Kingdom
How Should We Evaluate Artificial Intelligence Scribes? A Systematic Review, Evaluation Framework, and Case Study
Poster Number: P68
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Evaluation, Artificial Intelligence, Documentation Burden
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This panel presentation will explore the current landscape of artificial intelligence (AI) scribe implementation and evaluation. Attendees will gain insights from a systematic review highlighting early findings on AI scribe use, engagement, and performance as well as underscoring gaps in the literature. The session will apply these findings to introduce a comprehensive evaluation framework including approaches for initial and continuing evaluation of AI scribe technologies. The session will also share findings from a forthcoming randomized crossover trial, which applies the aforementioned framework, to compare AI scribe vendors. Panelists will discuss implications of AI advancements, emphasize the need for standardized assessment metrics, and propose strategies for ongoing evaluation and collaboration. This session will equip clinical leaders with tools to effectively implement and assess AI scribes in their institutions.
Speaker:
Adam Yan, MD
The Hospital for Sick Children
Authors:
Evan Orenstein, MD - Children's Healthcare of Atlanta; Jacqueline You, MD - Mass General Brigham; Adam Yan, MD - The Hospital for Sick Children; Naveed Rabbani, MD - Pediatric Physicians' Organization at Children's; Stella Shin, MD - Emory University/ Children's Healthcare of Atlanta;
Poster Number: P68
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Evaluation, Artificial Intelligence, Documentation Burden
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This panel presentation will explore the current landscape of artificial intelligence (AI) scribe implementation and evaluation. Attendees will gain insights from a systematic review highlighting early findings on AI scribe use, engagement, and performance as well as underscoring gaps in the literature. The session will apply these findings to introduce a comprehensive evaluation framework including approaches for initial and continuing evaluation of AI scribe technologies. The session will also share findings from a forthcoming randomized crossover trial, which applies the aforementioned framework, to compare AI scribe vendors. Panelists will discuss implications of AI advancements, emphasize the need for standardized assessment metrics, and propose strategies for ongoing evaluation and collaboration. This session will equip clinical leaders with tools to effectively implement and assess AI scribes in their institutions.
Speaker:
Adam Yan, MD
The Hospital for Sick Children
Authors:
Evan Orenstein, MD - Children's Healthcare of Atlanta; Jacqueline You, MD - Mass General Brigham; Adam Yan, MD - The Hospital for Sick Children; Naveed Rabbani, MD - Pediatric Physicians' Organization at Children's; Stella Shin, MD - Emory University/ Children's Healthcare of Atlanta;
Adam
Yan,
MD - The Hospital for Sick Children
Integrating Census Data with Electronic Health Records to Assess the Impact of Social Determinants of Health on Opioid Use Disorder
Poster Number: P69
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Geospatial (GIS) Data/Analysis, Health Equity, Population Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Electronic health record (EHR) data has become an invaluable resource for biomedical research, yet it is
limited for health disparity and equity research due to the lack of comprehensive information on social
determinants of health (SDoH). In this study, we enriched EHR data from a large healthcare system in the St.
Louis metropolitan area with SDoH from United States Census data to examine patterns of opioid use
disorder (OUD) and access to medications for OUD (MOUD). By linking patient addresses in the EHR to
census tracts in the geospatial data, we identified geographic and sociodemographic disparities in OUD
prevalence and MOUD treatment allocation. Patients who were male, Black, or living in areas with greater
poverty remained less likely to receive treatment. These findings highlight the value of integrating EHR and
geospatial SDoH data to uncover inequities in care and inform more equitable, targeted strategies to address
the opioid epidemic.
Speaker:
Zhen Luo, Master of Science in Biostatistics
Washington University in St. Louis
Authors:
Zhen Luo, Master of Science in Biostatistics - Washington University in St. Louis; Ruochong Fan, MA - Washington University in St. Louis; Wenyu Song, PhD - Brigham and Women's Hospital, Harvard Medical School; Adam Wilcox, PhD - Washington University in St. Louis; Linying Zhang, PhD - Washington University in St. Louis;
Poster Number: P69
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Geospatial (GIS) Data/Analysis, Health Equity, Population Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Electronic health record (EHR) data has become an invaluable resource for biomedical research, yet it is
limited for health disparity and equity research due to the lack of comprehensive information on social
determinants of health (SDoH). In this study, we enriched EHR data from a large healthcare system in the St.
Louis metropolitan area with SDoH from United States Census data to examine patterns of opioid use
disorder (OUD) and access to medications for OUD (MOUD). By linking patient addresses in the EHR to
census tracts in the geospatial data, we identified geographic and sociodemographic disparities in OUD
prevalence and MOUD treatment allocation. Patients who were male, Black, or living in areas with greater
poverty remained less likely to receive treatment. These findings highlight the value of integrating EHR and
geospatial SDoH data to uncover inequities in care and inform more equitable, targeted strategies to address
the opioid epidemic.
Speaker:
Zhen Luo, Master of Science in Biostatistics
Washington University in St. Louis
Authors:
Zhen Luo, Master of Science in Biostatistics - Washington University in St. Louis; Ruochong Fan, MA - Washington University in St. Louis; Wenyu Song, PhD - Brigham and Women's Hospital, Harvard Medical School; Adam Wilcox, PhD - Washington University in St. Louis; Linying Zhang, PhD - Washington University in St. Louis;
Zhen
Luo,
Master of Science in Biostatistics - Washington University in St. Louis
Multilayer U.S. Healthcare Mapping for AI/ML Implementation: Integrating Hospital, Socio-Environmental, and Clinical Data with Initial Focus on California's Elderly Population
Poster Number: P70
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Geospatial (GIS) Data/Analysis, Health Equity, Artificial Intelligence, Surveys and Needs Analysis, Public Health, Information Visualization, Healthcare Quality, Interoperability and Health Information Exchange
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study presents a novel interactive geospatial mapping tool integrating hospital AI/ML adoption status with socio-environmental factors and clinical data for elder care. Spatial analysis revealed significant national clustering of AI/ML adoption. The tool visualizes relationships between healthcare technology capabilities and community needs, enabling stakeholders to identify AI/ML disparities and develop targeted strategies to promote equitable distribution of AI-enhanced resources for elderly populations with complex care needs.
Speaker:
Yeon Mi Hwang, PhD
Stanford University
Authors:
Madelena Ng, DrPH - Stanford University; Malvika Pillai, PhD - Stanford University & VA Palo Alto; Michelle Sahai, BA - Stanford University; Tina Hernandez-Boussard, PhD - Stanford University;
Poster Number: P70
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Geospatial (GIS) Data/Analysis, Health Equity, Artificial Intelligence, Surveys and Needs Analysis, Public Health, Information Visualization, Healthcare Quality, Interoperability and Health Information Exchange
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study presents a novel interactive geospatial mapping tool integrating hospital AI/ML adoption status with socio-environmental factors and clinical data for elder care. Spatial analysis revealed significant national clustering of AI/ML adoption. The tool visualizes relationships between healthcare technology capabilities and community needs, enabling stakeholders to identify AI/ML disparities and develop targeted strategies to promote equitable distribution of AI-enhanced resources for elderly populations with complex care needs.
Speaker:
Yeon Mi Hwang, PhD
Stanford University
Authors:
Madelena Ng, DrPH - Stanford University; Malvika Pillai, PhD - Stanford University & VA Palo Alto; Michelle Sahai, BA - Stanford University; Tina Hernandez-Boussard, PhD - Stanford University;
Yeon Mi
Hwang,
PhD - Stanford University
Shannon Entropy-Driven Analysis of Healthcare Cost Efficiency
Poster Number: P71
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Healthcare Economics/Cost of Care, Diagnostic Systems, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Cost-effective medicine is a salient matter in the realm of healthcare. This work proposes a Shannon entropy-driven approach to evaluate healthcare cost efficiency on an evidence-based foundation. The Physician Fee Schedule (PFS) Relative Value files from the Centers for Medicare and Medicaid Services (CMS) were utilized to evaluate costs of outpatient imaging services. The use of Shannon entropy calculations in assessing the cost-effectiveness of medical diagnostic modalities has merit in advancing healthcare cost efficiency initiatives.
Speaker:
Paul Chong, DO
Walter Reed National Military Medical Center
Authors:
Emma Chua, _ - Pasadena City College; Boyu Peng, Master - Maze Engineers; Shuhan He, MD - Mass General Hospital/Harvard Medical School;
Poster Number: P71
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Healthcare Economics/Cost of Care, Diagnostic Systems, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Cost-effective medicine is a salient matter in the realm of healthcare. This work proposes a Shannon entropy-driven approach to evaluate healthcare cost efficiency on an evidence-based foundation. The Physician Fee Schedule (PFS) Relative Value files from the Centers for Medicare and Medicaid Services (CMS) were utilized to evaluate costs of outpatient imaging services. The use of Shannon entropy calculations in assessing the cost-effectiveness of medical diagnostic modalities has merit in advancing healthcare cost efficiency initiatives.
Speaker:
Paul Chong, DO
Walter Reed National Military Medical Center
Authors:
Emma Chua, _ - Pasadena City College; Boyu Peng, Master - Maze Engineers; Shuhan He, MD - Mass General Hospital/Harvard Medical School;
Paul
Chong,
DO - Walter Reed National Military Medical Center
Oral Health Care Spending Over the Life Span in Commercial and Medicaid Insured Populations
Poster Number: P72
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Healthcare Economics/Cost of Care, Public Health, Policy
Working Group: Dental Informatics Working Group
Primary Track: Policy
Programmatic Theme: Public Health Informatics
Merative MarketScan Research Database 2022 medical and dental claims data were used to examine spending on preventive and major dental care and dental-related health care for those aged 0-89 years. Both Medicaid and commercial preventive and major care spending was high for ages 0-21 years. All Medicaid spending then decreased, while commercial spending on all three types of care increased through age 89. Differing spending patterns suggest unmet dental need in adults with Medicaid coverage.
Speaker:
Morgan Santoro, Master of Public Health
CareQuest Institute for Oral Health
Authors:
Eric Tranby, PhD - CareQuest Institute for Oral Health; Lisa Heaton, PhD - CareQuest Institute for Oral Health; John O'Malley, MHI - CareQuest Institute for Oral Health;
Poster Number: P72
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Healthcare Economics/Cost of Care, Public Health, Policy
Working Group: Dental Informatics Working Group
Primary Track: Policy
Programmatic Theme: Public Health Informatics
Merative MarketScan Research Database 2022 medical and dental claims data were used to examine spending on preventive and major dental care and dental-related health care for those aged 0-89 years. Both Medicaid and commercial preventive and major care spending was high for ages 0-21 years. All Medicaid spending then decreased, while commercial spending on all three types of care increased through age 89. Differing spending patterns suggest unmet dental need in adults with Medicaid coverage.
Speaker:
Morgan Santoro, Master of Public Health
CareQuest Institute for Oral Health
Authors:
Eric Tranby, PhD - CareQuest Institute for Oral Health; Lisa Heaton, PhD - CareQuest Institute for Oral Health; John O'Malley, MHI - CareQuest Institute for Oral Health;
Morgan
Santoro,
Master of Public Health - CareQuest Institute for Oral Health
Decoding the Gut-Brain Axis: Genomic Intersections Between Microbial Dysbiosis and Dementia
Poster Number: P73
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Bioinformatics, Computational Biology
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Dementia pathogenesis involves genetic, environmental, and microbial factors, with gut microbiome dysbiosis increasingly recognized as a contributor to cognitive decline. This study explores genomic intersections between dementia-associated microbes and human neurodegenerative pathways using computational analysis and the BioCyc genome database. Findings reveal microbial taxa influencing neuroinflammatory and metabolic pathways, informing microbiome-targeted therapies. By identifying microbial biomarkers, this research advances precision medicine approaches, highlighting dietary and probiotic interventions for mitigating dementia progression.
Speaker:
Zahrah Shameer, Freshman in High School
Urbana High School
Authors:
Zahrah Shameer, Freshman in High School - Urbana High School; Shamsudeen Moidunny, PhD - University of Miami Miller School of Medicine; Anoop Titus, MD - Brown University; Kamlesh Yadav, PhD - Texas A&M University;
Poster Number: P73
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Bioinformatics, Computational Biology
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Dementia pathogenesis involves genetic, environmental, and microbial factors, with gut microbiome dysbiosis increasingly recognized as a contributor to cognitive decline. This study explores genomic intersections between dementia-associated microbes and human neurodegenerative pathways using computational analysis and the BioCyc genome database. Findings reveal microbial taxa influencing neuroinflammatory and metabolic pathways, informing microbiome-targeted therapies. By identifying microbial biomarkers, this research advances precision medicine approaches, highlighting dietary and probiotic interventions for mitigating dementia progression.
Speaker:
Zahrah Shameer, Freshman in High School
Urbana High School
Authors:
Zahrah Shameer, Freshman in High School - Urbana High School; Shamsudeen Moidunny, PhD - University of Miami Miller School of Medicine; Anoop Titus, MD - Brown University; Kamlesh Yadav, PhD - Texas A&M University;
Zahrah
Shameer,
Freshman in High School - Urbana High School
Standardizing Chaperone Documentation in the Emergency Department: A Cross-Campus Epic Optimization Initiative
Poster Number: P74
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Documentation Burden, Patient Safety, Informatics Implementation, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Chaperone documentation for sensitive examinations is a critical aspect of patient safety, compliance, and institutional policy adherence. Inconsistent documentation practices across emergency departments (EDs) at our multi-campus health system posed challenges for quality monitoring, audit-readiness, and staff accountability. This quality improvement (QI) project aimed to enhance compliance with chaperone documentation by standardizing practices, implementing automated tracking systems, and improving communication. Post-implementation, documentation completeness increased from 41% to 89% across 2 campuses and 4 unique sites.
Speaker:
Michael Alfonzo, MD, MS
Weill Cornell Medicine
Authors:
Peter Steel, MA, MBBA - NewYork-Presbyterian Weill Cornell Medicine; Eli Madden, MD, MPH - Weill Cornell Medicine; Patrick Rumble, BS - Weill Cornell Medicine; Nicole Gerber, MD - Weill Cornell Medicine;
Poster Number: P74
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Documentation Burden, Patient Safety, Informatics Implementation, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Chaperone documentation for sensitive examinations is a critical aspect of patient safety, compliance, and institutional policy adherence. Inconsistent documentation practices across emergency departments (EDs) at our multi-campus health system posed challenges for quality monitoring, audit-readiness, and staff accountability. This quality improvement (QI) project aimed to enhance compliance with chaperone documentation by standardizing practices, implementing automated tracking systems, and improving communication. Post-implementation, documentation completeness increased from 41% to 89% across 2 campuses and 4 unique sites.
Speaker:
Michael Alfonzo, MD, MS
Weill Cornell Medicine
Authors:
Peter Steel, MA, MBBA - NewYork-Presbyterian Weill Cornell Medicine; Eli Madden, MD, MPH - Weill Cornell Medicine; Patrick Rumble, BS - Weill Cornell Medicine; Nicole Gerber, MD - Weill Cornell Medicine;
Michael
Alfonzo,
MD, MS - Weill Cornell Medicine
Expanding AI Explainability with Algorithmovigilance Checkpoints for Trust and Safety (ACTS)
Poster Number: P75
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Human-computer Interaction, Clinical Decision Support, Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Both inexperienced and experienced clinicians are susceptible to automation bias, the tendency to over-rely on AI recommendations. Current XAI approaches that provide information on how patient features inform model outputs, may actually worsen automatic bias. We propose Algorithmovigilance Checkpoints for Trust and Safety (ACTS) to calibration appropriate user trust and promote patient safety by expanding the concept of model explainability to incorporate contextual factors influencing a prediction’s usefulness, including performance drift, fairness, uncertainty.
Speaker:
Sharon Davis, PhD
Vanderbilt University Medical Center
Author:
Megan Salwei, PhD - Vanderbilt University Medical Center;
Poster Number: P75
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Human-computer Interaction, Clinical Decision Support, Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Both inexperienced and experienced clinicians are susceptible to automation bias, the tendency to over-rely on AI recommendations. Current XAI approaches that provide information on how patient features inform model outputs, may actually worsen automatic bias. We propose Algorithmovigilance Checkpoints for Trust and Safety (ACTS) to calibration appropriate user trust and promote patient safety by expanding the concept of model explainability to incorporate contextual factors influencing a prediction’s usefulness, including performance drift, fairness, uncertainty.
Speaker:
Sharon Davis, PhD
Vanderbilt University Medical Center
Author:
Megan Salwei, PhD - Vanderbilt University Medical Center;
Sharon
Davis,
PhD - Vanderbilt University Medical Center
Emergency Clinician Workflow Across Legacy and Upgraded Workstation in the Midst of Technological Change
Poster Number: P76
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Human-computer Interaction, Workflow, Change Management
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Rapid advancements in healthcare technology require corresponding adjustments that impact workflow, staff satisfaction, and patient care. During a recent transition from legacy manual login workstations to newer proximity badge login hypervisor workstations, we found that upgrades induced minor workflow changes that reflect trade-offs between efficiency and security. While technological advancement can enhance specific aspects of performance, it may also introduce new workflow challenges.
Speaker:
Jennifer Rumsey, BS
USCSOMG
Authors:
Sudeep Hegde, PhD - Clemson University; Nicholas Perkins, DO - Prisma Health; Jeff Gerac, MD - Prisma Health; Frederick Lynch, DO - Prisma Health - Upstate; Ronald Pirrallo, MD - Prisma Health - Upstate; Sahil Sawant, BE - Clemson University;
Poster Number: P76
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Human-computer Interaction, Workflow, Change Management
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Rapid advancements in healthcare technology require corresponding adjustments that impact workflow, staff satisfaction, and patient care. During a recent transition from legacy manual login workstations to newer proximity badge login hypervisor workstations, we found that upgrades induced minor workflow changes that reflect trade-offs between efficiency and security. While technological advancement can enhance specific aspects of performance, it may also introduce new workflow challenges.
Speaker:
Jennifer Rumsey, BS
USCSOMG
Authors:
Sudeep Hegde, PhD - Clemson University; Nicholas Perkins, DO - Prisma Health; Jeff Gerac, MD - Prisma Health; Frederick Lynch, DO - Prisma Health - Upstate; Ronald Pirrallo, MD - Prisma Health - Upstate; Sahil Sawant, BE - Clemson University;
Jennifer
Rumsey,
BS - USCSOMG
Graph-to-Text Generation for Adverse Drug Events: Constructing and Leveraging a Knowledge Graph from FAERS
Poster Number: P77
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Extraction, Large Language Models (LLMs), Natural Language Processing, Data Mining
Primary Track: Applications
Adverse drug events(ADEs) pose challenges in drug safety monitoring due to complexity and sparsity of pharmacovigilance data. To address this, we developed a graph-to-text system that constructs and visualizes a knowledge graph from 21.7 million FAERS reports (2004–2024) using Neo4j and generates textual ADE summaries with GPT. The graph captures 10 key relationships between medications, ADEs, and associated factors. This approach enhances pharmacovigilance by improving ADE interpretability and supporting data-driven decision-making in drug safety.
Speaker:
Yiming Li, PhD
Harvard Medical School/Brigham and Women's Hospital
Authors:
Yiming Li, PhD - Harvard Medical School/Brigham and Women's Hospital; Li Zhou, MD, PhD, FACMI, FIAHSI, FAMIA - Brigham and Women's Hospital, Harvard Medical School; Cui Tao, PhD - Mayo Clinic;
Poster Number: P77
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Extraction, Large Language Models (LLMs), Natural Language Processing, Data Mining
Primary Track: Applications
Adverse drug events(ADEs) pose challenges in drug safety monitoring due to complexity and sparsity of pharmacovigilance data. To address this, we developed a graph-to-text system that constructs and visualizes a knowledge graph from 21.7 million FAERS reports (2004–2024) using Neo4j and generates textual ADE summaries with GPT. The graph captures 10 key relationships between medications, ADEs, and associated factors. This approach enhances pharmacovigilance by improving ADE interpretability and supporting data-driven decision-making in drug safety.
Speaker:
Yiming Li, PhD
Harvard Medical School/Brigham and Women's Hospital
Authors:
Yiming Li, PhD - Harvard Medical School/Brigham and Women's Hospital; Li Zhou, MD, PhD, FACMI, FIAHSI, FAMIA - Brigham and Women's Hospital, Harvard Medical School; Cui Tao, PhD - Mayo Clinic;
Yiming
Li,
PhD - Harvard Medical School/Brigham and Women's Hospital
Extracting cognitive domains of Parkinson’s disease patients using large language models
Poster Number: P78
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Extraction, Natural Language Processing, Large Language Models (LLMs)
Primary Track: Applications
This study pioneers the use of LLMs to extract diverse cognitive phenotypes from Parkinson's Disease patient narratives, aiming to address limitations of traditional neuropsychological assessments. We fine-tuned LLaMA-3-8B-instruct to identify 12 cognitive domains, demonstrating potential despite challenges like data imbalance and abstract phenotype representation. Future research will focus on expanding datasets and refining models for improved accuracy and clinical applicability.
Speaker:
Vipina K. Keloth, PhD
Yale University
Authors:
Varada Khanna, BTech - Yale University; Nilay Bhatt, BS - Yale University; Tom Shin, BS - Yale University; Sule Tinaz, MD, PhD - Yale University; Hua Xu, Ph.D - Yale University;
Poster Number: P78
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Extraction, Natural Language Processing, Large Language Models (LLMs)
Primary Track: Applications
This study pioneers the use of LLMs to extract diverse cognitive phenotypes from Parkinson's Disease patient narratives, aiming to address limitations of traditional neuropsychological assessments. We fine-tuned LLaMA-3-8B-instruct to identify 12 cognitive domains, demonstrating potential despite challenges like data imbalance and abstract phenotype representation. Future research will focus on expanding datasets and refining models for improved accuracy and clinical applicability.
Speaker:
Vipina K. Keloth, PhD
Yale University
Authors:
Varada Khanna, BTech - Yale University; Nilay Bhatt, BS - Yale University; Tom Shin, BS - Yale University; Sule Tinaz, MD, PhD - Yale University; Hua Xu, Ph.D - Yale University;
Vipina
K. Keloth,
PhD - Yale University
Extracting Real-World Trends of Cannabis Use from Clinical Documentation
Poster Number: P79
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Extraction, Natural Language Processing, Public Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Many patients with autoimmune rheumatic diseases (ARDs) use cannabis for symptom management, yet documentation in electronic health records (EHRs) is limited and often embedded in unstructured text. We applied natural language processing (NLP) to a large-scale EHR dataset to systematically characterize cannabis use documentation and patient motivations. Using ClinicalBERT and a validated cannabis lexicon, we analyzed 2.6 million clinical notes from 5,051 ARD patients. Our findings reveal disparities in documentation and highlight the importance of informatics approaches for uncovering real-world health behaviors and informing clinical care.
Speaker:
Titilola Falasinnu, PhD
Stanford School of Medicine
Authors:
Titilola Falasinnu, PhD - Stanford School of Medicine; Selen Bozkurt Watson, PhD, MS - Emory University; Tricia Park, BS - Emory University;
Poster Number: P79
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Extraction, Natural Language Processing, Public Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Many patients with autoimmune rheumatic diseases (ARDs) use cannabis for symptom management, yet documentation in electronic health records (EHRs) is limited and often embedded in unstructured text. We applied natural language processing (NLP) to a large-scale EHR dataset to systematically characterize cannabis use documentation and patient motivations. Using ClinicalBERT and a validated cannabis lexicon, we analyzed 2.6 million clinical notes from 5,051 ARD patients. Our findings reveal disparities in documentation and highlight the importance of informatics approaches for uncovering real-world health behaviors and informing clinical care.
Speaker:
Titilola Falasinnu, PhD
Stanford School of Medicine
Authors:
Titilola Falasinnu, PhD - Stanford School of Medicine; Selen Bozkurt Watson, PhD, MS - Emory University; Tricia Park, BS - Emory University;
Titilola
Falasinnu,
PhD - Stanford School of Medicine
Pets4Health: A Literature-Based Tool to Identify Therapeutic Animals
Poster Number: P80
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Retrieval, Controlled Terminologies, Ontologies, and Vocabularies, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Pets have a huge influence on our well-being. Human-animal interactions (HAI) have been shown to decrease levels of cortisol, lower blood pressure, reduce loneliness, increase feeling of social support, improve responsibility, improve mental health, improve nutrition, and avoid bad substances. HAI research can help us learn how animals can support healthcare therapy. The goal of this study was to develop a literature-based tool to identify animals that could be therapeutic for a given disease/condition.
Speaker:
Nathaniel Sarkar, Student
Barrington Middle School
Authors:
Neil Sarkar, PhD, MLIS - Rhode Island Quality Institute & Brown University; Elizabeth Chen, PhD - Brown University;
Poster Number: P80
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Retrieval, Controlled Terminologies, Ontologies, and Vocabularies, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Pets have a huge influence on our well-being. Human-animal interactions (HAI) have been shown to decrease levels of cortisol, lower blood pressure, reduce loneliness, increase feeling of social support, improve responsibility, improve mental health, improve nutrition, and avoid bad substances. HAI research can help us learn how animals can support healthcare therapy. The goal of this study was to develop a literature-based tool to identify animals that could be therapeutic for a given disease/condition.
Speaker:
Nathaniel Sarkar, Student
Barrington Middle School
Authors:
Neil Sarkar, PhD, MLIS - Rhode Island Quality Institute & Brown University; Elizabeth Chen, PhD - Brown University;
Nathaniel
Sarkar,
Student - Barrington Middle School
Quantifying Benefits of a Combined REDCap-on-FHIR + EMERSE Semi-Automated Chart Abstraction Procedure for Clinical Studies
Poster Number: P81
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Retrieval, Informatics Implementation, Data Standards
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Standards like fast healthcare interoperability resources (FHIR) can enhance data quality and reduce manual efforts in research workflows requiring retrieval of electronic health records (EHR) information. The University of Oklahoma launched two FHIR-enabled applications to increase accessibility and efficiency of EHR use in clinical studies. This work showcases the quantifiable advantages of automating case report form completion using REDCap on FHIR and EMERSE tools. The study assesses impact on workloads, accuracy, and feasibility of implementation.
Speaker:
David Bard, PhD
University of Oklahoma Health Sciences
Authors:
Ryan Nipp, MD, MPH - University of Oklahoma Health; Katie Keyser, BS - University of Oklahoma Health; Will Beasley, PhD - University of Oklahoma Health; Thomas Wilson, MS - University of Oklahoma Health; James Cheng, MA - University of Oklahoma Health;
Poster Number: P81
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Retrieval, Informatics Implementation, Data Standards
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Standards like fast healthcare interoperability resources (FHIR) can enhance data quality and reduce manual efforts in research workflows requiring retrieval of electronic health records (EHR) information. The University of Oklahoma launched two FHIR-enabled applications to increase accessibility and efficiency of EHR use in clinical studies. This work showcases the quantifiable advantages of automating case report form completion using REDCap on FHIR and EMERSE tools. The study assesses impact on workloads, accuracy, and feasibility of implementation.
Speaker:
David Bard, PhD
University of Oklahoma Health Sciences
Authors:
Ryan Nipp, MD, MPH - University of Oklahoma Health; Katie Keyser, BS - University of Oklahoma Health; Will Beasley, PhD - University of Oklahoma Health; Thomas Wilson, MS - University of Oklahoma Health; James Cheng, MA - University of Oklahoma Health;
David
Bard,
PhD - University of Oklahoma Health Sciences
A multi-agentic large language model-based framework for cancer registry automation
Poster Number: P82
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Retrieval, Information Extraction, Large Language Models (LLMs), Artificial Intelligence, Natural Language Processing, Data Standards
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
This study presents a multi-agent LLM-based framework (retriever, extractor, mapper) for automating cancer registry abstraction. Collaborating with cancer registrars, informatics experts, and clinicians, the framework accurately extracts and maps site-specific data to NAACCR and STORE standards. Piloted on prostate cancer cases at Mayo Clinic, it achieved 100% accuracy in report retrieval and high accuracy in data extraction and standardization.
Speaker:
Syed Arsalan Ahmed Naqvi, M.B.B.S
Mayo Clinic
Authors:
Denise Pedersen, BS, ODS-C - Mayo Clinic; Umair Ayub, PhD - Mayo Clinic; Umar Afzal, MBBS - Mayo Clinic; Salman Ayub Jajja, MBBS - Mayo Clinic; Ji-Eun Yum, B.S. - Mayo Clinic Alix School of Medicine - Arizona; Santiago Romero-Brufau, MD, PhD - Mayo Clinic; Amye Tevaarwerk, MD - Mayo Clinic; Cui Tao, PhD - Mayo Clinic; Eric Klee, PhD - Mayo Clinic; James Cerhan, MD, PhD - Mayo Clinic; Sara J. Holton, BA, ODS-C - Mayo Clinic; Irbaz Riaz, MD, MBI, PhD - Mayo Clinic;
Poster Number: P82
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Retrieval, Information Extraction, Large Language Models (LLMs), Artificial Intelligence, Natural Language Processing, Data Standards
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
This study presents a multi-agent LLM-based framework (retriever, extractor, mapper) for automating cancer registry abstraction. Collaborating with cancer registrars, informatics experts, and clinicians, the framework accurately extracts and maps site-specific data to NAACCR and STORE standards. Piloted on prostate cancer cases at Mayo Clinic, it achieved 100% accuracy in report retrieval and high accuracy in data extraction and standardization.
Speaker:
Syed Arsalan Ahmed Naqvi, M.B.B.S
Mayo Clinic
Authors:
Denise Pedersen, BS, ODS-C - Mayo Clinic; Umair Ayub, PhD - Mayo Clinic; Umar Afzal, MBBS - Mayo Clinic; Salman Ayub Jajja, MBBS - Mayo Clinic; Ji-Eun Yum, B.S. - Mayo Clinic Alix School of Medicine - Arizona; Santiago Romero-Brufau, MD, PhD - Mayo Clinic; Amye Tevaarwerk, MD - Mayo Clinic; Cui Tao, PhD - Mayo Clinic; Eric Klee, PhD - Mayo Clinic; James Cerhan, MD, PhD - Mayo Clinic; Sara J. Holton, BA, ODS-C - Mayo Clinic; Irbaz Riaz, MD, MBI, PhD - Mayo Clinic;
Syed Arsalan Ahmed
Naqvi,
M.B.B.S - Mayo Clinic
Leveraging Semantic Mapping and Large Language Models to Identify Aging-Related Biomarkers in Electronic Health Records
Poster Number: P83
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Retrieval, Large Language Models (LLMs), Information Visualization, Informatics Implementation, Natural Language Processing, Data Standards
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
This study systematically assessed the representation of aging-related biomarkers in electronic health records (EHRs). We mapped 358 biomarkers from literature reviews to clinical laboratory codes (LOINC) using ontology semantic embeddings, cosine similarity, and validation by a large language model (DeepSeek-R1). A semantic search web application was developed, accurately identifying clinically captured biomarkers. These findings support future predictive modeling and early detection strategies for aging-related diseases, including Alzheimer’s disease.
Speaker:
Md Kamruz Zaman Rana, MSHI
University of Missouri - Columbia
Authors:
Md Kamruz Zaman Rana, MSHI - University of Missouri - Columbia; Abu Mosa, PhD, MS, FAMIA - University of Missouri School of Medicine;
Poster Number: P83
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Retrieval, Large Language Models (LLMs), Information Visualization, Informatics Implementation, Natural Language Processing, Data Standards
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
This study systematically assessed the representation of aging-related biomarkers in electronic health records (EHRs). We mapped 358 biomarkers from literature reviews to clinical laboratory codes (LOINC) using ontology semantic embeddings, cosine similarity, and validation by a large language model (DeepSeek-R1). A semantic search web application was developed, accurately identifying clinically captured biomarkers. These findings support future predictive modeling and early detection strategies for aging-related diseases, including Alzheimer’s disease.
Speaker:
Md Kamruz Zaman Rana, MSHI
University of Missouri - Columbia
Authors:
Md Kamruz Zaman Rana, MSHI - University of Missouri - Columbia; Abu Mosa, PhD, MS, FAMIA - University of Missouri School of Medicine;
Md Kamruz Zaman
Rana,
MSHI - University of Missouri - Columbia
Infographics to Augment Clinical HIV-related Communication Increase Health Knowledge among Latinos with Low Health Literacy
Poster Number: P84
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Visualization, Global Health, Nursing Informatics, Chronic Care Management
Working Group: Nursing Informatics Working Group
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Our team designed bilingual infographics containing information on HIV-self-management. Following successful pilot studies, we further assessed the ability of infographics to improve outcomes via a randomized controlled trial with N=164 participants. This sub-study assessed differences in HIV-knowledge score changes between RCT participants considered “health literate” and “not health literate” at baseline. Findings showed those who received information via infographics, especially those who were "not health literate" at baseline, improved their knowledge scores more and more rapidly over time. This provides further evidence that well-designed infographics can enhance patient education.
Speaker:
Samantha Stonbraker, PhD, MPH, RN, FAAN
University of Colorado Anschutz Medical Campus
Authors:
Samantha Stonbraker, PhD, MPH, RN, FAAN - University of Colorado Anschutz Medical Campus; Stefanie Mayorga, MS - University of Colorado; Yazmina Espiritusanto Castro, BA - Clínica de Familia La Romana; Pamela Baez Caraballo, MD, MSc - Clínica de Familia La Romana; Beatrice A. Francis, MS - Denver Health and Hospital Authority; Zachary Giano, PhD - University of Colorado College of Nursing; Yanjun Gao, PhD - University of Colorado; Edward Gardner, MD - Denver Health and Hospital Authority; Margaret McLees, MD - Denver Health and Hospital Authority; Kellie Hawkins, MD, MPH - Denver Health and Hospital Authority;
Poster Number: P84
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Visualization, Global Health, Nursing Informatics, Chronic Care Management
Working Group: Nursing Informatics Working Group
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Our team designed bilingual infographics containing information on HIV-self-management. Following successful pilot studies, we further assessed the ability of infographics to improve outcomes via a randomized controlled trial with N=164 participants. This sub-study assessed differences in HIV-knowledge score changes between RCT participants considered “health literate” and “not health literate” at baseline. Findings showed those who received information via infographics, especially those who were "not health literate" at baseline, improved their knowledge scores more and more rapidly over time. This provides further evidence that well-designed infographics can enhance patient education.
Speaker:
Samantha Stonbraker, PhD, MPH, RN, FAAN
University of Colorado Anschutz Medical Campus
Authors:
Samantha Stonbraker, PhD, MPH, RN, FAAN - University of Colorado Anschutz Medical Campus; Stefanie Mayorga, MS - University of Colorado; Yazmina Espiritusanto Castro, BA - Clínica de Familia La Romana; Pamela Baez Caraballo, MD, MSc - Clínica de Familia La Romana; Beatrice A. Francis, MS - Denver Health and Hospital Authority; Zachary Giano, PhD - University of Colorado College of Nursing; Yanjun Gao, PhD - University of Colorado; Edward Gardner, MD - Denver Health and Hospital Authority; Margaret McLees, MD - Denver Health and Hospital Authority; Kellie Hawkins, MD, MPH - Denver Health and Hospital Authority;
Samantha
Stonbraker,
PhD, MPH, RN, FAAN - University of Colorado Anschutz Medical Campus
Designing a Visual Analytics Dashboard to Streamline Collaborative Common Data Element Mapping
Poster Number: P85
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Visualization, Human-computer Interaction, Bioinformatics
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics
Common Data Elements (CDEs) are standardized data structures that enable data harmonization and interoperability across studies. Mapping research data to CDEs is essential for achieving these benefits, and several mapping tools have been developed to facilitate this process1. However, due to the complexity of the CDE mapping workflow, such as extensive manual work and iterative multi-round review, it's still challenging to track the overall mapping progress. Additionally, a lack of understanding of the overall distribution of CDEs and data elements also challenges the decision-making. To address these challenges, we leverage human-centered design principles to design and develop a visual analytics dashboard that aims to facilitate the CDE mapping process.
Speaker:
Ruey-Ling Weng, MS.
Yale University
Authors:
Huan He, Ph.D. - Yale University; Vincent Zhang, MS - Yale University; Yujia Zhou, M.S. - Yale University; Lingfei Qian, PHD - Yale University; Jihoon Kim, PhD - Yale University; Na Hong, PhD - Yale University; Hua Xu, Ph.D - Yale University;
Poster Number: P85
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Visualization, Human-computer Interaction, Bioinformatics
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics
Common Data Elements (CDEs) are standardized data structures that enable data harmonization and interoperability across studies. Mapping research data to CDEs is essential for achieving these benefits, and several mapping tools have been developed to facilitate this process1. However, due to the complexity of the CDE mapping workflow, such as extensive manual work and iterative multi-round review, it's still challenging to track the overall mapping progress. Additionally, a lack of understanding of the overall distribution of CDEs and data elements also challenges the decision-making. To address these challenges, we leverage human-centered design principles to design and develop a visual analytics dashboard that aims to facilitate the CDE mapping process.
Speaker:
Ruey-Ling Weng, MS.
Yale University
Authors:
Huan He, Ph.D. - Yale University; Vincent Zhang, MS - Yale University; Yujia Zhou, M.S. - Yale University; Lingfei Qian, PHD - Yale University; Jihoon Kim, PhD - Yale University; Na Hong, PhD - Yale University; Hua Xu, Ph.D - Yale University;
Ruey-Ling
Weng,
MS. - Yale University
Developing a Visual Analytics Tool to Explore the Readability Levels of Health-related Documents
Poster Number: P86
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Visualization, Informatics Implementation, Quantitative Methods
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
For healthcare information to be accessible to a wide range of people, readability assessment is essential. This study aims to develop a visual analytics tool to streamline the readability analysis process. By synthesizing existing research, essentially analytical topics were found, and an R-Shiny application was created. With this application, users can compare readability assessments, examine distributions, enter data, and assess group and overall grade level variations. In addition to being a useful tool for researchers, this standardized approach facilitates efficient communication in healthcare settings. Future work includes formative evaluation and cloud-based deployment.
Speaker:
Himaja Chintalapalli, Medical Sciences
University of Cincinnati
Authors:
Himaja Chintalapalli, Medical Sciences - University of Cincinnati; Anunita Nattam - University of Cincinnati Department of Biomedical Informatics; Danny Wu, PhD - University of North Carolina at Chapel Hill;
Poster Number: P86
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Visualization, Informatics Implementation, Quantitative Methods
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
For healthcare information to be accessible to a wide range of people, readability assessment is essential. This study aims to develop a visual analytics tool to streamline the readability analysis process. By synthesizing existing research, essentially analytical topics were found, and an R-Shiny application was created. With this application, users can compare readability assessments, examine distributions, enter data, and assess group and overall grade level variations. In addition to being a useful tool for researchers, this standardized approach facilitates efficient communication in healthcare settings. Future work includes formative evaluation and cloud-based deployment.
Speaker:
Himaja Chintalapalli, Medical Sciences
University of Cincinnati
Authors:
Himaja Chintalapalli, Medical Sciences - University of Cincinnati; Anunita Nattam - University of Cincinnati Department of Biomedical Informatics; Danny Wu, PhD - University of North Carolina at Chapel Hill;
Himaja
Chintalapalli,
Medical Sciences - University of Cincinnati
Dashboard Development for Tracking Performance of Lung Cancer Screening
Poster Number: P87
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Visualization, Informatics Implementation, User-centered Design Methods, Machine Learning, Clinical Decision Support, Cancer Prevention, Public Health
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Geisinger deployed a machine learning model that identifies high-risk individuals for early Lung Cancer screening, who are contacted by a nursing team to recommend screening. Program stakeholders wanted to measure the success and impact of the screening program, including screening rates and results. We developed a dashboard in Tableau for use by our clinical and outreach teams to track the progress of identifying, conducting outreach, and screening high-risk patients.
Speaker:
Akiva Blickstein, MS
Geisinger
Authors:
Akiva Blickstein, MS - Geisinger; Biplab S Bhattacharya, PhD - Geisinger Health System; Casey Cauthorn, MS - Geisinger; Debdipto Misra, MS - Geisinger; Matthew Facktor, MD - Geisinger; Rebecca Maff, MS - Geisinger; Elliot Mitchell, PhD - Geisinger;
Poster Number: P87
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Information Visualization, Informatics Implementation, User-centered Design Methods, Machine Learning, Clinical Decision Support, Cancer Prevention, Public Health
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Geisinger deployed a machine learning model that identifies high-risk individuals for early Lung Cancer screening, who are contacted by a nursing team to recommend screening. Program stakeholders wanted to measure the success and impact of the screening program, including screening rates and results. We developed a dashboard in Tableau for use by our clinical and outreach teams to track the progress of identifying, conducting outreach, and screening high-risk patients.
Speaker:
Akiva Blickstein, MS
Geisinger
Authors:
Akiva Blickstein, MS - Geisinger; Biplab S Bhattacharya, PhD - Geisinger Health System; Casey Cauthorn, MS - Geisinger; Debdipto Misra, MS - Geisinger; Matthew Facktor, MD - Geisinger; Rebecca Maff, MS - Geisinger; Elliot Mitchell, PhD - Geisinger;
Akiva
Blickstein,
MS - Geisinger
Integrating Non-Medical Drivers of Health Screening into Healthcare Workflows: Lessons from the Travis County Accountable Health Community
Poster Number: P88
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Interoperability and Health Information Exchange, Data Sharing, Workflow, Evaluation, Population Health, Healthcare Economics/Cost of Care, Governance, Usability
Primary Track: Applications
Programmatic Theme: Public Health Informatics
The integration of non-medical drivers of health (NMDOH) into healthcare workflows is critical for improving patient outcomes and reducing costs. This panel will examine the Travis County Accountable Health Community (TCAHC) model, which leverages health information exchange (HIE) to enhance social care coordination. Panelists will discuss strategies for implementing NMDOH screening, optimizing referral workflows, and fostering cross-sector collaboration between healthcare providers and community organizations. Attendees will explore best practices for integrating NMDOH data into clinical decision-making, applying interoperability standards for real-time data sharing, and developing sustainable funding models to support long-term social care integration. The discussion will also address challenges faced by frontline providers in NMDOH screening and patient navigation, offering actionable insights into strengthening care coordination and reducing avoidable emergency department visits. Participants will leave with strategies to enhance social care referral networks, scale NMDOH interventions, and leverage policy levers to drive sustainable change.
Speaker:
Vidya Lakshminarayanan, MS
Connxus
Author:
Becky Carter, MPAff/MPH - Connxus | Central Texas Regional Health Information Exchange;
Poster Number: P88
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Interoperability and Health Information Exchange, Data Sharing, Workflow, Evaluation, Population Health, Healthcare Economics/Cost of Care, Governance, Usability
Primary Track: Applications
Programmatic Theme: Public Health Informatics
The integration of non-medical drivers of health (NMDOH) into healthcare workflows is critical for improving patient outcomes and reducing costs. This panel will examine the Travis County Accountable Health Community (TCAHC) model, which leverages health information exchange (HIE) to enhance social care coordination. Panelists will discuss strategies for implementing NMDOH screening, optimizing referral workflows, and fostering cross-sector collaboration between healthcare providers and community organizations. Attendees will explore best practices for integrating NMDOH data into clinical decision-making, applying interoperability standards for real-time data sharing, and developing sustainable funding models to support long-term social care integration. The discussion will also address challenges faced by frontline providers in NMDOH screening and patient navigation, offering actionable insights into strengthening care coordination and reducing avoidable emergency department visits. Participants will leave with strategies to enhance social care referral networks, scale NMDOH interventions, and leverage policy levers to drive sustainable change.
Speaker:
Vidya Lakshminarayanan, MS
Connxus
Author:
Becky Carter, MPAff/MPH - Connxus | Central Texas Regional Health Information Exchange;
Vidya
Lakshminarayanan,
MS - Connxus
CLEAR-MATCH: Patient-centered, AI-powered clinical trial matching
Poster Number: P89
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Delivering Health Information and Knowledge to the Public, Knowledge Representation and Information Modeling
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Therapeutic clinical trials are used for determining the efficacy of new therapies or existing therapies in new conditions for cancer patients. The number of patients enrolling in clinical trials is often low and the ability of individuals to identify relevant clinical trials for their disease may be limited. Artificial Intelligence (AI)-based tools may increase access to trials by supporting trial matching. Large Language Models (LLMs) are AI-based tools pre-trained from a large corpus of text for predicting subsequent text based on user input and may, if used appropriately, support the patient-driven clinical trial matching process. We have developed a modular, web-based application called CLEAR-MATCH (CLinical Enrollment Assistant in Research studies for trial MATCHing). CLEAR-MATCH implements a chat-like web interface for patient and chatbot interactions combined with a clinical trial matching engine. System components were developed modularly so that systematic evaluation of alternative implementations can be performed.
Speaker:
Amith Murthy, MS
Moffitt Cancer Center
Authors:
Amith Murthy, MS - Moffitt Cancer Center; Daniel Carvajal, Computer Engineer - Moffitt Cancer Center; Guillermo Gonzalez-Calderon - Moffitt Cancer Center & Research Institute; Rhoda Ghai-Cherry, BS - Moffitt Cancer Center; Sebastian Zapata-Tamayo, BS - Moffitt Cancer Center; Rodrigo Carvajal, B.Sc. - Moffitt Cancer Center & Research Institute; Steven Eschrich, PhD - Moffitt Cancer Center;
Poster Number: P89
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Delivering Health Information and Knowledge to the Public, Knowledge Representation and Information Modeling
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Therapeutic clinical trials are used for determining the efficacy of new therapies or existing therapies in new conditions for cancer patients. The number of patients enrolling in clinical trials is often low and the ability of individuals to identify relevant clinical trials for their disease may be limited. Artificial Intelligence (AI)-based tools may increase access to trials by supporting trial matching. Large Language Models (LLMs) are AI-based tools pre-trained from a large corpus of text for predicting subsequent text based on user input and may, if used appropriately, support the patient-driven clinical trial matching process. We have developed a modular, web-based application called CLEAR-MATCH (CLinical Enrollment Assistant in Research studies for trial MATCHing). CLEAR-MATCH implements a chat-like web interface for patient and chatbot interactions combined with a clinical trial matching engine. System components were developed modularly so that systematic evaluation of alternative implementations can be performed.
Speaker:
Amith Murthy, MS
Moffitt Cancer Center
Authors:
Amith Murthy, MS - Moffitt Cancer Center; Daniel Carvajal, Computer Engineer - Moffitt Cancer Center; Guillermo Gonzalez-Calderon - Moffitt Cancer Center & Research Institute; Rhoda Ghai-Cherry, BS - Moffitt Cancer Center; Sebastian Zapata-Tamayo, BS - Moffitt Cancer Center; Rodrigo Carvajal, B.Sc. - Moffitt Cancer Center & Research Institute; Steven Eschrich, PhD - Moffitt Cancer Center;
Amith
Murthy,
MS - Moffitt Cancer Center
Privacy-Preserving Synthetic Data Generation with Large Language Models for Enhanced Discourse-Based ADRD Assessment
Poster Number: P90
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Diagnostic Systems, Privacy and Security, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Alzheimer’s disease and related dementias (ADRD) impact cognitive and linguistic abilities, making discourse-based assessments valuable for early detection. However, limited training data hinder automated models. We propose a privacy-preserving synthetic data generation approach using large language model, to augment discourse datasets. Experimental results show that an ADRD detector trained on synthetic data achieves comparable performance to one trained on real data (p=0.82). Our method enables robust, privacy-compliant discourse analysis for ADRD assessment.
Speaker:
Jiaying Lu, PhD
Emory University School of Nursing's Center for Data Science
Authors:
Shifan Zhao, MS - Emory University; Chen Yue, MS - University of California, Berkeley; Jiexin Zheng, PhD - Peking University; Jiaying Lu, PhD - Emory University School of Nursing's Center for Data Science;
Poster Number: P90
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Diagnostic Systems, Privacy and Security, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Alzheimer’s disease and related dementias (ADRD) impact cognitive and linguistic abilities, making discourse-based assessments valuable for early detection. However, limited training data hinder automated models. We propose a privacy-preserving synthetic data generation approach using large language model, to augment discourse datasets. Experimental results show that an ADRD detector trained on synthetic data achieves comparable performance to one trained on real data (p=0.82). Our method enables robust, privacy-compliant discourse analysis for ADRD assessment.
Speaker:
Jiaying Lu, PhD
Emory University School of Nursing's Center for Data Science
Authors:
Shifan Zhao, MS - Emory University; Chen Yue, MS - University of California, Berkeley; Jiexin Zheng, PhD - Peking University; Jiaying Lu, PhD - Emory University School of Nursing's Center for Data Science;
Jiaying
Lu,
PhD - Emory University School of Nursing's Center for Data Science
AI-driven Demographic Bias Detection in Cardiovascular Medical Education
Poster Number: P91
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Education and Training, Health Equity
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
This study develops a Large Language Model-based approach to identify demographic biases in cardiovascular medical education curricula. Using chain-of-thought prompting strategies with Llama-3, the framework achieved an F1 score of 0.72 for demographic data extraction from clinical vignettes. This automated tool enables systematic evaluation of curricular materials, facilitating the creation of more inclusive educational content and advancing health equity in medical education.
Speaker:
Sudeshna Das, PhD
Emory University
Authors:
Yao Ge, Master - Emory University; Rand Ibrahim, MD - Emory University; Yuting Guo, MS - Emory University; Timothy Moran, PhD - Emory University; Modele Ogunniyi, MD, MPH - Emory University; Abeed Sarker, PhD - Emory University School of Medicine; Marta Rowh, MD, MPH, PhD - Emory University;
Poster Number: P91
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Education and Training, Health Equity
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
This study develops a Large Language Model-based approach to identify demographic biases in cardiovascular medical education curricula. Using chain-of-thought prompting strategies with Llama-3, the framework achieved an F1 score of 0.72 for demographic data extraction from clinical vignettes. This automated tool enables systematic evaluation of curricular materials, facilitating the creation of more inclusive educational content and advancing health equity in medical education.
Speaker:
Sudeshna Das, PhD
Emory University
Authors:
Yao Ge, Master - Emory University; Rand Ibrahim, MD - Emory University; Yuting Guo, MS - Emory University; Timothy Moran, PhD - Emory University; Modele Ogunniyi, MD, MPH - Emory University; Abeed Sarker, PhD - Emory University School of Medicine; Marta Rowh, MD, MPH, PhD - Emory University;
Sudeshna
Das,
PhD - Emory University
Enhancing Type 2 Diabetes Mellitus Phenotyping with GPT-4o via Multi-Hyperparameter Optimized Retrieval-Augmented Generation (RAG) Settings
Poster Number: P92
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Evaluation, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Low precision of ICD codes and unstructured clinical notes complicate cohort identification. This study evaluates GPT-4o's phenotyping capability using optimized Retrieval-Augmented Generation (RAG) settings with seven embedding models for type II diabetes mellitus. Results show significant improvements in key metrics over traditional methods. The insights for practical RAG usage and limitations of LLM phenotyping are also discussed.
Speaker:
Heekyong Park, PhD
Mass General Brigham
Authors:
Martin Rees, BS - Mass General Brigham; Kruger Nils, MD - Brigham and Women’s Hospital; Kenshiro Fuse, MD - Harvard T.H. Chan School of Public Health; Victor Castro, MS - Mass General Brigham; Vivian Gainer, MS - Mass General Brigham; Nich Wattanasin, MS - Mass General Brigham; Barbara Benoit, BS - Mass General Brigham; Kavishwar Wagholikar, MD, PhD - Harvard Medical School /MGH; Shawn Murphy, MD, Ph.D. - Massachusetts General Hospital;
Poster Number: P92
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Evaluation, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Low precision of ICD codes and unstructured clinical notes complicate cohort identification. This study evaluates GPT-4o's phenotyping capability using optimized Retrieval-Augmented Generation (RAG) settings with seven embedding models for type II diabetes mellitus. Results show significant improvements in key metrics over traditional methods. The insights for practical RAG usage and limitations of LLM phenotyping are also discussed.
Speaker:
Heekyong Park, PhD
Mass General Brigham
Authors:
Martin Rees, BS - Mass General Brigham; Kruger Nils, MD - Brigham and Women’s Hospital; Kenshiro Fuse, MD - Harvard T.H. Chan School of Public Health; Victor Castro, MS - Mass General Brigham; Vivian Gainer, MS - Mass General Brigham; Nich Wattanasin, MS - Mass General Brigham; Barbara Benoit, BS - Mass General Brigham; Kavishwar Wagholikar, MD, PhD - Harvard Medical School /MGH; Shawn Murphy, MD, Ph.D. - Massachusetts General Hospital;
Heekyong
Park,
PhD - Mass General Brigham
Is ChatGPT-4o Racially Biased for Medical Diagnosis?
Poster Number: P93
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Fairness and elimination of bias, Artificial Intelligence, Natural Language Processing
Primary Track: Applications
Programmatic Theme: Clinical Informatics
LLMs have been shown to be susceptible to racial biases, which can result in incorrect diagnoses and amplify harmful biases. In this study, we examine ChatGPT-4o’s output as the patient’s race changes and compare it with negligible prompt changes. While the newer version of ChatGPT does not display racial biases for patient diagnosis, it can still be affected by minimal reordering of the symptoms which were given to the LLM.
Speaker:
Tim Gruenloh, MS
University of Wisconsin - Madison
Authors:
Tim Gruenloh, MS - University of Wisconsin - Madison; Marie Pisani, BS - University of Wisconsin, Madison, Department of Medicine; John Caskey - University of Wisconsin-Madison; Maya Kruse, MS - University of Colorado Anschutz Medical Campus; Matthew Churpek, MD, MPH, PhD - University of Wisconsin-Madison; Guanhua Chen, PhD - University of Wisconsin - Madison; Yanjun Gao, PhD - University of Colorado; Majid Afshar, MD, MSCR - University of Wisconsin - Madison; Anoop Mayampurath, PhD - University of Wisconsin - Madison;
Poster Number: P93
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Fairness and elimination of bias, Artificial Intelligence, Natural Language Processing
Primary Track: Applications
Programmatic Theme: Clinical Informatics
LLMs have been shown to be susceptible to racial biases, which can result in incorrect diagnoses and amplify harmful biases. In this study, we examine ChatGPT-4o’s output as the patient’s race changes and compare it with negligible prompt changes. While the newer version of ChatGPT does not display racial biases for patient diagnosis, it can still be affected by minimal reordering of the symptoms which were given to the LLM.
Speaker:
Tim Gruenloh, MS
University of Wisconsin - Madison
Authors:
Tim Gruenloh, MS - University of Wisconsin - Madison; Marie Pisani, BS - University of Wisconsin, Madison, Department of Medicine; John Caskey - University of Wisconsin-Madison; Maya Kruse, MS - University of Colorado Anschutz Medical Campus; Matthew Churpek, MD, MPH, PhD - University of Wisconsin-Madison; Guanhua Chen, PhD - University of Wisconsin - Madison; Yanjun Gao, PhD - University of Colorado; Majid Afshar, MD, MSCR - University of Wisconsin - Madison; Anoop Mayampurath, PhD - University of Wisconsin - Madison;
Tim
Gruenloh,
MS - University of Wisconsin - Madison
Mixed-Methods Evaluation of User Engagement with an LLM-Based Reproductive Health Chatbot
Poster Number: P94
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Global Health, Evaluation, Public Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics
We analyze 432 user interactions from an LLM-based chatbot, deployed in India, that delivers Sexual and Reproductive Health content in Hindi and English. Based on number of messages sent, we categorize users into high, medium, and low engaged users utilizing percentile approach and qualitatively analyze a stratified purposive sample. Preliminary analysis shows unevenly distributed user segment with predominantly low user engagement. While the chatbot achieved initial adoption, strategies to enhance sustained engagement are crucial.
Speaker:
Aradhana Thapa, PhD student
Emory University
Authors:
Aradhana Thapa, PhD student - Emory University; Sumon Kanti Dey, BSc - Emory University; Zeel Mehta, MSc - Myna Mahila Foundation; Tanvi Divate, BA in Economics - Myna Mahila Foundation; Navin Kumar Singh, MA - Myna Mahila Foundation; Suhani Jalota, PhD, MBA - Stanford University, Myna Mahila Foundation; Azra Ismail, PhD - Emory University;
Poster Number: P94
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Global Health, Evaluation, Public Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics
We analyze 432 user interactions from an LLM-based chatbot, deployed in India, that delivers Sexual and Reproductive Health content in Hindi and English. Based on number of messages sent, we categorize users into high, medium, and low engaged users utilizing percentile approach and qualitatively analyze a stratified purposive sample. Preliminary analysis shows unevenly distributed user segment with predominantly low user engagement. While the chatbot achieved initial adoption, strategies to enhance sustained engagement are crucial.
Speaker:
Aradhana Thapa, PhD student
Emory University
Authors:
Aradhana Thapa, PhD student - Emory University; Sumon Kanti Dey, BSc - Emory University; Zeel Mehta, MSc - Myna Mahila Foundation; Tanvi Divate, BA in Economics - Myna Mahila Foundation; Navin Kumar Singh, MA - Myna Mahila Foundation; Suhani Jalota, PhD, MBA - Stanford University, Myna Mahila Foundation; Azra Ismail, PhD - Emory University;
Aradhana
Thapa,
PhD student - Emory University
Exploring the Feasibility of Large Language Models for Social Care-Health Care Integration: Perspectives from the Safety Net
Poster Number: P95
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Health Equity, Human-computer Interaction, Qualitative Methods
Primary Track: Applications
This study explores challenges with social need screening and referral in Michigan’s health and human service organizations and the potential for LLMs to be used to screen social risks. Using interviews and a ChatGPT-simulated chatbot, we identified key barriers and facilitators in four domains. Participants found the chatbot empathetic but raised concerns about accessibility, trust, and its limited role in interpersonal care. Findings suggest LLM-driven tools are better for non-patient-facing tasks for social care integration.
Speaker:
Yongjie Sha, MA
University of Michigan School of Information
Authors:
Yongjie Sha, MA - University of Michigan School of Information; Tiffany Veinot, PhD - University of Michigan School of Information; Megan Threats, PhD, MSLIS - University of Michigan - Ann Arbor;
Poster Number: P95
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Health Equity, Human-computer Interaction, Qualitative Methods
Primary Track: Applications
This study explores challenges with social need screening and referral in Michigan’s health and human service organizations and the potential for LLMs to be used to screen social risks. Using interviews and a ChatGPT-simulated chatbot, we identified key barriers and facilitators in four domains. Participants found the chatbot empathetic but raised concerns about accessibility, trust, and its limited role in interpersonal care. Findings suggest LLM-driven tools are better for non-patient-facing tasks for social care integration.
Speaker:
Yongjie Sha, MA
University of Michigan School of Information
Authors:
Yongjie Sha, MA - University of Michigan School of Information; Tiffany Veinot, PhD - University of Michigan School of Information; Megan Threats, PhD, MSLIS - University of Michigan - Ann Arbor;
Yongjie
Sha,
MA - University of Michigan School of Information
Improving Synthetic Training Data through Social Exemplars for Dialogue Systems Identifying Unmet Health-Related Social Needs
Poster Number: P96
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Human-computer Interaction, Personal Health Informatics, Diversity, Equity, Inclusion, and Accessibility, Health Equity, Public Health, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Unmet health-related social needs (HRSN) challenge personalized care and long-term outcomes. Dialogue systems can scale HRSN identification but require diverse, high-quality training data. We propose using socially grounded exemplars—context-specific alternatives generated by LLMs—to enrich synthetic conversations. Our two-stage approach produces exemplar-enriched personas and dialogues, achieving a 9% increase in lexical diversity with a 6% turn error rate. These findings demonstrate the promise of exemplar-guided generation for training more representative dialogue systems.
Speaker:
Syed-Amad Hussain, BSE
The Ohio State University
Authors:
Syed-Amad Hussain, BSE - The Ohio State University; Daniel Jackson, B.Sc. - Nationwide Childrens Hospital at Abigail Wexner Research Institute; Eric Fosler-Lussier, PhD - The Ohio State University; Emre Sezgin, PhD - Nationwide Children's Hospital / The Ohio State University College of Medicine;
Poster Number: P96
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Human-computer Interaction, Personal Health Informatics, Diversity, Equity, Inclusion, and Accessibility, Health Equity, Public Health, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Unmet health-related social needs (HRSN) challenge personalized care and long-term outcomes. Dialogue systems can scale HRSN identification but require diverse, high-quality training data. We propose using socially grounded exemplars—context-specific alternatives generated by LLMs—to enrich synthetic conversations. Our two-stage approach produces exemplar-enriched personas and dialogues, achieving a 9% increase in lexical diversity with a 6% turn error rate. These findings demonstrate the promise of exemplar-guided generation for training more representative dialogue systems.
Speaker:
Syed-Amad Hussain, BSE
The Ohio State University
Authors:
Syed-Amad Hussain, BSE - The Ohio State University; Daniel Jackson, B.Sc. - Nationwide Childrens Hospital at Abigail Wexner Research Institute; Eric Fosler-Lussier, PhD - The Ohio State University; Emre Sezgin, PhD - Nationwide Children's Hospital / The Ohio State University College of Medicine;
Syed-Amad
Hussain,
BSE - The Ohio State University
Agent-Based LLMs for Reliable Clinical Knowledge Graph Extraction
Poster Number: P97
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Extraction, Knowledge Representation and Information Modeling, Clinical Decision Support, Natural Language Processing, Interoperability and Health Information Exchange
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Large language models often hallucinate, risking inaccurate clinical information. We propose an open-source, agent-based language modeling approach to construct robust knowledge graphs from diverse clinical texts, including lung cancer, Sjögren’s syndrome, and Covid-19 data. Our pipeline, evaluated using betweenness and eigenvector centrality, identifies causal relationships such as Covid-19 causing pneumonia and other complications. Results outperform closed-source alternatives like GPT-4.5, Notebook LM, which offers a scalable, transparent framework for reliable clinical knowledge extraction.
Speaker:
Sydney Anuyah, Research Assistant/ M.Sc
Indiana University, Indianapolis
Authors:
Kaushik Mehedi, Research Assistant/Bachelors - Indiana University, Indianapolis; Brian Dixon, Professor/PhD, MPA, FACMI, FHIMSS - Regenstrief Institute; Rakesh Shiradkar, Professor/Ph.D. - Indiana University, Indianapolis; Sunandan Chakraborty, Professor/Ph.D. - Indiana University, Indianapolis; Arjan Durresi, Professor/Ph.D. - Indiana University, Indianapolis;
Poster Number: P97
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Extraction, Knowledge Representation and Information Modeling, Clinical Decision Support, Natural Language Processing, Interoperability and Health Information Exchange
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Large language models often hallucinate, risking inaccurate clinical information. We propose an open-source, agent-based language modeling approach to construct robust knowledge graphs from diverse clinical texts, including lung cancer, Sjögren’s syndrome, and Covid-19 data. Our pipeline, evaluated using betweenness and eigenvector centrality, identifies causal relationships such as Covid-19 causing pneumonia and other complications. Results outperform closed-source alternatives like GPT-4.5, Notebook LM, which offers a scalable, transparent framework for reliable clinical knowledge extraction.
Speaker:
Sydney Anuyah, Research Assistant/ M.Sc
Indiana University, Indianapolis
Authors:
Kaushik Mehedi, Research Assistant/Bachelors - Indiana University, Indianapolis; Brian Dixon, Professor/PhD, MPA, FACMI, FHIMSS - Regenstrief Institute; Rakesh Shiradkar, Professor/Ph.D. - Indiana University, Indianapolis; Sunandan Chakraborty, Professor/Ph.D. - Indiana University, Indianapolis; Arjan Durresi, Professor/Ph.D. - Indiana University, Indianapolis;
Sydney
Anuyah,
Research Assistant/ M.Sc - Indiana University, Indianapolis
Consistency and Uniqueness of Large Language Models in Clinical Text Extraction: A Focus on Changing and Maintaining Body Position
Poster Number: P98
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Extraction, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study evaluates the consistency and uniqueness of two large language models, Llama 3 and mistral 8x22b, in extracting clinical information on the mobility function “Changing and maintaining body position” from electronic health records. Using binary classification and varying temperature settings, the research measures intra-model reliability via Hamming distances and compares inter-model differences. Results highlight a trade-off between consistency and accuracy, underscoring the need for precise prompt calibration and a hybrid AI–human decision framework.
Speaker:
Xingyi Liu, Ph.D.
Mayo Clinic
Authors:
Xingyi Liu, Ph.D. - Mayo Clinic; Muskan LNU, Postdoctoral Research Fellow - Mayo Clinic; Heling Jia, MD - Mayo Clinic; Jennifer St. Sauver, PhD - Mayo Clinic; Sandeep R. Pagali, MD, MPH - Mayo Clinic; Sunghwan Sohn, PhD - Mayo Clinic;
Poster Number: P98
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Extraction, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study evaluates the consistency and uniqueness of two large language models, Llama 3 and mistral 8x22b, in extracting clinical information on the mobility function “Changing and maintaining body position” from electronic health records. Using binary classification and varying temperature settings, the research measures intra-model reliability via Hamming distances and compares inter-model differences. Results highlight a trade-off between consistency and accuracy, underscoring the need for precise prompt calibration and a hybrid AI–human decision framework.
Speaker:
Xingyi Liu, Ph.D.
Mayo Clinic
Authors:
Xingyi Liu, Ph.D. - Mayo Clinic; Muskan LNU, Postdoctoral Research Fellow - Mayo Clinic; Heling Jia, MD - Mayo Clinic; Jennifer St. Sauver, PhD - Mayo Clinic; Sandeep R. Pagali, MD, MPH - Mayo Clinic; Sunghwan Sohn, PhD - Mayo Clinic;
Xingyi
Liu,
Ph.D. - Mayo Clinic
A Prompt Library for Efficient Clinical Entity Recognition Using Large Language Models
Poster Number: P99
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Extraction, Natural Language Processing
Primary Track: Applications
Clinical information extraction (IE) using Large Language Models (LLMs) is hindered by the lack of standardized prompts and the need for extensive annotated datasets, complicating deployment across diverse clinical scenarios. To address these gaps, we introduce an automated framework for generating a comprehensive prompt library from existing knowledge sources, streamlining clinical IE. This library was populated from 70 PubMed articles on diverse clinical IE tasks. We evaluated our approach using three LLMs (GPT-4o, Llama-3.1-8B-Instruct, and Llama-3.3-70B-Instruct) across ten datasets. In few-shot settings, GPT-4o consistently outperformed Llama models, achieving F1 scores >0.7 on multiple datasets. Fine-tuning significantly improved Llama-3.1-8B-Instruct's performance, surpassing previous state-of-the-art results on four datasets with F1 scores exceeding 0.9. The framework reduces annotation burden, improves generalizability, and establishes performance baselines for clinical IE. This enables researchers and clinicians to efficiently leverage LLMs for diverse healthcare applications while maintaining high accuracy standards essential for clinical settings.
Speaker:
Yang Ren, Ph.D.
Yale University
Authors:
Yang Ren, Ph.D. - Yale University; Vipina K. Keloth, PhD - Yale University; Yan Hu, MS - UTHealth Science Center Houston; Hua Xu, Ph.D - Yale University;
Poster Number: P99
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Extraction, Natural Language Processing
Primary Track: Applications
Clinical information extraction (IE) using Large Language Models (LLMs) is hindered by the lack of standardized prompts and the need for extensive annotated datasets, complicating deployment across diverse clinical scenarios. To address these gaps, we introduce an automated framework for generating a comprehensive prompt library from existing knowledge sources, streamlining clinical IE. This library was populated from 70 PubMed articles on diverse clinical IE tasks. We evaluated our approach using three LLMs (GPT-4o, Llama-3.1-8B-Instruct, and Llama-3.3-70B-Instruct) across ten datasets. In few-shot settings, GPT-4o consistently outperformed Llama models, achieving F1 scores >0.7 on multiple datasets. Fine-tuning significantly improved Llama-3.1-8B-Instruct's performance, surpassing previous state-of-the-art results on four datasets with F1 scores exceeding 0.9. The framework reduces annotation burden, improves generalizability, and establishes performance baselines for clinical IE. This enables researchers and clinicians to efficiently leverage LLMs for diverse healthcare applications while maintaining high accuracy standards essential for clinical settings.
Speaker:
Yang Ren, Ph.D.
Yale University
Authors:
Yang Ren, Ph.D. - Yale University; Vipina K. Keloth, PhD - Yale University; Yan Hu, MS - UTHealth Science Center Houston; Hua Xu, Ph.D - Yale University;
Yang
Ren,
Ph.D. - Yale University
LLM-assisted Error Analysis and Reasoning for Clinical Concept Extraction
Poster Number: P100
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Extraction, Natural Language Processing
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
The majority of valuable information in electronic health records (EHRs) is written in free text requiring natural language processing (NLP) to autonomously extract information. However, EHR-based concept extraction faces challenges including multi-institutional portability, model generalizability, and result explainability. Multi-institutional portability measures out-of-box and post-refinement performance of a model when deploying it to EHRs from different institutions. Model deployment error analysis is a process used to document errors that appear after an NLP model run, which is mostly done manually by reviewing types of the errors, followed by adjusting the model to fix errors (i.e., refinement) to achieve optimal performance. However, this process is iterative and often complex, requiring both NLP expertise and problem-specific domain knowledge. The objective of our study is to systematically evaluation LLM’s capability in supporting systematic error analysis and reasoning using standard taxonomy across various clinical concept extraction tasks. We implemented GPT-4o, GPT-o3-mini, and GPT-o1 under the PHI-compliant Azure environment and Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct, and Llama-3.1-405B-Instruct-FP8 architectures within a PHI-compliant GPT environment. We employed in-context learning by providing error taxonomy, task-specific guideline, examples and structured prompts to guide model responses without requiring fine-tuning. GPT-4o achieved the highest F1-scores for the delirium (0.54) and fall (0.51) tasks. For the function task, GPT-o1 performed best with an F1-score of 0.40. In the SDoH task, LLaMA-3.1-70B-Instruct achieved the highest F1-score of 0.37. Overall GPT-4o consistently demonstrates strong and stable performance across tasks. Smaller LLaMA models, such as the 8B variant, show much lower F1-scores. GPT-based models generally outperform LLaMA models as expected.
Speaker:
Sunyang Fu, PhD, MHI
UTHealth
Authors:
Sunyang Fu, PhD, MHI - UTHealth; Qiuhao Lu, Ph.D. - University of Texas Health Science Center at Houston; Jaeron Ahn, PhD - UTHealth; Hanyun Yin, MS - Texas A&M University; Zhiyi Yue, MA - UTHealth; Fang Chen, Master - University of Texas Health Science Center at Houston; Taylor Harrison, M.B.A. - Mayo Clinic; Ming Huang, PhD - UTHealth Houston; Andrew Wen, MS - University of Texas Health Sciences Center at Houston; Liwei Wang, MD, PhD - UTHealth; Nahid Rianon, MD, PrPH - UTHealth; Min Ji Kwak, MD, DrPH - UTHealth; Ruihong Huang, PhD - UTHealth; Hongfang Liu, PhD - University of Texas Health Science Center at Houston;
Poster Number: P100
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Extraction, Natural Language Processing
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
The majority of valuable information in electronic health records (EHRs) is written in free text requiring natural language processing (NLP) to autonomously extract information. However, EHR-based concept extraction faces challenges including multi-institutional portability, model generalizability, and result explainability. Multi-institutional portability measures out-of-box and post-refinement performance of a model when deploying it to EHRs from different institutions. Model deployment error analysis is a process used to document errors that appear after an NLP model run, which is mostly done manually by reviewing types of the errors, followed by adjusting the model to fix errors (i.e., refinement) to achieve optimal performance. However, this process is iterative and often complex, requiring both NLP expertise and problem-specific domain knowledge. The objective of our study is to systematically evaluation LLM’s capability in supporting systematic error analysis and reasoning using standard taxonomy across various clinical concept extraction tasks. We implemented GPT-4o, GPT-o3-mini, and GPT-o1 under the PHI-compliant Azure environment and Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct, and Llama-3.1-405B-Instruct-FP8 architectures within a PHI-compliant GPT environment. We employed in-context learning by providing error taxonomy, task-specific guideline, examples and structured prompts to guide model responses without requiring fine-tuning. GPT-4o achieved the highest F1-scores for the delirium (0.54) and fall (0.51) tasks. For the function task, GPT-o1 performed best with an F1-score of 0.40. In the SDoH task, LLaMA-3.1-70B-Instruct achieved the highest F1-score of 0.37. Overall GPT-4o consistently demonstrates strong and stable performance across tasks. Smaller LLaMA models, such as the 8B variant, show much lower F1-scores. GPT-based models generally outperform LLaMA models as expected.
Speaker:
Sunyang Fu, PhD, MHI
UTHealth
Authors:
Sunyang Fu, PhD, MHI - UTHealth; Qiuhao Lu, Ph.D. - University of Texas Health Science Center at Houston; Jaeron Ahn, PhD - UTHealth; Hanyun Yin, MS - Texas A&M University; Zhiyi Yue, MA - UTHealth; Fang Chen, Master - University of Texas Health Science Center at Houston; Taylor Harrison, M.B.A. - Mayo Clinic; Ming Huang, PhD - UTHealth Houston; Andrew Wen, MS - University of Texas Health Sciences Center at Houston; Liwei Wang, MD, PhD - UTHealth; Nahid Rianon, MD, PrPH - UTHealth; Min Ji Kwak, MD, DrPH - UTHealth; Ruihong Huang, PhD - UTHealth; Hongfang Liu, PhD - University of Texas Health Science Center at Houston;
Sunyang
Fu,
PhD, MHI - UTHealth
Comparing Prompting Methods for Entity Extraction from Clinical Notes with RAG and Divide-and-Conquer Novel Methods Development
Poster Number: P101
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Extraction, Data Mining, Quantitative Methods, Natural Language Processing, Artificial Intelligence
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Large language models (LLMs) have demonstrated successful information extraction abilities and are dependent on the prompting techniques that elicit responses, with complex and long prompts degrading performance. This study develops and compares four prompting techniques that address these issues, including novel retrieval-augmented generation (RAG) and clinical divide-and-conquer (ClinDAC) methods. Evaluated against a manually-curated reference of echocardiograms, dividing prompts to reduce concurrent tasks improved performance in 5/6 models, whereas RAG and ClinDAC methods reduced prompt length.
Speaker:
Levi Kaster, BS
Washington University in St. Louis
Author:
Andrew Michelson, MD - Washington University in St. Louis;
Poster Number: P101
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Extraction, Data Mining, Quantitative Methods, Natural Language Processing, Artificial Intelligence
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Large language models (LLMs) have demonstrated successful information extraction abilities and are dependent on the prompting techniques that elicit responses, with complex and long prompts degrading performance. This study develops and compares four prompting techniques that address these issues, including novel retrieval-augmented generation (RAG) and clinical divide-and-conquer (ClinDAC) methods. Evaluated against a manually-curated reference of echocardiograms, dividing prompts to reduce concurrent tasks improved performance in 5/6 models, whereas RAG and ClinDAC methods reduced prompt length.
Speaker:
Levi Kaster, BS
Washington University in St. Louis
Author:
Andrew Michelson, MD - Washington University in St. Louis;
Levi
Kaster,
BS - Washington University in St. Louis
Probing Fine-grained and Coarse-grained Embeddings with Large Language Models for Patient-Trial Matching using Siamese Neural Network
Poster Number: P102
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Knowledge Representation and Information Modeling, Natural Language Processing
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
This study contributes to a broader understanding of the contextualization ability of LLM for patient-trial matching by drawing comparative insights between LLM-derived fine-grained and coarse-grained embeddings. In conjunction with a novel Siamese Neural Network-based model, namely Siamese-PTM, we investigate LlaMa-2’s ability to learn contextualized representations of the patient’s EHR and trial criteria at different granularity and validate it for patient-trial matching through both quantitative and qualitative evaluations on multiple datasets.
Speaker:
Shaika Chowdhury, PhD
Mayo Clinic
Authors:
Shaika Chowdhury, PhD - Mayo Clinic; Sivaraman Rajaganapathy, Research Fellow/Ph.D. - Mayo Clinic; Yue Yu, Ph.D. - Mayo Clinic; Cui Tao, PhD - Mayo Clinic; Young J Juhn, M.D., M.P.H - Mayo Clinic; James R Cerhan, M.D., PhD - Mayo Clinic; Maria Vassilaki, MD, PhD - Mayo Clinic; Nansu Zong, Ph.D. - Mayo Clinic;
Poster Number: P102
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Knowledge Representation and Information Modeling, Natural Language Processing
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
This study contributes to a broader understanding of the contextualization ability of LLM for patient-trial matching by drawing comparative insights between LLM-derived fine-grained and coarse-grained embeddings. In conjunction with a novel Siamese Neural Network-based model, namely Siamese-PTM, we investigate LlaMa-2’s ability to learn contextualized representations of the patient’s EHR and trial criteria at different granularity and validate it for patient-trial matching through both quantitative and qualitative evaluations on multiple datasets.
Speaker:
Shaika Chowdhury, PhD
Mayo Clinic
Authors:
Shaika Chowdhury, PhD - Mayo Clinic; Sivaraman Rajaganapathy, Research Fellow/Ph.D. - Mayo Clinic; Yue Yu, Ph.D. - Mayo Clinic; Cui Tao, PhD - Mayo Clinic; Young J Juhn, M.D., M.P.H - Mayo Clinic; James R Cerhan, M.D., PhD - Mayo Clinic; Maria Vassilaki, MD, PhD - Mayo Clinic; Nansu Zong, Ph.D. - Mayo Clinic;
Shaika
Chowdhury,
PhD - Mayo Clinic
TrialGenie: Empowering Clinical Trial Design with Agentic Intelligence and Real World Data
Poster Number: P103
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Real-World Evidence Generation, Artificial Intelligence
Primary Track: Applications
We present TrialGenie, an LLM-powered multi-agent collaboration pipeline for extracting real world evidence (RWE) to inform clinical trial design. TrialGenie can robustly perform target trial emulation and obtain estimated treatment effects that are similar to the ones derived from real-world randomized controlled trials, showing great promise in improving the efficiency and effectiveness of clinical trial designs.
Speaker:
Haoyang Li, PhD
Weill Cornell Medicine
Authors:
Haoyang Li, PhD - Weill Cornell Medicine; Weishen Pan, PhD - Weill Cornell Medicine; Suraj Rajendran, PhD - Weill Cornell Medicine; Chengxi Zang, PHD - Weill Cornell Medicine; Fei Wang, PhD - Weill Cornell Medicine;
Poster Number: P103
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Real-World Evidence Generation, Artificial Intelligence
Primary Track: Applications
We present TrialGenie, an LLM-powered multi-agent collaboration pipeline for extracting real world evidence (RWE) to inform clinical trial design. TrialGenie can robustly perform target trial emulation and obtain estimated treatment effects that are similar to the ones derived from real-world randomized controlled trials, showing great promise in improving the efficiency and effectiveness of clinical trial designs.
Speaker:
Haoyang Li, PhD
Weill Cornell Medicine
Authors:
Haoyang Li, PhD - Weill Cornell Medicine; Weishen Pan, PhD - Weill Cornell Medicine; Suraj Rajendran, PhD - Weill Cornell Medicine; Chengxi Zang, PHD - Weill Cornell Medicine; Fei Wang, PhD - Weill Cornell Medicine;
Haoyang
Li,
PhD - Weill Cornell Medicine
Better Domain Insight, Better Learning: Unlocking the Potential of ECG Foundation Models with Paired Cardiologist Reports
Poster Number: P104
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Bioinformatics, Large Language Models (LLMs), Deep Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Accurate electrogram (ECG) classification is essential for diagnosing cardiac conditions. Multimodal ECG Representation Learning (MERL), a cross-modal ECG foundation model leveraging both signals and matched clinical notes, has great potential for enhancing diagnostic accuracy. However, note encoding strategies remain underexplored. We evaluated five large language models (LLMs) for processing machine-generated reports to assess the impact of text embedding choices. Additionally, we compared the original and context-enhanced cardiologist reports and found that improve ECG representation learning compared to machine-generated reports. The context-enhanced reports achieved the highest AUC scores of 0.9290 ± 0.0010 for classifying five cardiac conditions in an in-hospital ECG dataset (PTB-XL) and 0.7774 ± 0.0105 for identifying acute coronary syndromes in a prehospital dataset (MEDIC). Our findings underscore the critical importance of refined clinical notes in MERL, suggesting that improved text quality significantly enhances feature extraction and model performance.
Speaker:
Lovely Yeswanth Panchumarthi, Masters in Computer Science
Emory University
Authors:
Ran Xiao, PhD - Emory University; Xiao Hu, PhD - Emory University; zeyuan Meng, Masters in Computer Science - Emory University; Saurabh Kataria, Postdoctoral - Emory University; Brain Gow, Professor - Massachusetts Institute of Technology; Tom Joseph Pollard, Professor - Massachusetts Institute of Technology; Del Bold, Professor - Emory University; Jessica Zègre-Hemsey, Assistant Professor - University of North Carolina at Chapel Hill; Dillon Dzikowicz, Assistant Professor - University of Rochester; Sherin Thomas, Masters in Data Science - University of Rochester; Chi-Ju Lia, Masters in Computer Science - University of Rochester; L’Juan Hale, Assistant Professor - Emory University; David W. Wright, Assistant Professor - Emory University; Lekshmi Kumar, Associate Professor - Emory University;
Poster Number: P104
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Bioinformatics, Large Language Models (LLMs), Deep Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Accurate electrogram (ECG) classification is essential for diagnosing cardiac conditions. Multimodal ECG Representation Learning (MERL), a cross-modal ECG foundation model leveraging both signals and matched clinical notes, has great potential for enhancing diagnostic accuracy. However, note encoding strategies remain underexplored. We evaluated five large language models (LLMs) for processing machine-generated reports to assess the impact of text embedding choices. Additionally, we compared the original and context-enhanced cardiologist reports and found that improve ECG representation learning compared to machine-generated reports. The context-enhanced reports achieved the highest AUC scores of 0.9290 ± 0.0010 for classifying five cardiac conditions in an in-hospital ECG dataset (PTB-XL) and 0.7774 ± 0.0105 for identifying acute coronary syndromes in a prehospital dataset (MEDIC). Our findings underscore the critical importance of refined clinical notes in MERL, suggesting that improved text quality significantly enhances feature extraction and model performance.
Speaker:
Lovely Yeswanth Panchumarthi, Masters in Computer Science
Emory University
Authors:
Ran Xiao, PhD - Emory University; Xiao Hu, PhD - Emory University; zeyuan Meng, Masters in Computer Science - Emory University; Saurabh Kataria, Postdoctoral - Emory University; Brain Gow, Professor - Massachusetts Institute of Technology; Tom Joseph Pollard, Professor - Massachusetts Institute of Technology; Del Bold, Professor - Emory University; Jessica Zègre-Hemsey, Assistant Professor - University of North Carolina at Chapel Hill; Dillon Dzikowicz, Assistant Professor - University of Rochester; Sherin Thomas, Masters in Data Science - University of Rochester; Chi-Ju Lia, Masters in Computer Science - University of Rochester; L’Juan Hale, Assistant Professor - Emory University; David W. Wright, Assistant Professor - Emory University; Lekshmi Kumar, Associate Professor - Emory University;
Lovely Yeswanth
Panchumarthi,
Masters in Computer Science - Emory University
Integrating Symbolic Regression for Generalizable and Interpretable Machine Learning in Cardiovascular Risk Prediction
Poster Number: P105
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Diagnostic Systems, Clinical Decision Support
Primary Track: Foundations
Accurate atherosclerotic cardiovascular disease (CVD) risk prediction is crucial for timely and targeted risk factor modification. Machine learning (ML) has been applied to CVD risk prediction but has limitations. We apply a symbolic regression ML modeling approach with random forest (SReRF). We found that SReRF performed well on a small dataset (10k) and outperformed traditional RF. Future SReRF will incorporate different functions that may further enhance their performance while maintaining interpretability.
Speaker:
Michael Ferguson, PhD.
UMass Chan Medical School
Authors:
Feifan Liu, PhD - University of Massachusetts Chan Medical School; Ben Gerber, MD, MPH - University of Massachusetts Chan Medical School; Huanmei Wu, FAMIA, PhD - Temple University; Teresa Schmidt, PhD - OCHIN; Honghuang Lin, PhD - University of Massachusetts Chan Medical School; David McManus, MD - University of Massachusetts Chan Medical School;
Poster Number: P105
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Diagnostic Systems, Clinical Decision Support
Primary Track: Foundations
Accurate atherosclerotic cardiovascular disease (CVD) risk prediction is crucial for timely and targeted risk factor modification. Machine learning (ML) has been applied to CVD risk prediction but has limitations. We apply a symbolic regression ML modeling approach with random forest (SReRF). We found that SReRF performed well on a small dataset (10k) and outperformed traditional RF. Future SReRF will incorporate different functions that may further enhance their performance while maintaining interpretability.
Speaker:
Michael Ferguson, PhD.
UMass Chan Medical School
Authors:
Feifan Liu, PhD - University of Massachusetts Chan Medical School; Ben Gerber, MD, MPH - University of Massachusetts Chan Medical School; Huanmei Wu, FAMIA, PhD - Temple University; Teresa Schmidt, PhD - OCHIN; Honghuang Lin, PhD - University of Massachusetts Chan Medical School; David McManus, MD - University of Massachusetts Chan Medical School;
Michael
Ferguson,
PhD. - UMass Chan Medical School
Data Representation Influences Groupwise Predictive Efficacy of Cardiovascular Outcomes in Obstructive Sleep Apnea Patientsn for Sleep Apnea Patients
Poster Number: P106
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Fairness and elimination of bias, Precision Medicine
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Predicting cardiovascular disease development in obstructive sleep apnea patients can tailor better preventive therapy, but predictive models in healthcare often have poor distributional robustness, i.e., they do not work equally well for all patients or all settings. Using data from the Sleep Heart Health Study and Wisconsin Sleep Cohort, we show how modifying training set proportions can help improve internal distributional robustness in predicting outcomes like congestive heart failure and stroke.
Speaker:
Victor Borza, M.S.
Vanderbilt University Department of Biomedical Informatics
Authors:
Raghu Upender, MD, MBA - Vanderbilt University Medical Center; Bradley Malin, PhD - Vanderbilt University Medical Center;
Poster Number: P106
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Fairness and elimination of bias, Precision Medicine
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Predicting cardiovascular disease development in obstructive sleep apnea patients can tailor better preventive therapy, but predictive models in healthcare often have poor distributional robustness, i.e., they do not work equally well for all patients or all settings. Using data from the Sleep Heart Health Study and Wisconsin Sleep Cohort, we show how modifying training set proportions can help improve internal distributional robustness in predicting outcomes like congestive heart failure and stroke.
Speaker:
Victor Borza, M.S.
Vanderbilt University Department of Biomedical Informatics
Authors:
Raghu Upender, MD, MBA - Vanderbilt University Medical Center; Bradley Malin, PhD - Vanderbilt University Medical Center;
Victor
Borza,
M.S. - Vanderbilt University Department of Biomedical Informatics
Applying Multi-Objective Optimization and Feature Selection to Predicting Patient Deterioration
Poster Number: P107
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Fairness and elimination of bias, Racial disparities, Patient Safety, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Epic’s Deterioration Index (DTI) is a proprietary machine learning model that predicts a patient’s likelihood of deterioration (defined as care escalation or death). We report results from a preliminary external validation of Epic’s DTI at Cedars-Sinai Medical Center and compare its performance to a fairness-aware alternative (Fair DTI). Both models achieved comparable balanced accuracy scores (0.64 – 0.68). Both models underperformed for Asian patients though the Fair DTI achieved an overall lower false negative rate.
Speaker:
Emily Wong, PhD
Cedars-Sinai Medical Center
Authors:
Anil Saini, PhD - Cedars-Sinai Medical Center; Emily Wong, PhD - Cedars-Sinai Medical Center; Priyanka Merchant, MS - Cedars-Sinai Medical Center; Nicole Beyrouthy, MS, RN-BC - Cedars-Sinai Medical Center; Michael Stange, BS - Cedars-Sinai Medical Center; Matthew Moore, MS - Cedars-Sinai Medical Center; Andrew Hudson, MD - Cedars-Sinai Medical Center; Tiffani Bright, PhD - Cedars-Sinai Medical Center;
Poster Number: P107
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Fairness and elimination of bias, Racial disparities, Patient Safety, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Epic’s Deterioration Index (DTI) is a proprietary machine learning model that predicts a patient’s likelihood of deterioration (defined as care escalation or death). We report results from a preliminary external validation of Epic’s DTI at Cedars-Sinai Medical Center and compare its performance to a fairness-aware alternative (Fair DTI). Both models achieved comparable balanced accuracy scores (0.64 – 0.68). Both models underperformed for Asian patients though the Fair DTI achieved an overall lower false negative rate.
Speaker:
Emily Wong, PhD
Cedars-Sinai Medical Center
Authors:
Anil Saini, PhD - Cedars-Sinai Medical Center; Emily Wong, PhD - Cedars-Sinai Medical Center; Priyanka Merchant, MS - Cedars-Sinai Medical Center; Nicole Beyrouthy, MS, RN-BC - Cedars-Sinai Medical Center; Michael Stange, BS - Cedars-Sinai Medical Center; Matthew Moore, MS - Cedars-Sinai Medical Center; Andrew Hudson, MD - Cedars-Sinai Medical Center; Tiffani Bright, PhD - Cedars-Sinai Medical Center;
Emily
Wong,
PhD - Cedars-Sinai Medical Center
Rural Medical Centers Struggle to Produce Well-Calibrated Clinical Prediction Models: Data Augmentation Can Help
Poster Number: P108
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Health Equity, Artificial Intelligence
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Machine learning models support many clinical tasks; however, challenges arise with the transportability of these models across a network of healthcare sites. While there are guidelines for updating models to account for local context, we hypothesize that not all healthcare organizations, especially those in smaller and rural communities, have the necessary patient volumes to facilitate local fine tuning to ensure models are reliable for their populations. To investigate these challenges, we conducted an experiment using data from a real network of hospitals to predict 30-day unplanned hospital readmission and a simulation study using data from a multi-site ICU dataset to evaluate the utility of synthetic data generation (SDG) to augment local data volumes. Several factors associated with rurality were correlated with model miscalibration and rural sites failed to meet sample size requirements for local recalibration. Our results indicate that deep learning approaches to SDG produced the best local classifiers.
Speaker:
Katherine Brown, PhD
Vanderbilt University Medical Center
Authors:
Katherine Brown, PhD - Vanderbilt University Medical Center; Bradley Malin, PhD - Vanderbilt University Medical Center; Sharon Davis, PhD - Vanderbilt University Medical Center;
Poster Number: P108
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Health Equity, Artificial Intelligence
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Machine learning models support many clinical tasks; however, challenges arise with the transportability of these models across a network of healthcare sites. While there are guidelines for updating models to account for local context, we hypothesize that not all healthcare organizations, especially those in smaller and rural communities, have the necessary patient volumes to facilitate local fine tuning to ensure models are reliable for their populations. To investigate these challenges, we conducted an experiment using data from a real network of hospitals to predict 30-day unplanned hospital readmission and a simulation study using data from a multi-site ICU dataset to evaluate the utility of synthetic data generation (SDG) to augment local data volumes. Several factors associated with rurality were correlated with model miscalibration and rural sites failed to meet sample size requirements for local recalibration. Our results indicate that deep learning approaches to SDG produced the best local classifiers.
Speaker:
Katherine Brown, PhD
Vanderbilt University Medical Center
Authors:
Katherine Brown, PhD - Vanderbilt University Medical Center; Bradley Malin, PhD - Vanderbilt University Medical Center; Sharon Davis, PhD - Vanderbilt University Medical Center;
Katherine
Brown,
PhD - Vanderbilt University Medical Center
Predicting Negative Patient Experience in the Hospital Setting
Poster Number: P109
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Healthcare Quality, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Predicting a patient’s satisfaction during their inpatient hospital stay is a difficult yet important task. Using information available during a patient’s hospitalization, we aimed to predict whether a patient would rate their inpatient hospital experience negatively on the HCAHPS survey using a generalized linear model with Lasso penalization.
Speaker:
Katherine Bews, MS
Mayo Clinic
Author:
Brendan Broderick, M.S. - Mayo Clinic;
Poster Number: P109
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Healthcare Quality, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Predicting a patient’s satisfaction during their inpatient hospital stay is a difficult yet important task. Using information available during a patient’s hospitalization, we aimed to predict whether a patient would rate their inpatient hospital experience negatively on the HCAHPS survey using a generalized linear model with Lasso penalization.
Speaker:
Katherine Bews, MS
Mayo Clinic
Author:
Brendan Broderick, M.S. - Mayo Clinic;
Katherine
Bews,
MS - Mayo Clinic
AI-Computer Based Antimicrobial Monitoring (AI-CBAM) to Address Inefficiencies in an Antimicrobial Stewardship Program at Mayo Clinic Rochester
Poster Number: P110
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Infectious Diseases and Epidemiology, Evaluation, Informatics Implementation
Primary Track: Applications
Mayo Clinic employs an Antimicrobial Stewardship program aimed at improving patient outcomes and reducing use of restricted antimicrobial agents, but the number of patients that can be assessed is limited due to manual chart review. The Kern center has developed two machine learning models and a UI to help identify and present patients needing infectious disease consultation and antimicrobial optimization. This initiative is currently undergoing a clinical trial to assess its effectiveness.
Speaker:
Ricardo Rojas, BA
Mayo Clinic
Author:
Ricardo Rojas, BA - Mayo Clinic;
Poster Number: P110
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Machine Learning, Infectious Diseases and Epidemiology, Evaluation, Informatics Implementation
Primary Track: Applications
Mayo Clinic employs an Antimicrobial Stewardship program aimed at improving patient outcomes and reducing use of restricted antimicrobial agents, but the number of patients that can be assessed is limited due to manual chart review. The Kern center has developed two machine learning models and a UI to help identify and present patients needing infectious disease consultation and antimicrobial optimization. This initiative is currently undergoing a clinical trial to assess its effectiveness.
Speaker:
Ricardo Rojas, BA
Mayo Clinic
Author:
Ricardo Rojas, BA - Mayo Clinic;
Ricardo
Rojas,
BA - Mayo Clinic
Generative AI in Mental Healthcare: A Systematic Literature Review
Poster Number: P111
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Mobile Health, Human-computer Interaction, Patient Engagement and Preferences
Primary Track: Foundations
Programmatic Theme: Consumer Health Informatics
Gen AI holds promise for addressing gaps in digital mental health support, yet systematic evaluation frameworks for patient-facing applications remain underdeveloped. This study conducts a systematic literature review to synthesize existing research on Gen AI in mental healthcare. Our findings highlight key evaluation considerations, including content accuracy and clinical appropriateness, empathy, ethics and safety, readability, user trust, and ease of use, providing insights for future framework development.
Speaker:
Polina Durneva, PhD
University of Memphis
Author:
Hedieh Ghorbanie, MS - University of Memphis;
Poster Number: P111
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Mobile Health, Human-computer Interaction, Patient Engagement and Preferences
Primary Track: Foundations
Programmatic Theme: Consumer Health Informatics
Gen AI holds promise for addressing gaps in digital mental health support, yet systematic evaluation frameworks for patient-facing applications remain underdeveloped. This study conducts a systematic literature review to synthesize existing research on Gen AI in mental healthcare. Our findings highlight key evaluation considerations, including content accuracy and clinical appropriateness, empathy, ethics and safety, readability, user trust, and ease of use, providing insights for future framework development.
Speaker:
Polina Durneva, PhD
University of Memphis
Author:
Hedieh Ghorbanie, MS - University of Memphis;
Polina
Durneva,
PhD - University of Memphis
Developing Gen AI-Enabled Chatbot for Self-Managing Academic Stress Among College Students
Poster Number: P112
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Mobile Health, Human-computer Interaction, User-centered Design Methods
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Stress is a prevalent issue among college students, with 45% experiencing higher-than-average stress levels. Generative AI (Gen AI) offers a promising approach for self-managing stress by overcoming the limitations of existing rule-based chatbots. This study develops and evaluates a Gen AI-enabled chatbot trained on credible stress management sources. Through user interviews, expert panel evaluation, and usability testing, the chatbot aims to enhance accessibility and engagement in academic stress management among college students.
Speaker:
Polina Durneva, PhD
University of Memphis
Authors:
Yuan Zhang, PhD - University of Memphis; Sohye Lee, RN, PhD - University of Memphis;
Poster Number: P112
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Mobile Health, Human-computer Interaction, User-centered Design Methods
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Stress is a prevalent issue among college students, with 45% experiencing higher-than-average stress levels. Generative AI (Gen AI) offers a promising approach for self-managing stress by overcoming the limitations of existing rule-based chatbots. This study develops and evaluates a Gen AI-enabled chatbot trained on credible stress management sources. Through user interviews, expert panel evaluation, and usability testing, the chatbot aims to enhance accessibility and engagement in academic stress management among college students.
Speaker:
Polina Durneva, PhD
University of Memphis
Authors:
Yuan Zhang, PhD - University of Memphis; Sohye Lee, RN, PhD - University of Memphis;
Polina
Durneva,
PhD - University of Memphis
Preferences of Transgender Women and Professional Study Recruiters for a Digital Recruitment Tool for Phase I HIV Vaccine Trials
Poster Number: P113
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Mobile Health, Patient Engagement and Preferences, User-centered Design Methods, Nursing Informatics
Working Group: Nursing Informatics Working Group
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
We designed a digital tool, VaxCom, via participatory design (PD) with transgender women (TW) and vaccine recruitment specialists. During sessions, we discussed the app’s appearance, layout, and content. Priority content included potential interactions between vaccines and hormone therapy, gender-inclusive language trainings, general vaccine safety, and the rationale for focusing on TW in vaccine trials recruitment. Both groups preferred a digital tool that is professional, user-friendly, and contains information that is easy to read and understand.
Speaker:
Brittany Curet, BSN, RN
University of Colorado
Authors:
Brittany Curet, BSN, RN - University of Colorado; Christine Tagliaferri Rael, PhD - University of Colorado; Hong Van Tieu, MD, MS - New York Blood Center Enterprises; Jorge Soler, PhD, MPH - New York Blood Center Enterprises; Samantha Stonbraker, PhD, MPH, RN, FAAN - University of Colorado Anschutz Medical Campus;
Poster Number: P113
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Mobile Health, Patient Engagement and Preferences, User-centered Design Methods, Nursing Informatics
Working Group: Nursing Informatics Working Group
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
We designed a digital tool, VaxCom, via participatory design (PD) with transgender women (TW) and vaccine recruitment specialists. During sessions, we discussed the app’s appearance, layout, and content. Priority content included potential interactions between vaccines and hormone therapy, gender-inclusive language trainings, general vaccine safety, and the rationale for focusing on TW in vaccine trials recruitment. Both groups preferred a digital tool that is professional, user-friendly, and contains information that is easy to read and understand.
Speaker:
Brittany Curet, BSN, RN
University of Colorado
Authors:
Brittany Curet, BSN, RN - University of Colorado; Christine Tagliaferri Rael, PhD - University of Colorado; Hong Van Tieu, MD, MS - New York Blood Center Enterprises; Jorge Soler, PhD, MPH - New York Blood Center Enterprises; Samantha Stonbraker, PhD, MPH, RN, FAAN - University of Colorado Anschutz Medical Campus;
Brittany
Curet,
BSN, RN - University of Colorado
Modeling the Temporal and Directional Dynamics of Depression Using Digital Social Traces: A Dual-SEM Approach
Poster Number: P114
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Mobile Health, Personal Health Informatics, Quantitative Methods, Social Media and Connected Health
Primary Track: Foundations
Programmatic Theme: Consumer Health Informatics
Depression poses a global health challenge, and its early detection is critical for effective interventions. Recent studies reveal associations between depression and digital traces of social behavior (e.g., phone calls) but often rely on cross-sectional analyses that overlook temporal and directional dynamics. This study addresses these limitations using a dual-structural equation modeling (SEM) approach, including Latent Growth Curve Modeling (LGCM) and Cross-Lagged Panel Modeling (CLPM), to analyze eight weeks of data from depression survey instruments and phone call logs for 200+ participants. LGCM indicates that initial depressive symptoms influence changes in social engagement (outgoing calls) over time. In addition, CLPM demonstrates a unidirectional link where early depressive symptoms predict subsequent social involvement (fewer missed calls). Guided by sociological theories, such as the Buffering Hypothesis, this study highlights how longitudinal, theory-driven methods can advance healthcare by providing actionable insights for proactive depression detection and overcoming the constraints of cross-sectional approaches.
Speaker:
Eiman Ahmed, PhD in Information Science; BS in Computer Science
Rutgers University
Authors:
Eiman Ahmed, PhD in Information Science; BS in Computer Science - Rutgers University; Andrew Peterson, PhD - Rutgers University; Vivek Singh, PhD - Rutgers University;
Poster Number: P114
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Mobile Health, Personal Health Informatics, Quantitative Methods, Social Media and Connected Health
Primary Track: Foundations
Programmatic Theme: Consumer Health Informatics
Depression poses a global health challenge, and its early detection is critical for effective interventions. Recent studies reveal associations between depression and digital traces of social behavior (e.g., phone calls) but often rely on cross-sectional analyses that overlook temporal and directional dynamics. This study addresses these limitations using a dual-structural equation modeling (SEM) approach, including Latent Growth Curve Modeling (LGCM) and Cross-Lagged Panel Modeling (CLPM), to analyze eight weeks of data from depression survey instruments and phone call logs for 200+ participants. LGCM indicates that initial depressive symptoms influence changes in social engagement (outgoing calls) over time. In addition, CLPM demonstrates a unidirectional link where early depressive symptoms predict subsequent social involvement (fewer missed calls). Guided by sociological theories, such as the Buffering Hypothesis, this study highlights how longitudinal, theory-driven methods can advance healthcare by providing actionable insights for proactive depression detection and overcoming the constraints of cross-sectional approaches.
Speaker:
Eiman Ahmed, PhD in Information Science; BS in Computer Science
Rutgers University
Authors:
Eiman Ahmed, PhD in Information Science; BS in Computer Science - Rutgers University; Andrew Peterson, PhD - Rutgers University; Vivek Singh, PhD - Rutgers University;
Eiman
Ahmed,
PhD in Information Science; BS in Computer Science - Rutgers University
TopicForest: Embedding-driven Hierarchical Clustering and Labeling for Biomedical Literature
Poster Number: P115
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Large Language Models (LLMs), Information Retrieval
Primary Track: Foundations
Programmatic Theme: Translational Bioinformatics
We propose TopicForest, a novel hierarchical topic modeling approach designed to address the challenge of navigating and interpreting large-scale biomedical literature. Leveraging advances in Large Language Models (LLMs), TopicForest first embeds biomedical articles into a high-dimensional semantic space and then constructs a hierarchical topic structure via dendrogram cutting. Unlike existing embedding-based methods (e.g., BERTopic), which yield only flat topic partitions, TopicForest captures multi-scale topic granularity—from broad thematic areas to specialized subtopics—directly from contextual embeddings. Furthermore, TopicForest employs an LLM-based labeling strategy, automatically generating coherent and descriptive labels for topics at each hierarchical level. Comprehensive evaluations demonstrate that TopicForest’s hierarchical clustering achieves comparable or superior performance to flat clustering methods, while the LLM-based labels significantly outperform traditional labeling approaches in both coherence and diversity. Thus, TopicForest effectively facilitates interactive exploration and meaningful interpretation of complex biomedical corpora.
Speaker:
Chia-Hsuan Chang, PhD
Yale BIDS
Authors:
Chia-Hsuan Chang, PhD - Yale BIDS; Bin Choi, B.S. - Yale-NUS College; Brian Ondov, PhD - Yale School of Medicine; Xueqing Peng, PhD - Yale University; Huan He, Ph.D. - Yale University; Hua Xu, Ph.D - Yale University;
Poster Number: P115
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Large Language Models (LLMs), Information Retrieval
Primary Track: Foundations
Programmatic Theme: Translational Bioinformatics
We propose TopicForest, a novel hierarchical topic modeling approach designed to address the challenge of navigating and interpreting large-scale biomedical literature. Leveraging advances in Large Language Models (LLMs), TopicForest first embeds biomedical articles into a high-dimensional semantic space and then constructs a hierarchical topic structure via dendrogram cutting. Unlike existing embedding-based methods (e.g., BERTopic), which yield only flat topic partitions, TopicForest captures multi-scale topic granularity—from broad thematic areas to specialized subtopics—directly from contextual embeddings. Furthermore, TopicForest employs an LLM-based labeling strategy, automatically generating coherent and descriptive labels for topics at each hierarchical level. Comprehensive evaluations demonstrate that TopicForest’s hierarchical clustering achieves comparable or superior performance to flat clustering methods, while the LLM-based labels significantly outperform traditional labeling approaches in both coherence and diversity. Thus, TopicForest effectively facilitates interactive exploration and meaningful interpretation of complex biomedical corpora.
Speaker:
Chia-Hsuan Chang, PhD
Yale BIDS
Authors:
Chia-Hsuan Chang, PhD - Yale BIDS; Bin Choi, B.S. - Yale-NUS College; Brian Ondov, PhD - Yale School of Medicine; Xueqing Peng, PhD - Yale University; Huan He, Ph.D. - Yale University; Hua Xu, Ph.D - Yale University;
Chia-Hsuan
Chang,
PhD - Yale BIDS
Section Header Normalization in Biomedical Articles using Transformers
Poster Number: P116
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Machine Learning, Information Extraction
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Variations in biomedical section headers complicates tasks such as information extraction. Using structured abstract header mappings from NLM, this study fine-tunes a Sentence Transformer with contrastive loss to generate embeddings for label assignment via embedding similarity or neural network classification. On the labeled test set, both methods achieve F1=0.88, while unlabeled data shows more “None” assignments with embedding similarity. Future work will focus on multi-label classification and broader section coverage.
Speaker:
Joseph Menke, MS
University of Illinois, Urbana-Champaign
Authors:
Sylvey Lin, BS - University of Illinois Urbana-Champaign; Joseph Menke, MS - University of Illinois, Urbana-Champaign; Arthur Holt, MBA - University of Illinois at Chicago; Halil Kilicoglu, PhD - University of Illinois Urbana Champaign; Neil Smalheiser, MD-PhD - University of Illinois at Chicago;
Poster Number: P116
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Machine Learning, Information Extraction
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Variations in biomedical section headers complicates tasks such as information extraction. Using structured abstract header mappings from NLM, this study fine-tunes a Sentence Transformer with contrastive loss to generate embeddings for label assignment via embedding similarity or neural network classification. On the labeled test set, both methods achieve F1=0.88, while unlabeled data shows more “None” assignments with embedding similarity. Future work will focus on multi-label classification and broader section coverage.
Speaker:
Joseph Menke, MS
University of Illinois, Urbana-Champaign
Authors:
Sylvey Lin, BS - University of Illinois Urbana-Champaign; Joseph Menke, MS - University of Illinois, Urbana-Champaign; Arthur Holt, MBA - University of Illinois at Chicago; Halil Kilicoglu, PhD - University of Illinois Urbana Champaign; Neil Smalheiser, MD-PhD - University of Illinois at Chicago;
Joseph
Menke,
MS - University of Illinois, Urbana-Champaign
Optimizing Data Quality for Heart Disease Trajectory Analysis
Poster Number: P117
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Machine Learning, Data Mining, Data transformation/ETL, Data Standards, Precision Medicine, Artificial Intelligence, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Despite the rise in machine learning (ML), data automatically extracted from unstructured sources continue to suffer from quality issues exacerbated by the black-box nature of ML, necessitating extensive human oversight. We introduce a methodology that standardizes ML-extracted data for algorithmic analysis and explains the rationale behind the extraction process, enabling context-specific decision-making. Our approach recovered an additional 14% of previously unusable records in the electronic health record (EHR) of the Department of Veterans Affairs (VA).
Speaker:
Eungyoung Han, Biomedical Informatics/PhD
The Department of Veteran Affairs
Authors:
Eungyoung Han, Biomedical Informatics/PhD - The Department of Veteran Affairs; Philip Tsao, PhD - Stanford University / VA Palo Alto Health Care System; Themistocles Assimes, MD, PhD - Stanford University/VA Palo Alto Health Care System; Rodrigo Guarischi-Sousa, PhD - VA Palo Alto Health Care System; Austin Hilliard, PhD - VA Palo Alto Health Care System; Ming Li Chen, MD, MS - Stanford University/VA Palo Alto Health Care System; Marya Husary, MPH - VA Palo Alto Health Care System; Labiba Shere, NA - VA Palo Alto Health Care System;
Poster Number: P117
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Machine Learning, Data Mining, Data transformation/ETL, Data Standards, Precision Medicine, Artificial Intelligence, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Despite the rise in machine learning (ML), data automatically extracted from unstructured sources continue to suffer from quality issues exacerbated by the black-box nature of ML, necessitating extensive human oversight. We introduce a methodology that standardizes ML-extracted data for algorithmic analysis and explains the rationale behind the extraction process, enabling context-specific decision-making. Our approach recovered an additional 14% of previously unusable records in the electronic health record (EHR) of the Department of Veterans Affairs (VA).
Speaker:
Eungyoung Han, Biomedical Informatics/PhD
The Department of Veteran Affairs
Authors:
Eungyoung Han, Biomedical Informatics/PhD - The Department of Veteran Affairs; Philip Tsao, PhD - Stanford University / VA Palo Alto Health Care System; Themistocles Assimes, MD, PhD - Stanford University/VA Palo Alto Health Care System; Rodrigo Guarischi-Sousa, PhD - VA Palo Alto Health Care System; Austin Hilliard, PhD - VA Palo Alto Health Care System; Ming Li Chen, MD, MS - Stanford University/VA Palo Alto Health Care System; Marya Husary, MPH - VA Palo Alto Health Care System; Labiba Shere, NA - VA Palo Alto Health Care System;
Eungyoung
Han,
Biomedical Informatics/PhD - The Department of Veteran Affairs
Aspect-Based Sentiment Analysis of Lupus Pain Discussions on Reddit
Poster Number: P118
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Machine Learning, Large Language Models (LLMs), Social Media and Connected Health
Primary Track: Applications
Aspect-based sentiment analysis (ABSA) was used to examine sentiment surrounding lupus-related pain in online discussions. Reddit posts from r/lupus and r/lupussupport were processed using a custom pain lexicon, and 998 matched sentences were manually labeled as neutral, positive, or negative. The DeBERTa model over 5-fold cross validations outperformed the LLaMA3 70B models, which improved with increased shots and the inclusion of Chain-of-Thought (CoT) prompting.
Speaker:
Tricia Park, BS
Emory University
Authors:
Drew Walker, PhD - Emory University; Aishwarya Alagappan, MBBS - Stanford University; Nathan Le, Computer Science - Stanford University School Of Medicine; Abeed Sarker, PhD - Emory University School of Medicine; Titilola Falasinnu, PhD - Stanford School of Medicine; Selen Bozkurt Watson, PhD, MS - Emory University;
Poster Number: P118
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Machine Learning, Large Language Models (LLMs), Social Media and Connected Health
Primary Track: Applications
Aspect-based sentiment analysis (ABSA) was used to examine sentiment surrounding lupus-related pain in online discussions. Reddit posts from r/lupus and r/lupussupport were processed using a custom pain lexicon, and 998 matched sentences were manually labeled as neutral, positive, or negative. The DeBERTa model over 5-fold cross validations outperformed the LLaMA3 70B models, which improved with increased shots and the inclusion of Chain-of-Thought (CoT) prompting.
Speaker:
Tricia Park, BS
Emory University
Authors:
Drew Walker, PhD - Emory University; Aishwarya Alagappan, MBBS - Stanford University; Nathan Le, Computer Science - Stanford University School Of Medicine; Abeed Sarker, PhD - Emory University School of Medicine; Titilola Falasinnu, PhD - Stanford School of Medicine; Selen Bozkurt Watson, PhD, MS - Emory University;
Tricia
Park,
BS - Emory University
Modeling Explicit Causality in Medical Device Adverse Event Reports
Poster Number: P119
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Machine Learning, Legal, Ethical, Social and Regulatory Issues, Information Extraction, Patient Safety
Primary Track: Foundations
Programmatic Theme: Public Health Informatics
The medical device reporting system is a tool made by the U.S. Food and Drug Administration (FDA) that allows post-market surveillance of medical device performance and potential device-related issues. These reports are publicly stored in the Manufacturer and User Facility Device Experience (MAUDE) database and contain a significant amount of unstructured text. Extracting entities like adverse events and understanding whether they are causally related to the device can aid in device monitoring. In this study, we aim to create an annotation schema for modeling causality of adverse event mentions in medical device reports and establish a performance baseline for this new dataset, using models based on Bidirectional Encoder Representations from Transformers (BERT). Our results suggest that these models have potential in downstream applications for detecting device-related risks.
Speaker:
Tim Miller, PhD
Children's Hospital Boston/Harvard Medical School
Authors:
Tim Miller, PhD - Children's Hospital Boston/Harvard Medical School; Susmitha Wunnava, PhD - Harvard Medical School; Joyce Guo, BA, BS - Boston Children's Hospital; Gaby Dinh, MS - Boston Children's Hospital;
Poster Number: P119
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Machine Learning, Legal, Ethical, Social and Regulatory Issues, Information Extraction, Patient Safety
Primary Track: Foundations
Programmatic Theme: Public Health Informatics
The medical device reporting system is a tool made by the U.S. Food and Drug Administration (FDA) that allows post-market surveillance of medical device performance and potential device-related issues. These reports are publicly stored in the Manufacturer and User Facility Device Experience (MAUDE) database and contain a significant amount of unstructured text. Extracting entities like adverse events and understanding whether they are causally related to the device can aid in device monitoring. In this study, we aim to create an annotation schema for modeling causality of adverse event mentions in medical device reports and establish a performance baseline for this new dataset, using models based on Bidirectional Encoder Representations from Transformers (BERT). Our results suggest that these models have potential in downstream applications for detecting device-related risks.
Speaker:
Tim Miller, PhD
Children's Hospital Boston/Harvard Medical School
Authors:
Tim Miller, PhD - Children's Hospital Boston/Harvard Medical School; Susmitha Wunnava, PhD - Harvard Medical School; Joyce Guo, BA, BS - Boston Children's Hospital; Gaby Dinh, MS - Boston Children's Hospital;
Tim
Miller,
PhD - Children's Hospital Boston/Harvard Medical School
Measuring PHI Memorization in a BERT-based Clinical Text De-identification Model with Token-level Membership Inference Attacks
Poster Number: P120
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Privacy and Security, Evaluation, Large Language Models (LLMs)
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
This study investigates PHI memorization in BERT-based clinical text de-identification models using a token-level membership inference attack classifier. Analyzing 1,234 annotated clinical notes, we developed the detection approach based on target and shadow models using Longformer. Results showed no significant memorization for general tokens or most PHI subgroups (AUC=0.50). However, uncommon "ORGANIZATION" tokens exhibited significant attack performance (AUC=0.72), suggesting context-specific PHI memorization patterns and potential privacy risks.
Speaker:
Dalton Simancek, MSI
University of Michigan
Authors:
V.G.Vinod Vydiswaran, Ph.D. - University of Michigan; Dalton Simancek, MSI - University of Michigan;
Poster Number: P120
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Privacy and Security, Evaluation, Large Language Models (LLMs)
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
This study investigates PHI memorization in BERT-based clinical text de-identification models using a token-level membership inference attack classifier. Analyzing 1,234 annotated clinical notes, we developed the detection approach based on target and shadow models using Longformer. Results showed no significant memorization for general tokens or most PHI subgroups (AUC=0.50). However, uncommon "ORGANIZATION" tokens exhibited significant attack performance (AUC=0.72), suggesting context-specific PHI memorization patterns and potential privacy risks.
Speaker:
Dalton Simancek, MSI
University of Michigan
Authors:
V.G.Vinod Vydiswaran, Ph.D. - University of Michigan; Dalton Simancek, MSI - University of Michigan;
Dalton
Simancek,
MSI - University of Michigan
Advancing Post-Lower Limb Fracture Care: Developing a Clinical Decision Support System in Skilled Nursing Facilities
Poster Number: P121
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Nursing Informatics, Clinical Decision Support, Healthcare Quality, Patient Safety, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Osteoporotic fractures significantly impact health outcomes and secondary fragility fractures are common emphasizing the need for effective post-fracture care in skilled nursing facilities. The OPTIONS (OsteoPorotic fracTure preventION System) study uses a Design Science Framework to develop a clinical decision support (CDS) system through scoping reviews, expert consultations, and iterative evaluations. Results from 82 studies informed CDS features such as tailored assessments, interventions, and provider tools. Development continues with pilot testing to ensure optimal implementation effectiveness.
Speaker:
Veysel Baris, Nurse
Dokuz Eylul University
Authors:
Veysel Baris, Nurse - Dokuz Eylul University; Min-Jeoung Kang, RN, PhD - Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA; Alice Kim, MS, RD - Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA; Kumiko O. Schnock, RN, PhD - Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA; Pamela M Garabedian, BS - Research Information Science & Computing, Mass General Brigham, Boston, Massachusetts, United States; Nancy K Latham, PhD - Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA; Brittin Wagner, BS - PointClickCare Inc., Boston, MA, USA; Elizabeth Dennis, PhD, RD - University of Maryland School of Medicine, Baltimore, MD, USA; Jason Falvey, PhD - University of Maryland School of Medicine, Baltimore, MD, USA; Ling Tang, BS - University of Maryland School of Medicine, Baltimore, MD, USA; Jay Magaziner, PhD - University of Maryland School of Medicine, Baltimore, MD, USA; Richard White, BSc - University of Maryland School of Medicine, Baltimore, MD, USA; Rodrigo Valderrábano, MD - Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA; Xander Lilly, BA - PointClickCare Inc., Boston, MA, USA; Denise Orwig, PhD - University of Maryland School of Medicine, Baltimore, MD, USA; Patricia C. Dykes, RN, PhD - Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA;
Poster Number: P121
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Nursing Informatics, Clinical Decision Support, Healthcare Quality, Patient Safety, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Osteoporotic fractures significantly impact health outcomes and secondary fragility fractures are common emphasizing the need for effective post-fracture care in skilled nursing facilities. The OPTIONS (OsteoPorotic fracTure preventION System) study uses a Design Science Framework to develop a clinical decision support (CDS) system through scoping reviews, expert consultations, and iterative evaluations. Results from 82 studies informed CDS features such as tailored assessments, interventions, and provider tools. Development continues with pilot testing to ensure optimal implementation effectiveness.
Speaker:
Veysel Baris, Nurse
Dokuz Eylul University
Authors:
Veysel Baris, Nurse - Dokuz Eylul University; Min-Jeoung Kang, RN, PhD - Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA; Alice Kim, MS, RD - Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA; Kumiko O. Schnock, RN, PhD - Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA; Pamela M Garabedian, BS - Research Information Science & Computing, Mass General Brigham, Boston, Massachusetts, United States; Nancy K Latham, PhD - Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA; Brittin Wagner, BS - PointClickCare Inc., Boston, MA, USA; Elizabeth Dennis, PhD, RD - University of Maryland School of Medicine, Baltimore, MD, USA; Jason Falvey, PhD - University of Maryland School of Medicine, Baltimore, MD, USA; Ling Tang, BS - University of Maryland School of Medicine, Baltimore, MD, USA; Jay Magaziner, PhD - University of Maryland School of Medicine, Baltimore, MD, USA; Richard White, BSc - University of Maryland School of Medicine, Baltimore, MD, USA; Rodrigo Valderrábano, MD - Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA; Xander Lilly, BA - PointClickCare Inc., Boston, MA, USA; Denise Orwig, PhD - University of Maryland School of Medicine, Baltimore, MD, USA; Patricia C. Dykes, RN, PhD - Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA;
Veysel
Baris,
Nurse - Dokuz Eylul University
Identification of Relationship Between Unprofessional Language in Clinical Notes Among Patients with Limited English Proficiency and Adverse Clinical Outcomes: A Natural Language Processing Approach
Poster Number: P122
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Nursing Informatics, Natural Language Processing, Racial disparities, Health Equity, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics
In patients with limited English proficiency (LEP), clinicians may use unprofessional language that reinforces stereotypes. A rule-based NLP algorithm (F-score: 0.9) was built to detect such language in MIMIC-IV clinical notes. LEP patients had lower overall use, but Black and Hispanic patients had higher rates than white patients. Translator use increased unprofessional language likelihood. While not associated with adverse outcomes, findings highlight documentation disparities and the potential of NLP to promote equity.
Speaker:
Irem Ozbay, MS, RN
Istanbul Sabahattin Zaim University
Authors:
Sang Bin You, MSN, RN - University of Pennsylvania; Mollie Hobensack, PhD, RN - Icahn School of Medicine at Mount Sinai; Brigitte Woo, PhD - University of Pennsylvania; Aviv Landau, PhD - University of Pennsylvania; Jiyoun Song, PhD - University of Pennsylvania School of Nursing;
Poster Number: P122
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Nursing Informatics, Natural Language Processing, Racial disparities, Health Equity, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics
In patients with limited English proficiency (LEP), clinicians may use unprofessional language that reinforces stereotypes. A rule-based NLP algorithm (F-score: 0.9) was built to detect such language in MIMIC-IV clinical notes. LEP patients had lower overall use, but Black and Hispanic patients had higher rates than white patients. Translator use increased unprofessional language likelihood. While not associated with adverse outcomes, findings highlight documentation disparities and the potential of NLP to promote equity.
Speaker:
Irem Ozbay, MS, RN
Istanbul Sabahattin Zaim University
Authors:
Sang Bin You, MSN, RN - University of Pennsylvania; Mollie Hobensack, PhD, RN - Icahn School of Medicine at Mount Sinai; Brigitte Woo, PhD - University of Pennsylvania; Aviv Landau, PhD - University of Pennsylvania; Jiyoun Song, PhD - University of Pennsylvania School of Nursing;
Irem
Ozbay,
MS, RN - Istanbul Sabahattin Zaim University
Barriers & Enablers to Digitalizing Oncology Research Treatment Plans at a Dynamic Clinical Research Enterprise
Poster Number: P123
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Nursing Informatics, Workflow, Patient Safety, Change Management, Governance, Informatics Implementation
Working Group: Nursing Informatics Working Group
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Clinical research digitalization initiatives are crucial to integrating research into clinical practice, streamlining clinician workflows and improving patient safety. However, there are several barriers specific to clinical research that increase the complexity of intake, design, build, validation & amendment processes for oncology treatment plans. At Stanford Healthcare, we leveraged interdisciplinary partnerships to design & implement new end-to-end digitalization workflows and develop the infrastructure necessary to enable a dynamic research environment.
Speaker:
Shannon O'Brien, MSN, FNP-C
Stanford Healthcare
Author:
Pooja Mishra, BS - Stanford Healthcare;
Poster Number: P123
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Nursing Informatics, Workflow, Patient Safety, Change Management, Governance, Informatics Implementation
Working Group: Nursing Informatics Working Group
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Clinical research digitalization initiatives are crucial to integrating research into clinical practice, streamlining clinician workflows and improving patient safety. However, there are several barriers specific to clinical research that increase the complexity of intake, design, build, validation & amendment processes for oncology treatment plans. At Stanford Healthcare, we leveraged interdisciplinary partnerships to design & implement new end-to-end digitalization workflows and develop the infrastructure necessary to enable a dynamic research environment.
Speaker:
Shannon O'Brien, MSN, FNP-C
Stanford Healthcare
Author:
Pooja Mishra, BS - Stanford Healthcare;
Shannon
O'Brien,
MSN, FNP-C - Stanford Healthcare
Leveraging a Sociotechnical Framework to Augment Workflow Analysis: Lessons Learned from a Virtual Nursing Implementation
Poster Number: P124
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Nursing Informatics, Workflow, Telemedicine
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Successful virtual nursing implementation extends beyond workflow optimization to include the social dynamics of remote team integration. Using a sociotechnical framework, we identified onboarding challenges that impacted efficiency, including increased patient throughput times. Targeted interventions, such as incorporating virtual nurses into unit meetings, improved team cohesion and workflow efficiency. These findings highlight the critical role of social integration in optimizing virtual nursing adoption and outcomes.
Speaker:
Brian Douthit, PhD, RN, NI-BC
Department of Biomedical Informatics, Vanderbilt University
Authors:
Brian Douthit, PhD, RN, NI-BC - Department of Biomedical Informatics, Vanderbilt University; Peter Embi, MD - VUMC; Sandy Alexander, MSN, MBA, RN - Vanderbilt University Medical Center; Laurie Novak, PhD, MHSA - Vanderbilt University Medical Center Dept of Biomedical Informatics; Rhonda Day, MSN, RN - Vanderbilt University Medical Center; Catherine Ivory, PhD, NI-BC, NEA-BC, FAAN - Vanderbilt University Medical Center;
Poster Number: P124
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Nursing Informatics, Workflow, Telemedicine
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Successful virtual nursing implementation extends beyond workflow optimization to include the social dynamics of remote team integration. Using a sociotechnical framework, we identified onboarding challenges that impacted efficiency, including increased patient throughput times. Targeted interventions, such as incorporating virtual nurses into unit meetings, improved team cohesion and workflow efficiency. These findings highlight the critical role of social integration in optimizing virtual nursing adoption and outcomes.
Speaker:
Brian Douthit, PhD, RN, NI-BC
Department of Biomedical Informatics, Vanderbilt University
Authors:
Brian Douthit, PhD, RN, NI-BC - Department of Biomedical Informatics, Vanderbilt University; Peter Embi, MD - VUMC; Sandy Alexander, MSN, MBA, RN - Vanderbilt University Medical Center; Laurie Novak, PhD, MHSA - Vanderbilt University Medical Center Dept of Biomedical Informatics; Rhonda Day, MSN, RN - Vanderbilt University Medical Center; Catherine Ivory, PhD, NI-BC, NEA-BC, FAAN - Vanderbilt University Medical Center;
Brian
Douthit,
PhD, RN, NI-BC - Department of Biomedical Informatics, Vanderbilt University
Associations of Symptom-Health Behavior Clusters with Complications in Colorectal Cancer Patients According to Comorbidities
Poster Number: P125
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Patient / Person Generated Health Data (Patient Reported Outcomes), Artificial Intelligence, Machine Learning
Primary Track: Applications
This study aimed to classify colorectal cancer (CRC) patients into clusters based on symptoms and health behaviors to assess their association with postoperative complications. Using the K-modes algorithm, 985 CRC patients were grouped based on symptom burden, physical activity, alcohol consumption, smoking, and comorbidities. In patients with comorbidities, a cluster with both poor symptoms and health behaviors had significantly higher risks of complications. Findings highlight the need for personalized perioperative management to mitigate complication risks.
Speaker:
Jae Hyun Park, M.D.
Seoul National University Hospital
Authors:
Jae Hyun Park, M.D. - Seoul National University Hospital; Jiwon Yu, M.S. - Seoul National University; Hakjun Kim, Ph.D - Hallym University; Nan Song, Ph.D - Chungbuk National University; Min Jung Kim, M.D. - Seoul National University Hospital; Rumi Shin, M.D. - SMG-SNU Boramae Medical Center; AeSun Shin, M.D. - Seoul National University College of Medicine; Seung-Yong Jeong, M.D. - Seoul National University College of Medicine; Young Ho Yun, M.D. - Seoul National University College of Medicine; Jin-ah Sim, Ph.D. - Hallym University; Ji Won Park, M.D., Ph.D. - Seoul National University Hospital;
Poster Number: P125
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Patient / Person Generated Health Data (Patient Reported Outcomes), Artificial Intelligence, Machine Learning
Primary Track: Applications
This study aimed to classify colorectal cancer (CRC) patients into clusters based on symptoms and health behaviors to assess their association with postoperative complications. Using the K-modes algorithm, 985 CRC patients were grouped based on symptom burden, physical activity, alcohol consumption, smoking, and comorbidities. In patients with comorbidities, a cluster with both poor symptoms and health behaviors had significantly higher risks of complications. Findings highlight the need for personalized perioperative management to mitigate complication risks.
Speaker:
Jae Hyun Park, M.D.
Seoul National University Hospital
Authors:
Jae Hyun Park, M.D. - Seoul National University Hospital; Jiwon Yu, M.S. - Seoul National University; Hakjun Kim, Ph.D - Hallym University; Nan Song, Ph.D - Chungbuk National University; Min Jung Kim, M.D. - Seoul National University Hospital; Rumi Shin, M.D. - SMG-SNU Boramae Medical Center; AeSun Shin, M.D. - Seoul National University College of Medicine; Seung-Yong Jeong, M.D. - Seoul National University College of Medicine; Young Ho Yun, M.D. - Seoul National University College of Medicine; Jin-ah Sim, Ph.D. - Hallym University; Ji Won Park, M.D., Ph.D. - Seoul National University Hospital;
Jae Hyun
Park,
M.D. - Seoul National University Hospital
Digital Engagement Patterns in Heart Failure Self-Care: Insights from Activity Tracker and Smart Scale Data
Poster Number: P126
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Patient / Person Generated Health Data (Patient Reported Outcomes), Chronic Care Management, Personal Health Informatics, Diversity, Equity, Inclusion, and Accessibility, Machine Learning, Nursing Informatics, Mobile Health, Health Equity
Working Group: Nursing Informatics Working Group
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Digital health tools can support self-care for adults with heart failure, yet engagement disparities exist. This study identified 4 distinct digital engagement clusters among 81 adults with heart failure using activity trackers and smart scales. Low engagers were younger, more likely to be a minority, and face financial hardship. Findings highlight the need for tailored digital health strategies to increase engagement in underserved populations.
Speaker:
Namuun Clifford, MSN, RN, FNP-C
The University of Texas at Austin
Authors:
Grace Lee, MS - The University of Texas at Austin; Kavita Radhakrishnan, PhD - University of Texas - Austin;
Poster Number: P126
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Patient / Person Generated Health Data (Patient Reported Outcomes), Chronic Care Management, Personal Health Informatics, Diversity, Equity, Inclusion, and Accessibility, Machine Learning, Nursing Informatics, Mobile Health, Health Equity
Working Group: Nursing Informatics Working Group
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Digital health tools can support self-care for adults with heart failure, yet engagement disparities exist. This study identified 4 distinct digital engagement clusters among 81 adults with heart failure using activity trackers and smart scales. Low engagers were younger, more likely to be a minority, and face financial hardship. Findings highlight the need for tailored digital health strategies to increase engagement in underserved populations.
Speaker:
Namuun Clifford, MSN, RN, FNP-C
The University of Texas at Austin
Authors:
Grace Lee, MS - The University of Texas at Austin; Kavita Radhakrishnan, PhD - University of Texas - Austin;
Namuun
Clifford,
MSN, RN, FNP-C - The University of Texas at Austin
Profiling Cancer Patients with Low Health Literacy
Poster Number: P127
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Patient / Person Generated Health Data (Patient Reported Outcomes), Diversity, Equity, Inclusion, and Accessibility, Machine Learning
Primary Track: Foundations
Programmatic Theme: Consumer Health Informatics
This study investigates health literacy (HL) among adult patients at DFCI, highlighting the need for tailored support in ambulatory oncology. Using an EHR-integrated questionnaire, HL was assessed in seven languages, categorizing patients into "Poor" and "Adequate" HL levels. Among 22,101 responses since April 2023, 29% reported poor HL. Significant associations were found between HL levels and patient characteristics such as interpreter need, primary language, age, and education. Outcomes linked to poor HL included worse physical and mental health, and increased health-related social needs.
Principal Component Analysis (PCA) reduced data dimensionality, with the top two components used for clustering. A cluster size of k = 8 achieved the highest silhouette score (0.3844), indicating moderate separation. Two clusters characterized by poor HL were further analyzed. Patients requiring an interpreter, older adults (80+), and those with lower education levels were identified as high-risk groups.
By leveraging patient profiles from clustering, we aim to identify at-risk patients for intervention and support, even without direct HL data. This approach underscores the importance of addressing HL disparities to improve patient outcomes in oncology settings.
Speaker:
Mary Schindler, Bachelors
Dana-Farber Cancer Institute
Authors:
Mary Schindler, Bachelors - Dana-Farber Cancer Institute; Michael Manni, BS - Dana-Farber Cancer Institute; Jocelyn Siegel, NA - Dana-Farber Cancer Institute; Kelsey Li, BS - Dana-Farber Cancer Institute; Gabe Verzino, MPH; Nadine McCleary, MD - Dana-Farber Cancer Institute; Michael Hassett, MD, MPH;
Poster Number: P127
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Patient / Person Generated Health Data (Patient Reported Outcomes), Diversity, Equity, Inclusion, and Accessibility, Machine Learning
Primary Track: Foundations
Programmatic Theme: Consumer Health Informatics
This study investigates health literacy (HL) among adult patients at DFCI, highlighting the need for tailored support in ambulatory oncology. Using an EHR-integrated questionnaire, HL was assessed in seven languages, categorizing patients into "Poor" and "Adequate" HL levels. Among 22,101 responses since April 2023, 29% reported poor HL. Significant associations were found between HL levels and patient characteristics such as interpreter need, primary language, age, and education. Outcomes linked to poor HL included worse physical and mental health, and increased health-related social needs.
Principal Component Analysis (PCA) reduced data dimensionality, with the top two components used for clustering. A cluster size of k = 8 achieved the highest silhouette score (0.3844), indicating moderate separation. Two clusters characterized by poor HL were further analyzed. Patients requiring an interpreter, older adults (80+), and those with lower education levels were identified as high-risk groups.
By leveraging patient profiles from clustering, we aim to identify at-risk patients for intervention and support, even without direct HL data. This approach underscores the importance of addressing HL disparities to improve patient outcomes in oncology settings.
Speaker:
Mary Schindler, Bachelors
Dana-Farber Cancer Institute
Authors:
Mary Schindler, Bachelors - Dana-Farber Cancer Institute; Michael Manni, BS - Dana-Farber Cancer Institute; Jocelyn Siegel, NA - Dana-Farber Cancer Institute; Kelsey Li, BS - Dana-Farber Cancer Institute; Gabe Verzino, MPH; Nadine McCleary, MD - Dana-Farber Cancer Institute; Michael Hassett, MD, MPH;
Mary
Schindler,
Bachelors - Dana-Farber Cancer Institute
Adolescent and Parent Perspectives on Proxy Patient Portals: Awareness, Access, and Educational Strategies
Poster Number: P128
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Pediatrics, Policy, Patient Engagement and Preferences
Primary Track: Policy
Programmatic Theme: Clinical Research Informatics
This study examines older adolescent, young adults, and parent perspectives on patient and proxy portal use, highlighting gaps in awareness, education, and concerns about confidentiality. Survey data from 518 participants reveal low parental understanding of proxy portals and adolescent fears of parental access reducing portal use. Findings emphasize the need for provider-led education and digital strategies to support appropriate portal adoption and foster trust in adolescent confidentiality within digital health ecosystems.
Speaker:
Stephanie Nino de Rivera, BA
Columbia University
Authors:
Stephanie Nino de Rivera, BA - Columbia University; Ruth Masterson Creber, PhD, MSc, RN - Columbia University; Natalie Benda, PhD - Columbia University School of Nursing; Meghan Reading Turchioe, PhD, MPH, RN - Columbia University School of Nursing; Erika Abramson, MD, MSc - Weill Cornell Medicine; Marianne Sharko, MD, MS - Weill Cornell Medicine;
Poster Number: P128
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Pediatrics, Policy, Patient Engagement and Preferences
Primary Track: Policy
Programmatic Theme: Clinical Research Informatics
This study examines older adolescent, young adults, and parent perspectives on patient and proxy portal use, highlighting gaps in awareness, education, and concerns about confidentiality. Survey data from 518 participants reveal low parental understanding of proxy portals and adolescent fears of parental access reducing portal use. Findings emphasize the need for provider-led education and digital strategies to support appropriate portal adoption and foster trust in adolescent confidentiality within digital health ecosystems.
Speaker:
Stephanie Nino de Rivera, BA
Columbia University
Authors:
Stephanie Nino de Rivera, BA - Columbia University; Ruth Masterson Creber, PhD, MSc, RN - Columbia University; Natalie Benda, PhD - Columbia University School of Nursing; Meghan Reading Turchioe, PhD, MPH, RN - Columbia University School of Nursing; Erika Abramson, MD, MSc - Weill Cornell Medicine; Marianne Sharko, MD, MS - Weill Cornell Medicine;
Stephanie
Nino de Rivera,
BA - Columbia University
Process Improvement and Electronic Health Record Enhancement for Adult Admissions to a Pediatric Hospital
Poster Number: P129
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Pediatrics, Workflow, Transitions of Care, Patient Safety, Chronic Care Management, Change Management, Clinical Decision Support
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Patients with childhood-onset conditions are increasingly living into adulthood, and pediatric-based care has shown to improve outcomes for appropriately triaged individuals. Cincinnati Children’s Hospital Medical Center (CCHMC) faces a growing number of adult-aged admissions, due continued subspecialty care or delayed transition to adult medicine. Currently, a paper-based admission process is used, which is inconsistent, inefficient and burdensome. This initiative implements a streamlined electronic admission process using an online form and electronic health record (EHR) enhancements.
Speaker:
Carly Noel, DO, MPH
Cincinnati Children's Hospital
Authors:
Carly Noel, DO, MPH - Cincinnati Children's Hospital; Rachel Peterson, MD - Cincinnati Children's Hospital Medical Center; Melanie Cole, PT, DPT - Cincinnati Children's Hospital Medical Center; Ginger Coffey, DNP, MBA, RN - Cincinnati Children's Hospital Medical Center; Matthew Molloy, MD, MPH - Cincinnati Children's Hospital Medical Center; Nicole Brown, MD - Cincinnati Children's Hospital Medical Center;
Poster Number: P129
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Pediatrics, Workflow, Transitions of Care, Patient Safety, Chronic Care Management, Change Management, Clinical Decision Support
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Patients with childhood-onset conditions are increasingly living into adulthood, and pediatric-based care has shown to improve outcomes for appropriately triaged individuals. Cincinnati Children’s Hospital Medical Center (CCHMC) faces a growing number of adult-aged admissions, due continued subspecialty care or delayed transition to adult medicine. Currently, a paper-based admission process is used, which is inconsistent, inefficient and burdensome. This initiative implements a streamlined electronic admission process using an online form and electronic health record (EHR) enhancements.
Speaker:
Carly Noel, DO, MPH
Cincinnati Children's Hospital
Authors:
Carly Noel, DO, MPH - Cincinnati Children's Hospital; Rachel Peterson, MD - Cincinnati Children's Hospital Medical Center; Melanie Cole, PT, DPT - Cincinnati Children's Hospital Medical Center; Ginger Coffey, DNP, MBA, RN - Cincinnati Children's Hospital Medical Center; Matthew Molloy, MD, MPH - Cincinnati Children's Hospital Medical Center; Nicole Brown, MD - Cincinnati Children's Hospital Medical Center;
Carly
Noel,
DO, MPH - Cincinnati Children's Hospital
Clinician Absenteeism Slows Emergency Department Care Across a Simulated Network
Poster Number: P130
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Policy, Workforce Development, Administrative Systems, Healthcare Quality, Healthcare Economics/Cost of Care
Primary Track: Applications
Programmatic Theme: Public Health Informatics
When chronically strained emergency department (ED) staffing is further stressed by disasters or pandemics, patient care is slowed and compromised. We develop and explore a network simulation model to stress test ED staffing under baseline and strain environmental conditions. Across the simulated network and across facility types, patient length of stay increases with the degree and severity of clinician absenteeism. Our work builds the groundwork to stress test clinician staffing plans and improve ED resilience.
Speaker:
Arwen Declan, MD PhD
Prisma Health
Authors:
Martha Sabogal de la Pava, MS - Clemson University; Aisha Nelson, BS - US Department of Defense; Emily Tucker, PhD;
Poster Number: P130
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Policy, Workforce Development, Administrative Systems, Healthcare Quality, Healthcare Economics/Cost of Care
Primary Track: Applications
Programmatic Theme: Public Health Informatics
When chronically strained emergency department (ED) staffing is further stressed by disasters or pandemics, patient care is slowed and compromised. We develop and explore a network simulation model to stress test ED staffing under baseline and strain environmental conditions. Across the simulated network and across facility types, patient length of stay increases with the degree and severity of clinician absenteeism. Our work builds the groundwork to stress test clinician staffing plans and improve ED resilience.
Speaker:
Arwen Declan, MD PhD
Prisma Health
Authors:
Martha Sabogal de la Pava, MS - Clemson University; Aisha Nelson, BS - US Department of Defense; Emily Tucker, PhD;
Arwen
Declan,
MD PhD - Prisma Health
Using Social Determinants to Predict Unmet Mental Health Needs: An XGBoost Machine Learning Approach
Poster Number: P131
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Population Health, Machine Learning, Racial disparities
Primary Track: Policy
Programmatic Theme: Public Health Informatics
Using XGBoost and nationally representative data from the All of Us Research Program, this study examined how social determinants of health predict unmet mental health need across racial groups. Stress and loneliness emerged as the most influential predictors, highlighting the urgent need for early screening and intervention beyond clinical settings. Findings underscore the importance of age-inclusive, community-based strategies to address these psychosocial risks and reduce barriers to accessing mental healthcare, particularly among socioeconomically vulnerable populations.
Speaker:
Haleigh Kampman, MPH
IUPUI
Author:
Haleigh Kampman, MPH - IUPUI;
Poster Number: P131
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Population Health, Machine Learning, Racial disparities
Primary Track: Policy
Programmatic Theme: Public Health Informatics
Using XGBoost and nationally representative data from the All of Us Research Program, this study examined how social determinants of health predict unmet mental health need across racial groups. Stress and loneliness emerged as the most influential predictors, highlighting the urgent need for early screening and intervention beyond clinical settings. Findings underscore the importance of age-inclusive, community-based strategies to address these psychosocial risks and reduce barriers to accessing mental healthcare, particularly among socioeconomically vulnerable populations.
Speaker:
Haleigh Kampman, MPH
IUPUI
Author:
Haleigh Kampman, MPH - IUPUI;
Haleigh
Kampman,
MPH - IUPUI
Enhancing Primary Care Phenotyping: Automating Phenoflow Integration with EHR Systems
Poster Number: P132
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Population Health, Phenomics and Phenome-wide Association Studies, Interoperability and Health Information Exchange, Information Retrieval, Controlled Terminologies, Ontologies, and Vocabularies
Primary Track: Applications
We enhanced Phenoflow, which powers the HDR UK National Phenotype Library, to automatically translate phenotype definitions into queries for SystmOne, a UK primary care EHR system. Using native SystmOne population health tools, we developed a model and web tool to streamline reuse. Benchmarking showed significant time savings and broader accessibility. This approach enables rapid patient identification and embedded EHR alerts, with potential for expansion to other systems and clinical decision support automation.
Speaker:
Saif Latifi, BSc (Hons) Computer Science wt/ a Year in Industry
King's College London
Authors:
Martin Chapman - King's College London; Iain Marshall, PhD MBChB - King's College London; Vasa Curcin, PhD - King's College London;
Poster Number: P132
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Population Health, Phenomics and Phenome-wide Association Studies, Interoperability and Health Information Exchange, Information Retrieval, Controlled Terminologies, Ontologies, and Vocabularies
Primary Track: Applications
We enhanced Phenoflow, which powers the HDR UK National Phenotype Library, to automatically translate phenotype definitions into queries for SystmOne, a UK primary care EHR system. Using native SystmOne population health tools, we developed a model and web tool to streamline reuse. Benchmarking showed significant time savings and broader accessibility. This approach enables rapid patient identification and embedded EHR alerts, with potential for expansion to other systems and clinical decision support automation.
Speaker:
Saif Latifi, BSc (Hons) Computer Science wt/ a Year in Industry
King's College London
Authors:
Martin Chapman - King's College London; Iain Marshall, PhD MBChB - King's College London; Vasa Curcin, PhD - King's College London;
Saif
Latifi,
BSc (Hons) Computer Science wt/ a Year in Industry - King's College London
Deep phenotyping obesity using EHR data: Promise, Challenges, and Future Directions
Poster Number: P133
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Precision Medicine, Artificial Intelligence, Clinical Decision Support
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Obesity affects approximately 34% of adults and 15–20% of children and adolescents in the U.S, and poses significant economic and psychosocial burdens. Due to the multifaceted nature of obesity, currently patient responses to any single anti-obesity medication (AOM) vary significantly, highlighting the need for developing approaches to obesity deep phenotyping and associated precision medicine. While recent advancement in classical phenotyping-guided pharmacotherapies have shown clinical value, they are less embraced by healthcare providers within the precision medicine framework, primarily due to their operational complexity and lack of granularity. From this perspective, several recent review articles highlighted the importance of obesity deep phenotyping for personalized precision medicine. In view of the established role of electronic health record (EHR) as an important data source for clinical phenotypings, we offer an in-depth analysis of the commonly available data elements from obesity patients prior to pharmacotherapy. We also experimented with a multi-modal longitudinal deep autoencoder to explore the feasibility, data requirements, clustering patterns, and challenges associated with EHR-based obesity deep phenotyping. Our analysis indicates at least nine clusters, among which five have distinct explainable clinical relevance. Further research within larger independent cohorts to validate the reproducibility, uncover more detailed substructures and corresponding treatment response is warranted.
Speaker:
Xiaoyang Ruan, PhD
The University of Texas Health Science Center at Houston
Authors:
Xiaoyang Ruan, PhD - The University of Texas Health Science Center at Houston; Shuyu Lu, PhD student - UT Health Houston; Liwei Wang, MD, PhD - UTHealth; Andrew Wen, MS - University of Texas Health Sciences Center at Houston; Sameer Murali, MD - UT Health Houston; Hongfang Liu, PhD - University of Texas Health Science Center at Houston;
Poster Number: P133
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Precision Medicine, Artificial Intelligence, Clinical Decision Support
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Obesity affects approximately 34% of adults and 15–20% of children and adolescents in the U.S, and poses significant economic and psychosocial burdens. Due to the multifaceted nature of obesity, currently patient responses to any single anti-obesity medication (AOM) vary significantly, highlighting the need for developing approaches to obesity deep phenotyping and associated precision medicine. While recent advancement in classical phenotyping-guided pharmacotherapies have shown clinical value, they are less embraced by healthcare providers within the precision medicine framework, primarily due to their operational complexity and lack of granularity. From this perspective, several recent review articles highlighted the importance of obesity deep phenotyping for personalized precision medicine. In view of the established role of electronic health record (EHR) as an important data source for clinical phenotypings, we offer an in-depth analysis of the commonly available data elements from obesity patients prior to pharmacotherapy. We also experimented with a multi-modal longitudinal deep autoencoder to explore the feasibility, data requirements, clustering patterns, and challenges associated with EHR-based obesity deep phenotyping. Our analysis indicates at least nine clusters, among which five have distinct explainable clinical relevance. Further research within larger independent cohorts to validate the reproducibility, uncover more detailed substructures and corresponding treatment response is warranted.
Speaker:
Xiaoyang Ruan, PhD
The University of Texas Health Science Center at Houston
Authors:
Xiaoyang Ruan, PhD - The University of Texas Health Science Center at Houston; Shuyu Lu, PhD student - UT Health Houston; Liwei Wang, MD, PhD - UTHealth; Andrew Wen, MS - University of Texas Health Sciences Center at Houston; Sameer Murali, MD - UT Health Houston; Hongfang Liu, PhD - University of Texas Health Science Center at Houston;
Xiaoyang
Ruan,
PhD - The University of Texas Health Science Center at Houston
Can ensemble methods improve predictive performance of existing models estimating chronic kidney disease among patients with diabetes?
Poster Number: P134
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Precision Medicine, Clinical Decision Support, Machine Learning, Quantitative Methods
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Clinical prediction models often suffer from poor model transportability and/or subgroup performance resulting from using a single data source. We aimed to determine whether ensemble methods can combine multiple existing models to improve predictive performance when compared to component models. As a case study, we used electronic medical records from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) to test ensemble methods for models estimating the risk of developing chronic kidney disease (CKD) among people with diabetes in a cohort of 37,604 individuals. We considered 13 models identified from prior systematic reviews and combined their unique risk estimates using many strategies (e.g., averaging or mixture-of-experts). We assessed discrimination, precision, recall, calibration, net reclassification index, and integrated discrimination improvement. Ensemble methods performed well, but no better than the best performing component model. This study suggests ensemble methods may not improve predictive performance, though further research should confirm these findings.
Speaker:
Jason Black, MSc
University of Calgary
Authors:
Jason Black, MSc - University of Calgary; David Campbell, MD, PhD - University of Calgary; Paul Ronksley, PhD - University of Calgary; Kerry McBrien, MD, MPH - University of Calgary; Tyler Williamson, PhD - University of Calgary;
Poster Number: P134
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Precision Medicine, Clinical Decision Support, Machine Learning, Quantitative Methods
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Clinical prediction models often suffer from poor model transportability and/or subgroup performance resulting from using a single data source. We aimed to determine whether ensemble methods can combine multiple existing models to improve predictive performance when compared to component models. As a case study, we used electronic medical records from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) to test ensemble methods for models estimating the risk of developing chronic kidney disease (CKD) among people with diabetes in a cohort of 37,604 individuals. We considered 13 models identified from prior systematic reviews and combined their unique risk estimates using many strategies (e.g., averaging or mixture-of-experts). We assessed discrimination, precision, recall, calibration, net reclassification index, and integrated discrimination improvement. Ensemble methods performed well, but no better than the best performing component model. This study suggests ensemble methods may not improve predictive performance, though further research should confirm these findings.
Speaker:
Jason Black, MSc
University of Calgary
Authors:
Jason Black, MSc - University of Calgary; David Campbell, MD, PhD - University of Calgary; Paul Ronksley, PhD - University of Calgary; Kerry McBrien, MD, MPH - University of Calgary; Tyler Williamson, PhD - University of Calgary;
Jason
Black,
MSc - University of Calgary
On Detecting Dynamics of Clinical Biomarkers
Poster Number: P135
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Precision Medicine, Personal Health Informatics, Systems Biology
Primary Track: Foundations
Programmatic Theme: Translational Bioinformatics
We examine better ways of analyzing longitudinal trends in clinical laboratory values from common clinical panels, such as complete blood count, comprehensive metabolic panel, lipid panel, etc. using a novel multivariate distance drift diffusion framework (MD3F). MD3F uses joint representation of longitudinal measurements of clinical values to estimate the amount of dynamical change, or drift. These estimates have the potential to be useful in research and clinical settings.
Speaker:
Alexander Alekseyenko, PhD, FAMIA, FACMI
Medical University of South Carolina
Author:
Alexander Alekseyenko, PhD, FAMIA, FACMI - Medical University of South Carolina;
Poster Number: P135
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Precision Medicine, Personal Health Informatics, Systems Biology
Primary Track: Foundations
Programmatic Theme: Translational Bioinformatics
We examine better ways of analyzing longitudinal trends in clinical laboratory values from common clinical panels, such as complete blood count, comprehensive metabolic panel, lipid panel, etc. using a novel multivariate distance drift diffusion framework (MD3F). MD3F uses joint representation of longitudinal measurements of clinical values to estimate the amount of dynamical change, or drift. These estimates have the potential to be useful in research and clinical settings.
Speaker:
Alexander Alekseyenko, PhD, FAMIA, FACMI
Medical University of South Carolina
Author:
Alexander Alekseyenko, PhD, FAMIA, FACMI - Medical University of South Carolina;
Alexander
Alekseyenko,
PhD, FAMIA, FACMI - Medical University of South Carolina
Evaluating Electronic Reporting of Cases to Public Health: A Review Study
Poster Number: P136
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Public Health, Infectious Diseases and Epidemiology, Evaluation
Working Group: Public Health Informatics Working Group
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Efficient reporting of notifiable infectious diseases is essential for public health surveillance. This critical review evaluates the impact of electronic reporting of cases on timeliness, completeness, and volume to public health utilizing 2010-2024 studies. The Quality Assessment with Diverse Studies (QuADS) tool was used to assess quality, and key findings synthesized to highlight advantages and limitations. Studies (n=8) demonstrated that electronic methods enhanced timeliness of disease reporting by reducing delays with automation, and improved completeness.
Speaker:
Chanhee Kim, PhD
University of Minnesota
Authors:
Chanhee Kim, PhD - University of Minnesota; Lawrence Chen, BS - 2Tufts University School of Medicine; Jacqueline Cassman, MPH - Minnesota Department of Health; Sarah Solarz, MPH - Minnesota Department of Health; Sripriya Rajamani, MBBS, MPH, PhD, FAMIA - University of Minnesota;
Poster Number: P136
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Public Health, Infectious Diseases and Epidemiology, Evaluation
Working Group: Public Health Informatics Working Group
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Efficient reporting of notifiable infectious diseases is essential for public health surveillance. This critical review evaluates the impact of electronic reporting of cases on timeliness, completeness, and volume to public health utilizing 2010-2024 studies. The Quality Assessment with Diverse Studies (QuADS) tool was used to assess quality, and key findings synthesized to highlight advantages and limitations. Studies (n=8) demonstrated that electronic methods enhanced timeliness of disease reporting by reducing delays with automation, and improved completeness.
Speaker:
Chanhee Kim, PhD
University of Minnesota
Authors:
Chanhee Kim, PhD - University of Minnesota; Lawrence Chen, BS - 2Tufts University School of Medicine; Jacqueline Cassman, MPH - Minnesota Department of Health; Sarah Solarz, MPH - Minnesota Department of Health; Sripriya Rajamani, MBBS, MPH, PhD, FAMIA - University of Minnesota;
Chanhee
Kim,
PhD - University of Minnesota
Developing Standardized REDCap Templates and Dashboards to Enhance Measles Outbreak Preparedness and Response
Poster Number: P137
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Public Health, Infectious Diseases and Epidemiology, Data Modernization
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Data collection, monitoring, and analytical tools enable consistent data collection, improve outbreak management efficiency, and support state, tribal, local, and territorial (STLT) health departments in addressing public health emergencies. In this work, we present an iterative approach to designing and operationalizing a standardized REDCap template for measles case investigation and contact monitoring. These free, publicly available tools ensure equitable access to strengthen public health systems.
Speaker:
Elizabeth Campbell, MS, MSPH, PhD
Johns Hopkins Bloomberg School of Public Health
Authors:
Elizabeth Campbell, MS, MSPH, PhD - Johns Hopkins Bloomberg School of Public Health; Haley Farrie, MPH - Johns Hopkins Center for Outbreak Response Innovation; Caitlin Rivers, PhD, MPH - Center for Outbreak Response Innovation; Sutyajeet Soneja, PhD - Center for Outbreak Response Innovation; Eric Toner, MD - Center for Outbreak Response Innovation;
Poster Number: P137
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Public Health, Infectious Diseases and Epidemiology, Data Modernization
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Data collection, monitoring, and analytical tools enable consistent data collection, improve outbreak management efficiency, and support state, tribal, local, and territorial (STLT) health departments in addressing public health emergencies. In this work, we present an iterative approach to designing and operationalizing a standardized REDCap template for measles case investigation and contact monitoring. These free, publicly available tools ensure equitable access to strengthen public health systems.
Speaker:
Elizabeth Campbell, MS, MSPH, PhD
Johns Hopkins Bloomberg School of Public Health
Authors:
Elizabeth Campbell, MS, MSPH, PhD - Johns Hopkins Bloomberg School of Public Health; Haley Farrie, MPH - Johns Hopkins Center for Outbreak Response Innovation; Caitlin Rivers, PhD, MPH - Center for Outbreak Response Innovation; Sutyajeet Soneja, PhD - Center for Outbreak Response Innovation; Eric Toner, MD - Center for Outbreak Response Innovation;
Elizabeth
Campbell,
MS, MSPH, PhD - Johns Hopkins Bloomberg School of Public Health
Stakeholders’ perspectives on the feasibility of implementing strategies to promote digital inclusion within healthcare.
Poster Number: P138
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Qualitative Methods, Public Health, Population Health, Diversity, Equity, Inclusion, and Accessibility
Primary Track: Foundations
Programmatic Theme: Public Health Informatics
Seventeen stakeholders from different professional backgrounds, including commissioners of healthcare services, were interviewed to explore their perspectives of strategies to promote digital inclusion. Four key themes were identified: the feasibility of implementing user-friendly designs in digital health technologies, developing cross-organisational collaborations, delivering resources to the community, and the perceived value of digitally inclusive strategies. We highlight current challenges that stakeholders experienced when implementing strategies and identify areas of further improvement to promote digitally inclusive strategies.
Speaker:
Sarah Wilson, MSc, BSc
Newcastle University
Authors:
Clare Tolley - Newcastle University; Riona McArdle, PhD - Newcastle University; Robert Slight, PhD - The Newcastle upon Tyne Hospitals NHS Foundation Trust; Sarah Slight, PhD - Newcastle University; Sarah Wilson, MSc, BSc - Newcastle University;
Poster Number: P138
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Qualitative Methods, Public Health, Population Health, Diversity, Equity, Inclusion, and Accessibility
Primary Track: Foundations
Programmatic Theme: Public Health Informatics
Seventeen stakeholders from different professional backgrounds, including commissioners of healthcare services, were interviewed to explore their perspectives of strategies to promote digital inclusion. Four key themes were identified: the feasibility of implementing user-friendly designs in digital health technologies, developing cross-organisational collaborations, delivering resources to the community, and the perceived value of digitally inclusive strategies. We highlight current challenges that stakeholders experienced when implementing strategies and identify areas of further improvement to promote digitally inclusive strategies.
Speaker:
Sarah Wilson, MSc, BSc
Newcastle University
Authors:
Clare Tolley - Newcastle University; Riona McArdle, PhD - Newcastle University; Robert Slight, PhD - The Newcastle upon Tyne Hospitals NHS Foundation Trust; Sarah Slight, PhD - Newcastle University; Sarah Wilson, MSc, BSc - Newcastle University;
Sarah
Wilson,
MSc, BSc - Newcastle University
Effect of Increased Survey Incentives on Technology Integration Research
Poster Number: P139
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Quantitative Methods, Nursing Informatics, Interoperability and Health Information Exchange
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
One of the most ethically acceptable and popular recruitment strategies is offering incentives. However, there has been an ongoing debate about the ideal incentive amount. Thus, our study aims to assess the effect of increasing incentives for health information technology maturity survey response among the long-term care workforce. Among those who returned the survey after receiving the incentive increase notice, an increase of up to $75 incentive is effective for a quicker survey response.
Speaker:
Soojeong Han, PhD
Columbia University School of Nursing
Authors:
Soojeong Han, PhD, ARNP, RN - Columbia University School of Nursing; Gregory Alexander, PhD, RN, FAAN, FACMI, FIAHSI - Columbia University School of Nursing; Lusine Poghosyan, PhD, MPH, RN - Columbia University School of Nursing; M. Schrimpf, BA - Columbia University School of Nursing; Sabrina Tasnova, BA - Columbia University School of Nursing; Rachel Rodriguez, BA - Columbia University Mailman School of Public Health; Hana Amer, BA - Columbia University School of Nursing;
Poster Number: P139
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Quantitative Methods, Nursing Informatics, Interoperability and Health Information Exchange
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
One of the most ethically acceptable and popular recruitment strategies is offering incentives. However, there has been an ongoing debate about the ideal incentive amount. Thus, our study aims to assess the effect of increasing incentives for health information technology maturity survey response among the long-term care workforce. Among those who returned the survey after receiving the incentive increase notice, an increase of up to $75 incentive is effective for a quicker survey response.
Speaker:
Soojeong Han, PhD
Columbia University School of Nursing
Authors:
Soojeong Han, PhD, ARNP, RN - Columbia University School of Nursing; Gregory Alexander, PhD, RN, FAAN, FACMI, FIAHSI - Columbia University School of Nursing; Lusine Poghosyan, PhD, MPH, RN - Columbia University School of Nursing; M. Schrimpf, BA - Columbia University School of Nursing; Sabrina Tasnova, BA - Columbia University School of Nursing; Rachel Rodriguez, BA - Columbia University Mailman School of Public Health; Hana Amer, BA - Columbia University School of Nursing;
Soojeong
Han,
PhD - Columbia University School of Nursing
Facebook is a Rich Data Source for Parkinson’s Disease Discourse
Poster Number: P140
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Social Media and Connected Health, Natural Language Processing, Patient / Person Generated Health Data (Patient Reported Outcomes), Machine Learning, Data Mining
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Parkinson’s disease (PD) has a prolonged prodromal phase and progressive symptom burden, yet traditional monitoring relies on post-diagnosis clinical visits. This study evaluates Facebook as a longitudinal data source for PD-related discourse. Among participants with PD, 90% created accounts pre-diagnosis, with an average of 14 years of history. A Naïve Bayes classifier (AUC=0.94) identified PD-related content, including pre-diagnostic posts, demonstrating Facebook’s potential for early detection, disease monitoring, and patient-centered research.
Speaker:
Jeanne Powell, PhD
Emory University
Authors:
Charles Cao, B.S. - Emory University; Kayla Means, B.S. - Emory University; Sahithi Lakamana, Systems Software Engineer - Emory University; Abeed Sarker, PhD - Emory University School of Medicine; J. Lucas Mckay, PhD, MSCR - Emory University;
Poster Number: P140
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Social Media and Connected Health, Natural Language Processing, Patient / Person Generated Health Data (Patient Reported Outcomes), Machine Learning, Data Mining
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Parkinson’s disease (PD) has a prolonged prodromal phase and progressive symptom burden, yet traditional monitoring relies on post-diagnosis clinical visits. This study evaluates Facebook as a longitudinal data source for PD-related discourse. Among participants with PD, 90% created accounts pre-diagnosis, with an average of 14 years of history. A Naïve Bayes classifier (AUC=0.94) identified PD-related content, including pre-diagnostic posts, demonstrating Facebook’s potential for early detection, disease monitoring, and patient-centered research.
Speaker:
Jeanne Powell, PhD
Emory University
Authors:
Charles Cao, B.S. - Emory University; Kayla Means, B.S. - Emory University; Sahithi Lakamana, Systems Software Engineer - Emory University; Abeed Sarker, PhD - Emory University School of Medicine; J. Lucas Mckay, PhD, MSCR - Emory University;
Jeanne
Powell,
PhD - Emory University
Social Media Surveillance of Rave-Related Substance Use and Mental Health Using Natural Language Processing on Reddit Data
Poster Number: P141
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Social Media and Connected Health, Natural Language Processing, Drug Discoveries, Repurposing, and Side-effect, Public Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This research explores trends in substance use and harm reduction strategies within rave culture by applying natural language processing techniques to Reddit posts. It highlights mental health impacts, including post-rave depression. Understanding substance use in this subcultural context is critical, as raves often involve drug environments with limited access to formal surveillance or public health interventions. Findings offer insights into understudied environments and demonstrate the potential of social media data for informing public health support.
Speaker:
Ella Minyoung Lee, Master of Public Health
Emory University
Authors:
Abeed Sarker, PhD - Emory University School of Medicine; Ella Minyoung Lee, Master of Public Health - Emory University; Don Operario, PhD - Emory University Rollins School of Public Health;
Poster Number: P141
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Social Media and Connected Health, Natural Language Processing, Drug Discoveries, Repurposing, and Side-effect, Public Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This research explores trends in substance use and harm reduction strategies within rave culture by applying natural language processing techniques to Reddit posts. It highlights mental health impacts, including post-rave depression. Understanding substance use in this subcultural context is critical, as raves often involve drug environments with limited access to formal surveillance or public health interventions. Findings offer insights into understudied environments and demonstrate the potential of social media data for informing public health support.
Speaker:
Ella Minyoung Lee, Master of Public Health
Emory University
Authors:
Abeed Sarker, PhD - Emory University School of Medicine; Ella Minyoung Lee, Master of Public Health - Emory University; Don Operario, PhD - Emory University Rollins School of Public Health;
Ella Minyoung
Lee,
Master of Public Health - Emory University
Clinical Decision Support to Address Virtual Urgent Care High-Utilizers
Poster Number: P142
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Telemedicine, Clinical Decision Support, Transitions of Care
Primary Track: Applications
Programmatic Theme: Clinical Informatics
A clinical decision support (CDS) intervention identified high utilizers of virtual urgent care and prompted providers to document a follow-up care plan using a SmartPhrase, .VUCHIGHUTILIZER. Among 1069 CDS-eligible patient encounters, SmartPhrase use occurred 27.9% of the time. When the SmartPhrase was used, 30-day visit rates decreased from 2.7 to 1.3 per patient. Bootstrapping analysis estimated a 1.46-visit reduction (95% CI –1.79 to –1.08). Future research can explore patient adherence to follow-up plans.
Speaker:
Jared Silberlust, MD MPH
NYU Langone Health
Authors:
Brian Roberts, DO - NYU Langone Health; Victoria Leybov, MD - NYU Langone Health; Alexander Tran, MD - NYU Langone Health; Nicholas Genes, MD, PhD - NYU Grossman School of Medicine;
Poster Number: P142
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Telemedicine, Clinical Decision Support, Transitions of Care
Primary Track: Applications
Programmatic Theme: Clinical Informatics
A clinical decision support (CDS) intervention identified high utilizers of virtual urgent care and prompted providers to document a follow-up care plan using a SmartPhrase, .VUCHIGHUTILIZER. Among 1069 CDS-eligible patient encounters, SmartPhrase use occurred 27.9% of the time. When the SmartPhrase was used, 30-day visit rates decreased from 2.7 to 1.3 per patient. Bootstrapping analysis estimated a 1.46-visit reduction (95% CI –1.79 to –1.08). Future research can explore patient adherence to follow-up plans.
Speaker:
Jared Silberlust, MD MPH
NYU Langone Health
Authors:
Brian Roberts, DO - NYU Langone Health; Victoria Leybov, MD - NYU Langone Health; Alexander Tran, MD - NYU Langone Health; Nicholas Genes, MD, PhD - NYU Grossman School of Medicine;
Jared
Silberlust,
MD MPH - NYU Langone Health
Can a financial incentive boost eConsult referrals?
Poster Number: P143
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Telemedicine, Healthcare Economics/Cost of Care, Teaching Innovation
Primary Track: Applications
eConsults asynchronously facilitate specialist access but face adoption challenges outside value-based care. We studied the effect of a financial incentive for primary care providers at an urban academic health system for sending eConsults. While 54 of 57 studied providers participated, and eligible eConsults increased by 69% in the first month, the effect was not sustained over the incentive period. Our findings suggest financial incentives alone do not drive eConsult adoption in the long term.
Speaker:
Elisabeth Rosen, MD
Mount Sinai Health System
Authors:
Julian Snyder, BA - Icahn School of Medicine at Mount Sinai; Elisabeth Rosen, MD - Mount Sinai Health System; Ronald Tamler, MD, PhD - Icahn School of Medicine at Mount Sinai;
Poster Number: P143
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Telemedicine, Healthcare Economics/Cost of Care, Teaching Innovation
Primary Track: Applications
eConsults asynchronously facilitate specialist access but face adoption challenges outside value-based care. We studied the effect of a financial incentive for primary care providers at an urban academic health system for sending eConsults. While 54 of 57 studied providers participated, and eligible eConsults increased by 69% in the first month, the effect was not sustained over the incentive period. Our findings suggest financial incentives alone do not drive eConsult adoption in the long term.
Speaker:
Elisabeth Rosen, MD
Mount Sinai Health System
Authors:
Julian Snyder, BA - Icahn School of Medicine at Mount Sinai; Elisabeth Rosen, MD - Mount Sinai Health System; Ronald Tamler, MD, PhD - Icahn School of Medicine at Mount Sinai;
Elisabeth
Rosen,
MD - Mount Sinai Health System
User Perceptions of Hospital IT: Lessons Learned from Focus Group Discussions
Poster Number: P144
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: User-centered Design Methods, Qualitative Methods, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics
To address hospital staff frustration with HIT, we used a mixed-method approach to capture user perception of IT to inform health informaticists on technology innovation following the human-centered design (HCD) methodology. Our research yielded two key recommendations: 1) developing a repository of user personas to address user specific workflow and needs, and 2) balancing onsite and remote support of hospital IT for better workflow integration. This project provided baseline data for a longitudinal evaluation.
Speaker:
Qingyan Ma, PhD
Memorial Sloan Kettering Cancer Center
Authors:
Odamea Akomah, MCH - Memorial Sloan Kettering Cancer Center; Erica Swiatek, MS - Memorial Sloan Kettering Cancer Center; Jackie Denmark, BS - Memorial Sloan Kettering Cancer Center; Peter Stetson, MD - Memorial Sloan Kettering Cancer Center;
Poster Number: P144
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: User-centered Design Methods, Qualitative Methods, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics
To address hospital staff frustration with HIT, we used a mixed-method approach to capture user perception of IT to inform health informaticists on technology innovation following the human-centered design (HCD) methodology. Our research yielded two key recommendations: 1) developing a repository of user personas to address user specific workflow and needs, and 2) balancing onsite and remote support of hospital IT for better workflow integration. This project provided baseline data for a longitudinal evaluation.
Speaker:
Qingyan Ma, PhD
Memorial Sloan Kettering Cancer Center
Authors:
Odamea Akomah, MCH - Memorial Sloan Kettering Cancer Center; Erica Swiatek, MS - Memorial Sloan Kettering Cancer Center; Jackie Denmark, BS - Memorial Sloan Kettering Cancer Center; Peter Stetson, MD - Memorial Sloan Kettering Cancer Center;
Qingyan
Ma,
PhD - Memorial Sloan Kettering Cancer Center
Saving Time and Reducing Burden: An Innovative Nutrition Calculation Tool for Registered Dietitians
Poster Number: P145
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Workflow, Healthcare Quality, Surveys and Needs Analysis
Primary Track: Applications
This project aimed to assess the difference in time and burden of a new standardized nutritional support calculator for hospitalized patients. Traditionally, registered dietitians (RDs) manually calculate the nutrition patients receive, which can be tedious and burdensome. A pre/post survey was completed by RDs regarding the calculation process. The post implementation survey revealed that the nutrition calculation tool reduced calculation time and alleviated RD burden.
Speaker:
Alexandria Weaver, MS, RD
Stanford Health Care
Authors:
Alexandria Weaver, MS, RD - Stanford Health Care; Brittney D. Patterson, MS, RD - Stanford Health Care; Laura Kao, BS, RD - Stanford Health Care; Cassendra A. Munro, PhD - Stanford Health Care;
Poster Number: P145
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Workflow, Healthcare Quality, Surveys and Needs Analysis
Primary Track: Applications
This project aimed to assess the difference in time and burden of a new standardized nutritional support calculator for hospitalized patients. Traditionally, registered dietitians (RDs) manually calculate the nutrition patients receive, which can be tedious and burdensome. A pre/post survey was completed by RDs regarding the calculation process. The post implementation survey revealed that the nutrition calculation tool reduced calculation time and alleviated RD burden.
Speaker:
Alexandria Weaver, MS, RD
Stanford Health Care
Authors:
Alexandria Weaver, MS, RD - Stanford Health Care; Brittney D. Patterson, MS, RD - Stanford Health Care; Laura Kao, BS, RD - Stanford Health Care; Cassendra A. Munro, PhD - Stanford Health Care;
Alexandria
Weaver,
MS, RD - Stanford Health Care
Emergency Clinician Workflow Across Legacy and Upgraded Workstation in the Midst of Technological Change
Poster Number: P146
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Workflow, User-centered Design Methods, Change Management
Primary Track: Applications
Rapid advancements in healthcare technology require corresponding adjustments that impact workflow, staff satisfaction, and patient care. During a recent transition from legacy manual login workstations to newer proximity badge login hypervisor workstations, we found that upgrades induced minor workflow changes that reflect trade-offs between efficiency and security. While technological advancement can enhance specific aspects of performance, it may also introduce new workflow challenges.
Speaker:
Jennifer Rumsey, MD
Prisma Health, USC School of Medicine Greenville
Authors:
Arwen Declan, MD PhD - Prisma Health; Sudeep Hegde, PhD - Clemson University; Nicholas Perkins, DO - Prisma Health; Jeffrey Gerac, MD - Prisma Health; Frederick Lynch, MD - Prisma Health; Ronald Pirrallo, MD - Prisma Health; Sahil Sawant, Master's of Industrial Engineering - Clemson University;
Poster Number: P146
Presentation Time: 05:30 PM - 06:30 PM
Abstract Keywords: Workflow, User-centered Design Methods, Change Management
Primary Track: Applications
Rapid advancements in healthcare technology require corresponding adjustments that impact workflow, staff satisfaction, and patient care. During a recent transition from legacy manual login workstations to newer proximity badge login hypervisor workstations, we found that upgrades induced minor workflow changes that reflect trade-offs between efficiency and security. While technological advancement can enhance specific aspects of performance, it may also introduce new workflow challenges.
Speaker:
Jennifer Rumsey, MD
Prisma Health, USC School of Medicine Greenville
Authors:
Arwen Declan, MD PhD - Prisma Health; Sudeep Hegde, PhD - Clemson University; Nicholas Perkins, DO - Prisma Health; Jeffrey Gerac, MD - Prisma Health; Frederick Lynch, MD - Prisma Health; Ronald Pirrallo, MD - Prisma Health; Sahil Sawant, Master's of Industrial Engineering - Clemson University;
Jennifer
Rumsey,
MD - Prisma Health, USC School of Medicine Greenville
Real-World Clinical Pathway Implementation of Pediatric Guidelines Through Scalable Digital Integration
Category
Poster - Regular
Description
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11/17/2025 06:30 PM (Eastern Time (US & Canada))