RDMA: Agent-Driven Rare Disease Data Discovery

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.

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;

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Integrating AI Asthma Ascertainment Algorithms to Different Institutions: Challenges, Solutions, and Best Practices

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.

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;

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Evaluating AI-Driven Automation for Synthetic FHIR Data Generation

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.

Author:
Ahmad Patooghy, Ph.D. - North Carolina A&T State University;

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A Systematic Approach for Identifying, Prioritizing, and Measuring the Impact of Generative AI Use Cases Using the AMA’s Return on Health Framework

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.

Author:
Dharan Sankar Jaisankar, MS - GenServe.AI;

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Explainable AI for Automatic Detection and Localization of Shoulder Joint in MRIs

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.

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;

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Large Language Model Sycophancy in Simulated Clinical Encounters

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.

Authors:
Yi Wang, B.S. - Stanford University; Carl Preaiksaitis, MD, MEd - Stanford University; Christian Rose, MD - Stanford University, School of Medicine;

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Two-Stage Decoupling Framework for Variable-Length Glaucoma Prognosis

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.

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;

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Artificial Intelligence as Solution for Documentation Burden: A Dutch Nurses’ Viewpoint

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.

Authors:
Sofie Noorland, MSc RN - Netherlands Institute for Health Services Research; Anneke Francke, PhD RN - Netherlands Institute for Health Services Research;

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LLM-as-a-Judge: Automating Clinical Summarization Evaluation Using Large Language Models and a Validated Human Instrument

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.

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;

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Outpatient Parenteral Antimicrobial Therapy: Rapid Prototyping Leads to Faster Creation of a New Healthcare Delivery Model

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.

Authors:
Patrick Wilson, MPH - Mayo Clinic; Douglas Challener, MD - Mayo Clinic; Abinash Virk, MD - Mayo Clinic; Christina Rivera, Pharm.D., R.Ph. - Mayo Clinic;

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LongMedQA: A Multimodal Benchmark for Long Context Medical Question Answering with Electronic Health Records

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.

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;

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A PubMed Parsing Agent for Detecting University of Kentucky Studies that "Meet People Where They Are"

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.

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;

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Leveraging a Large Language Model to Optimize HIV Care Engagement

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.

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;

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Predicting Treatment Failure with Sodium-Glucose Cotransporter-2 Inhibitors (SGLT2i) in Patients with Type 2 Diabetes: A Novel Artificial Intelligence and Machine Learning Approach

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.

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;

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Chronological Age Prediction based on DNA Methylation and Clinical Features

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.

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;

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The molecular mechanisms of comorbidities between Cardiovascular Disease and Alzheimer’s Disease

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.

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;

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Thinking, Fast and Slow: DualReasoning Enhances Clinical Knowledge Extraction from Large Language Models

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.

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;

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Using Mendelian randomization and large-scale proteomics data to identify drug targets for lipid-lowering

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.

Authors:
Sergio Mundo, PhD - Vanderbilt University Medical Center; QiPing Feng, PhD - Vanderbilt University Medical Center; Wei-Qi Wei, MD, PhD - Vanderbilt University Medical Center;

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The Disease-Diagnosis Semantic Gap: Implications for Causal Inference

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.

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;

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Beyond Documentation: Assessment of Large Language Model Performance in Summarizing Cancer Wound Care Nursing Notes

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.

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;

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Genetic Risk Factors for Kidney Disease: A Statistical Approach to Early Detection

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.

Authors:
Akshitha G, High School Student - Rock Hill High School; Christie Martin, PhD, MPH, RN-BC, LHIT-HP - University of Minnesota School of Nursing;

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Theory-Informed Co-Design for a Pragmatic Remote Monitoring Intervention: Insights from Remote Monitoring for Equity in Advancing the Control of Hypertension (REACH) Study

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.

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;

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Variation in Healthcare System Performance in the Leapfrog Group’s 2024 CPOE Evaluation Tool

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.

Authors:
David Classen, MD - University of Utah School of Medicine; Melissa Danforth, BA - The Leapfrog Group; David Bates, MD - Mass General Brigham; Harvard University;

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Harnessing the Power of Non-Interruptive Clinical Decision Support to Prevent Safety Events

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

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;

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Implementing Clinical Decision Support to Facilitate Opt-out HIV Testing for Admitted Adolescent Patients

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.

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;

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Standardizing Enterprise Reporting to Improve Health System Performance

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.

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;

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Needs-Based Clinical Decision Support for Difficult Airway Situational Awareness in Inpatient Providers

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.

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;

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Implementing an Electronic Decision Aid to Reduce Bleeding Risk with Anticoagulants: A Qualitative Study with Patients and Clinicians

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.

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;

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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

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.

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;

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Testing a Design Framework for Presenting Machine Learning Predictions to Patients

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.

Authors:
Afra Shamnath, Master of Public Health - Columbia University; Meghan Reading Turchioe, PhD, MPH, RN - Columbia University School of Nursing;

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Leveraging Information Quality, Social Influence and Technical Support to Enhance Health Information Exchange Adoption in Emergency Care Settings

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.

Authors:
Brian Dixon, MPA, PhD - Regenstrief Institute; Saurabh Rahurkar, DrPH, DDS - The Ohio State University College of Medicine;

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Ensemble-Based Narrative Analysis for ADHD Diagnosis: An Integration of LLaMA3, RoBERTa and SVM Classifiers

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.

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;

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Using Large Language Models for Predicting ALSFRS-R Sub-item Scores From Clinician-Annotated ALS Patient Data

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.

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;

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Evaluation of an Ensemble Machine Learning Model for Predicting Continuous Renal Replacement Therapy Initiation in a Pediatric and Cardiac ICU

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.

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;

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Real-World Clinical Pathway Implementation of Pediatric Guidelines Through Scalable Digital Integration

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.

Author:
Zhen Lin, PhD RN - Johns Hopkins All Children's Hospital;

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Preventing Surgical Site Infections

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.

Authors:
Michelle Dempsey, BS CPBI - Geisinger; Biplab S Bhattacharya, PhD - Geisinger Health System; David Vawdrey, PhD - Geisinger; Eric Reich, MSHI - Geisinger;

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Enhancing Missense Variant Classification in Predicted Intrinsically Disordered Regions

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.

Authors:
Steven Hart, PhD - Mayo Clinic; Rohan Gnanaolivu, Masters - Mayo Clinic;

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LLM-Based Medical Code Mapping: From Custom to Standard Codes

Presentation Time: 05:30 PM - 06:30 PM

Abstract Keywords: Controlled Terminologies, Ontologies, and Vocabularies, Interoperability and Health Information Exchange, Knowledge Representation and Information Modeling, Information Retrieval, Large Language Models (LLMs)
Primary Track: Foundations

Despite the widespread availability of standardized medical coding systems like LOINC and ICD, healthcare facilities rely on custom code systems packed with local abbreviations and inconsistent terminology. This fragmentation severely impedes data exchange and necessitates labor-intensive mapping to standard code systems. Our LLM-based framework enhances semantics of abbreviated codes, transforms them into vector embeddings, and aligns them with standard codes through contrastive learning, achieving 74.55% top-1 accuracy and substantially outperforming baselines.

Authors:
Yan Zhuang, Ph.D. - Indiana University; Hao Liu, PhD - Montclair State University;

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An AI Semantic Mapping Tool in a Knowledge Management System

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.

Authors:
Marcelo Fiszman, MD, Ph.D. FACMI, DipIBLM - Semedy Inc; Kai Großjohann, MSc - Semedy, Inc.; Roberto Rocha, MD, PhD, FACMI - Semedy, Inc.;

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Machine learning composite variable for pre-operative volume status in orthotopic liver transplant predicts postoperative length of stay

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.

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;

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A Bayesian-Driven Two-Compartment Model Algorithm for Vancomycin AUC24 Calculation

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.

Authors:
Dhruvin Patel, Computer Science - Loyola University Chicago; Nazanin Azarvash, BS - Loyola University Chicago; Samie Tootooni, PhD - Loyola University Chicago;

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Data-Driven Depression Subtypes and Heart Failure Risk Analysis

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.

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;

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Disaster-Ready Hemodialysis Information Sharing: What to Share and How to Communicate

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.

Authors:
Keisuke Ido, MPH - Tohoku University Hospital; Tadashi Ishii, MD, PhD - Tohoku University Hospital; Mariko Miyazaki, MD, PhD - Tohoku University Graduate School of Medicine;

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Research in the Age of Artificial Intelligence: Integrating Participant Perspectives with Current Research Innovations to Foster Patient-Centric Practices

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.

Authors:
Sarah Eslami, Bachelors of Science - Columbia University; Stephanie Nino de Rivera, BA - Columbia University; Ruth Masterson Creber, PhD, MSc, RN - Columbia University;

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Visualising Health Data in FHIR Format for Decision Making: Developing A Dashboard Application for Disaster Management in Indonesia

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.

Authors:
Hiro Putra Faisal, MD - Tohoku University; Masaharu Nakayama, MD, PhD, FAMIA - Tohoku University;

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From Text to Codes: Optimizing ICD-10 Coding Workflow with GPT-driven Retrieval-Augmented Generation

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.

Authors:
Zhen Hou, MS - Indiana University; Yan Zhuang, Ph.D. - Indiana University;

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Data Without Boundaries: Resilient EMR Systems for Low-Resource Clinics

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.

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;

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A Simple Pipeline to Facilitate Toxicology Report Data Transformation

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.

Authors:
Donielle Beiler, B.S. - Geisinger; Vanessa Troiani, PhD - Geisinger;

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Enabling Scalable Predictive Monitoring and Alarm Analytics via a Real-Time Platform for Processing Continuous Cardiorespiratory Monitoring Data

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.

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;

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The Impact of Graphical Complexity in VR on Task Performance: Insights from a Preliminary Study

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.

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;

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FairFML: A Unified Approach to Algorithmic Fair Federated Learning with Applications to Reducing Gender Disparities in Cardiac Arrest Outcomes

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.

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;

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Tackling Gender and Ethnicity Bias in Privacy-Preserving Clinical Decision- Making Across Hospital Collaborations

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.

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;

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An approach to explore an open-source language models’ representation of interprofessionality.

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.

Authors:
Frédéric Baroz, MD, MSc, PhDc - McGill University; Katherine Blondon, MD, PhD - Hopitaux Universitaires Geneve;

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Automatically Generating Patient-specific Clinical Questions from Patient Messages to Relieve Clinician Burden

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.

Author:
Dina Demner-Fushman, MD - National Library of Medicine;

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Mapping Health Informatics Education and Industry Job Competencies in the United States: Literature Trends and A Case Study

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.

Authors:
Fei Yu, PhD - UNC at Chapel Hill; Jenny Kaselak, MEd - UNC at Chapel Hill; David Gotz - University of North Carolina;

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GenAI vs. Human Reviewers in Classifying Alzheimer’s Disease and Related Dementia (ADRD) Literature: A Comparative Study

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.

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;

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Exploring Institutional Policies for Generative AI Integration in Health Informatics Education: A Structural and Thematic Analysis

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.

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;

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Environmental Impact on Transitioning to a Lower Carbon Footprint Asthma Inhalers

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.

Author:
Geoffrey Chupp, MD - Pulmonary, Critical Care and Sleep Medicine, Yale University;

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Trends in adopting the national environmental exposure assessment note template in the Veterans Affairs Health System

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.

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;

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Shannon Entropy-Driven Analysis of Healthcare Cost Efficiency

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.

Authors:
Emma Chua, _ - Pasadena City College; Boyu Peng, Master - Maze Engineers; Shuhan He, MD - Mass General Hospital/Harvard Medical School;

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Oral Health Care Spending Over the Life Span in Commercial and Medicaid Insured Populations

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.

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;

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Decoding the Gut-Brain Axis: Genomic Intersections Between Microbial Dysbiosis and Dementia

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.

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;

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Standardizing Chaperone Documentation in the Emergency Department: A Cross-Campus Epic Optimization Initiative

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.

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;

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Expanding AI Explainability with Algorithmovigilance Checkpoints for Trust and Safety (ACTS)

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.

Author:
Megan Salwei, PhD - Vanderbilt University Medical Center;

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Emergency Clinician Workflow Across Legacy and Upgraded Workstation in the Midst of Technological Change

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.

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;

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Graph-to-Text Generation for Adverse Drug Events: Constructing and Leveraging a Knowledge Graph from FAERS

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.

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;

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Extracting cognitive domains of Parkinson’s disease patients using large language models

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.

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;

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Extracting Real-World Trends of Cannabis Use from Clinical Documentation

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.

Authors:
Titilola Falasinnu, PhD - Stanford School of Medicine; Selen Bozkurt Watson, PhD, MS - Emory University; Tricia Park, BS - Emory University;

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Pets4Health: A Literature-Based Tool to Identify Therapeutic Animals

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.

Authors:
Neil Sarkar, PhD, MLIS - Rhode Island Quality Institute & Brown University; Elizabeth Chen, PhD - Brown University;

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Quantifying Benefits of a Combined REDCap-on-FHIR + EMERSE Semi-Automated Chart Abstraction Procedure for Clinical Studies

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.

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;

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A multi-agentic large language model-based framework for cancer registry automation

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.

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;

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Infographics to Augment Clinical HIV-related Communication Increase Health Knowledge among Latinos with Low Health Literacy

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.

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;

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Designing a Visual Analytics Dashboard to Streamline Collaborative Common Data Element Mapping

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.

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;

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Developing a Visual Analytics Tool to Explore the Readability Levels of Health-related Documents

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.

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;

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Dashboard Development for Tracking Performance of Lung Cancer Screening

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.

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;

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CLEAR-MATCH: Patient-centered, AI-powered clinical trial matching

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.

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;

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Privacy-Preserving Synthetic Data Generation with Large Language Models for Enhanced Discourse-Based ADRD Assessment

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.

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;

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AI-driven Demographic Bias Detection in Cardiovascular Medical Education

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.

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;

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Enhancing Type 2 Diabetes Mellitus Phenotyping with GPT-4o via Multi-Hyperparameter Optimized Retrieval-Augmented Generation (RAG) Settings

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.

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;

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Is ChatGPT-4o Racially Biased for Medical Diagnosis?

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.

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;

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Mixed-Methods Evaluation of User Engagement with an LLM-Based Reproductive Health Chatbot

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.

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;

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Exploring the Feasibility of Large Language Models for Social Care-Health Care Integration: Perspectives from the Safety Net

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.

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;

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Improving Synthetic Training Data through Social Exemplars for Dialogue Systems Identifying Unmet Health-Related Social Needs

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.

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;

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Agent-Based LLMs for Reliable Clinical Knowledge Graph Extraction

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.

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;

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Integrating Symbolic Regression for Generalizable and Interpretable Machine Learning in Cardiovascular Risk Prediction

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.

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;

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Data Representation Influences Groupwise Predictive Efficacy of Cardiovascular Outcomes in Obstructive Sleep Apnea Patientsn for Sleep Apnea Patients

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.

Authors:
Raghu Upender, MD, MBA - Vanderbilt University Medical Center; Bradley Malin, PhD - Vanderbilt University Medical Center;

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Applying Multi-Objective Optimization and Feature Selection to Predicting Patient Deterioration

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.

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;

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Predicting Negative Patient Experience in the Hospital Setting

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.

Author:
Brendan Broderick, M.S. - Mayo Clinic;

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AI-Computer Based Antimicrobial Monitoring (AI-CBAM) to Address Inefficiencies in an Antimicrobial Stewardship Program at Mayo Clinic Rochester

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.

Author:
Ricardo Rojas, BA - Mayo Clinic;

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Generative AI in Mental Healthcare: A Systematic Literature Review

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.

Author:
Hedieh Ghorbanie, MS - University of Memphis;

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Developing Gen AI-Enabled Chatbot for Self-Managing Academic Stress Among College Students

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.

Authors:
Yuan Zhang, PhD - University of Memphis; Sohye Lee, RN, PhD - University of Memphis;

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Preferences of Transgender Women and Professional Study Recruiters for a Digital Recruitment Tool for Phase I HIV Vaccine Trials

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.

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;

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Section Header Normalization in Biomedical Articles using Transformers

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.

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;

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Optimizing Data Quality for Heart Disease Trajectory Analysis

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).

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;

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Aspect-Based Sentiment Analysis of Lupus Pain Discussions on Reddit

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.

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;

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Measuring PHI Memorization in a BERT-based Clinical Text De-identification Model with Token-level Membership Inference Attacks

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.

Authors:
V.G.Vinod Vydiswaran, Ph.D. - University of Michigan; Dalton Simancek, MSI - University of Michigan;

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Barriers & Enablers to Digitalizing Oncology Research Treatment Plans at a Dynamic Clinical Research Enterprise

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.

Author:
Pooja Mishra, BS - Stanford Healthcare;

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Leveraging a Sociotechnical Framework to Augment Workflow Analysis: Lessons Learned from a Virtual Nursing Implementation

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.

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;

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Associations of Symptom-Health Behavior Clusters with Complications in Colorectal Cancer Patients According to Comorbidities

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.

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;

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Digital Engagement Patterns in Heart Failure Self-Care: Insights from Activity Tracker and Smart Scale Data

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.

Authors:
Grace Lee, MS - The University of Texas at Austin; Kavita Radhakrishnan, PhD - University of Texas - Austin;

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Profiling Cancer Patients with Low Health Literacy

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.

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;

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Adolescent and Parent Perspectives on Proxy Patient Portals: Awareness, Access, and Educational Strategies

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.

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;

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Process Improvement and Electronic Health Record Enhancement for Adult Admissions to a Pediatric Hospital

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.

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;

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Clinician Absenteeism Slows Emergency Department Care Across a Simulated Network

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.

Authors:
Martha Sabogal de la Pava, MS - Clemson University; Aisha Nelson, BS - US Department of Defense; Emily Tucker, PhD;

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Using Social Determinants to Predict Unmet Mental Health Needs: An XGBoost Machine Learning Approach

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.

Author:
Haleigh Kampman, MPH - IUPUI;

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Enhancing Primary Care Phenotyping: Automating Phenoflow Integration with EHR Systems

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.

Authors:
Martin Chapman - King's College London; Iain Marshall, PhD MBChB - King's College London; Vasa Curcin, PhD - King's College London;

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Evaluating Electronic Reporting of Cases to Public Health: A Review Study

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.

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;

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Developing Standardized REDCap Templates and Dashboards to Enhance Measles Outbreak Preparedness and Response

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.

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;

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Stakeholders’ perspectives on the feasibility of implementing strategies to promote digital inclusion within healthcare.

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.

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;

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Effect of Increased Survey Incentives on Technology Integration Research

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.

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;

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Facebook is a Rich Data Source for Parkinson’s Disease Discourse

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.

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;

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Social Media Surveillance of Rave-Related Substance Use and Mental Health Using Natural Language Processing on Reddit Data

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.

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;

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Clinical Decision Support to Address Virtual Urgent Care High-Utilizers

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.

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;

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Can a financial incentive boost eConsult referrals?

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.

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;

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User Perceptions of Hospital IT: Lessons Learned from Focus Group Discussions

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.

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;

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Saving Time and Reducing Burden: An Innovative Nutrition Calculation Tool for Registered Dietitians

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.

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;

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Emergency Clinician Workflow Across Legacy and Upgraded Workstation in the Midst of Technological Change

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.

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;

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