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11/18/2025 |
5:00 PM – 6:30 PM |
Room 13
Poster Session 3
Presentation Type: Posters
The Informatics of Value-Base Care: Missing Opportunities
Poster Number: P01
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Workforce Development, Teaching Innovation, Curriculum Development
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
Value-based care (VBC) is transforming the healthcare landscape by shifting the focus from the volume of services provided to the quality of care delivered. In this model, providers are rewarded based on patient health outcomes, with the aim of encouraging a more patient-centered approach. Achieving VBC, however, requires robust health information technology (IT) systems to collect and analyze data that can both define and assess the success of these value-based efforts. The purpose of this poster is to thoroughly explore the various components of VBC using a modern informatics framework, specifically through a structured Informatics Stack. By examining each layer of the stack, we will identify opportunities where informatics research can move VBC research forward.
Speaker:
Chen Dun, MHS
Johns Hopkins University
Authors:
Caitlin Hicks, MD, MS - Johns Hopkins University; Harold Lehmann, MD, PhD - Johns Hopkins University;
Poster Number: P01
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Workforce Development, Teaching Innovation, Curriculum Development
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
Value-based care (VBC) is transforming the healthcare landscape by shifting the focus from the volume of services provided to the quality of care delivered. In this model, providers are rewarded based on patient health outcomes, with the aim of encouraging a more patient-centered approach. Achieving VBC, however, requires robust health information technology (IT) systems to collect and analyze data that can both define and assess the success of these value-based efforts. The purpose of this poster is to thoroughly explore the various components of VBC using a modern informatics framework, specifically through a structured Informatics Stack. By examining each layer of the stack, we will identify opportunities where informatics research can move VBC research forward.
Speaker:
Chen Dun, MHS
Johns Hopkins University
Authors:
Caitlin Hicks, MD, MS - Johns Hopkins University; Harold Lehmann, MD, PhD - Johns Hopkins University;
Chen
Dun,
MHS - Johns Hopkins University
Development of Billing for Point-of-Care Ultrasound (POCUS) in the Pediatric Emergency Department
Poster Number: P02
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Workflow, Imaging Informatics, Pediatrics
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Point-of-care ultrasound (POCUS) is an essential tool for pediatric emergency medicine (PEM) physicians. We conducted walkthroughs of POCUS users workflow from completion of POCUS study to documentation which informed our development of structured forms for documentation. We then analyzed the rate of images with standardized documentation per month using a statistical process control chart. We also collected the number of charges for each different POCUS Current Procedural Terminology (CPT) codes after starting this program.
Speaker:
Carrie Ng, MD
Emory University School of Medicine, Children's Healthcare of Atlanta
Authors:
Carrie Ng, MD - Emory University School of Medicine/Children's Healthcare of Atlanta; Tal Berkowitz, MD, MPH - Emory University School of Medicine / Children's Healthcare of Atlanta; Lekha Shah, MD - Emory University School of Medicine / Children's Healthcare of Atlanta; Peter Gutierrez, MD - Emory University School of Medicine / Children's Healthcare of Atlanta; Joshua Pulinat, MD - Emory University School of Medicine / Children's Healthcare of Atlanta; Naveen Muthu, MD - Children's Healthcare of Atlanta;
Poster Number: P02
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Workflow, Imaging Informatics, Pediatrics
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Point-of-care ultrasound (POCUS) is an essential tool for pediatric emergency medicine (PEM) physicians. We conducted walkthroughs of POCUS users workflow from completion of POCUS study to documentation which informed our development of structured forms for documentation. We then analyzed the rate of images with standardized documentation per month using a statistical process control chart. We also collected the number of charges for each different POCUS Current Procedural Terminology (CPT) codes after starting this program.
Speaker:
Carrie Ng, MD
Emory University School of Medicine, Children's Healthcare of Atlanta
Authors:
Carrie Ng, MD - Emory University School of Medicine/Children's Healthcare of Atlanta; Tal Berkowitz, MD, MPH - Emory University School of Medicine / Children's Healthcare of Atlanta; Lekha Shah, MD - Emory University School of Medicine / Children's Healthcare of Atlanta; Peter Gutierrez, MD - Emory University School of Medicine / Children's Healthcare of Atlanta; Joshua Pulinat, MD - Emory University School of Medicine / Children's Healthcare of Atlanta; Naveen Muthu, MD - Children's Healthcare of Atlanta;
Carrie
Ng,
MD - Emory University School of Medicine, Children's Healthcare of Atlanta
Integrating a Breast Cancer Risk Model into a Clinical Workflow
Poster Number: P03
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: User-centered Design Methods, Workflow, Machine Learning, Personal Health Informatics, Cancer Prevention
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Breast cancer is the most commonly diagnosed cancer in women. A Breast Cancer Risk Estimator (BCRE) machine learning model was developed to enable outreach to high-risk patients that need mammogram screening. In this poster, we present a clinical workflow with a user-centered design that enables immediate patient outreach, minimizes unnecessary touchpoints and integrates with the EHR as well as current breast cancer protocols. Program evaluation includes monitoring patient outreach status and outcomes.
Speaker:
Rebecca Maff, MS
Geisinger
Authors:
Tamanna Tabassum Munia, PhD - Geisinger; Elliot Mitchell, PhD - Geisinger; Brandy Feltman, LPN - Geisinger; Timothy Murphy, MD - Geisinger; Robin Skrine, MD - Geisinger; Rosemary Leeming, MD - Geisinger; David Vawdrey, PhD - Geisinger; Aalpen Patel - Geisinger Health System; Keith Boell, DO - Geisinger; Biplab S Bhattacharya, PhD - Geisinger Health System;
Poster Number: P03
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: User-centered Design Methods, Workflow, Machine Learning, Personal Health Informatics, Cancer Prevention
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Breast cancer is the most commonly diagnosed cancer in women. A Breast Cancer Risk Estimator (BCRE) machine learning model was developed to enable outreach to high-risk patients that need mammogram screening. In this poster, we present a clinical workflow with a user-centered design that enables immediate patient outreach, minimizes unnecessary touchpoints and integrates with the EHR as well as current breast cancer protocols. Program evaluation includes monitoring patient outreach status and outcomes.
Speaker:
Rebecca Maff, MS
Geisinger
Authors:
Tamanna Tabassum Munia, PhD - Geisinger; Elliot Mitchell, PhD - Geisinger; Brandy Feltman, LPN - Geisinger; Timothy Murphy, MD - Geisinger; Robin Skrine, MD - Geisinger; Rosemary Leeming, MD - Geisinger; David Vawdrey, PhD - Geisinger; Aalpen Patel - Geisinger Health System; Keith Boell, DO - Geisinger; Biplab S Bhattacharya, PhD - Geisinger Health System;
Rebecca
Maff,
MS - Geisinger
Automated Assessments of Clinical Encounters: Provider Perspectives
Poster Number: P04
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Usability, User-centered Design Methods, Artificial Intelligence, Information Visualization, Health Equity, Diversity, Equity, Inclusion, and Accessibility, Human-computer Interaction
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Care quality and outcomes are influenced by the quality of patient-provider communication; ConverSense was developed to automatically assess and provide clinicians with feedback on interpersonal communication in patient-provider interactions, including implicit bias. Through interviews with 24 primary care providers who received feedback from ConverSense from simulated clinical encounters, we identified 6 themes about the usefulness of ConverSense. Findings provide guidance for improved usability and transparency of automated assessment and feedback on patient-provider communication.
Speaker:
Claire Lai, B.S.
Univeristy of Washington
Authors:
Claire Lai, B.S. - Univeristy of Washington; Reggie Casanova-Perez - Department of Biomedical Informatics and Medical Education, University of Washington; Raina Langevin, PhD - University of Washington; Anuujin Tsedenbal, B.S. - UC San Diego; Simran Saxena, MS - Microsoft; Wanda Pratt, PhD, FACMI - University of Washington; Janice Sabin - University of Washington; Brian Wood, M.D. - University of Washington; Nadir Weibel, PhD - UC San Diego; Andrea Hartzler, PhD - University of Washington;
Poster Number: P04
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Usability, User-centered Design Methods, Artificial Intelligence, Information Visualization, Health Equity, Diversity, Equity, Inclusion, and Accessibility, Human-computer Interaction
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Care quality and outcomes are influenced by the quality of patient-provider communication; ConverSense was developed to automatically assess and provide clinicians with feedback on interpersonal communication in patient-provider interactions, including implicit bias. Through interviews with 24 primary care providers who received feedback from ConverSense from simulated clinical encounters, we identified 6 themes about the usefulness of ConverSense. Findings provide guidance for improved usability and transparency of automated assessment and feedback on patient-provider communication.
Speaker:
Claire Lai, B.S.
Univeristy of Washington
Authors:
Claire Lai, B.S. - Univeristy of Washington; Reggie Casanova-Perez - Department of Biomedical Informatics and Medical Education, University of Washington; Raina Langevin, PhD - University of Washington; Anuujin Tsedenbal, B.S. - UC San Diego; Simran Saxena, MS - Microsoft; Wanda Pratt, PhD, FACMI - University of Washington; Janice Sabin - University of Washington; Brian Wood, M.D. - University of Washington; Nadir Weibel, PhD - UC San Diego; Andrea Hartzler, PhD - University of Washington;
Claire
Lai,
B.S. - Univeristy of Washington
Motivational Messaging in a 1-week Trial of a Digital Health Program for Older Family Care Partners of Persons with Heart Failure
Poster Number: P05
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Usability, Patient Engagement and Preferences, Qualitative Methods, Nursing Informatics
Working Group: Nursing Informatics Working Group
Primary Track: Applications
We assessed the usability and acceptability of motivational messaging in a digital health physical activity program for older family caregivers of persons with heart failure, through a 1-week, two-phase field test. Phase 1 participants found automated messages insufficiently motivating, leading to refinements in Phase 2, where a health coach manually delivered messages. Participants preferred personalized, encouraging messages with a human touch. Findings highlight the need for tailored, human-centered digital health interventions to enhance older caregiver engagement.
Speaker:
Dawon Baik, PhD
University of Colorado
Authors:
Catherine Jankowski, PhD - University of Colorado; Blaine Reeder, PhD - University of Missouri - Columbia; Larry Allen, MD, MHS - University of Colorado; Heather Coats, PhD - University of Colorado; Colleen McIlvennan, PhD, DNP, ANP - University of Colorado; Paul Cook, PhD - University of Colorado;
Poster Number: P05
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Usability, Patient Engagement and Preferences, Qualitative Methods, Nursing Informatics
Working Group: Nursing Informatics Working Group
Primary Track: Applications
We assessed the usability and acceptability of motivational messaging in a digital health physical activity program for older family caregivers of persons with heart failure, through a 1-week, two-phase field test. Phase 1 participants found automated messages insufficiently motivating, leading to refinements in Phase 2, where a health coach manually delivered messages. Participants preferred personalized, encouraging messages with a human touch. Findings highlight the need for tailored, human-centered digital health interventions to enhance older caregiver engagement.
Speaker:
Dawon Baik, PhD
University of Colorado
Authors:
Catherine Jankowski, PhD - University of Colorado; Blaine Reeder, PhD - University of Missouri - Columbia; Larry Allen, MD, MHS - University of Colorado; Heather Coats, PhD - University of Colorado; Colleen McIlvennan, PhD, DNP, ANP - University of Colorado; Paul Cook, PhD - University of Colorado;
Dawon
Baik,
PhD - University of Colorado
Development and Validation of a Machine Learning Model to Predict Postoperative Morbidity and Mortality of Patients with COVID-19 who Underwent Major Surgery
Poster Number: P06
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Surgery, Machine Learning, Clinical Decision Support, Patient Safety, Healthcare Quality
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Active coronavirus disease (COVID-19) increases the risk of morbidity and mortality after surgery. This investigation utilized machine learning algorithms to predict postoperative outcomes based on preoperative features.
Multiple supervised machine learning algorithms were trained on 153 features for 10,613 patients with COVID-19 who underwent major surgery in 2022, and validated on 5,269 patients from 2021. Patients with COVID-19 in 2021 and 2022 had a significantly higher 30-day composite mortality or major morbidity rate of 17.0% and 11.8%, respectively, compared to 3.2% and 3.0% of patients without (p<0.001). LightGBM was the best performing algorithm, with an area under the receiver operating characteristic curve and F1 score of 0.865 and 0.512 when training on 2022 data, and 0.898 and 0.617 when validating on 2021 data, respectively. This approach accurately identified patients with COVID-19 who experienced postoperative morbidity and mortality, and may provide a more objective approach to risk stratification.
Speaker:
Sean McDermott, MD
University of Pittsburgh
Authors:
Sean McDermott, MD - University of Pittsburgh; Leming Zhou, PhD, FAMIA - University of Pittsburgh; A. Murat Kaynar, MD, MPH - University of Pittsburgh Medical Center;
Poster Number: P06
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Surgery, Machine Learning, Clinical Decision Support, Patient Safety, Healthcare Quality
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Active coronavirus disease (COVID-19) increases the risk of morbidity and mortality after surgery. This investigation utilized machine learning algorithms to predict postoperative outcomes based on preoperative features.
Multiple supervised machine learning algorithms were trained on 153 features for 10,613 patients with COVID-19 who underwent major surgery in 2022, and validated on 5,269 patients from 2021. Patients with COVID-19 in 2021 and 2022 had a significantly higher 30-day composite mortality or major morbidity rate of 17.0% and 11.8%, respectively, compared to 3.2% and 3.0% of patients without (p<0.001). LightGBM was the best performing algorithm, with an area under the receiver operating characteristic curve and F1 score of 0.865 and 0.512 when training on 2022 data, and 0.898 and 0.617 when validating on 2021 data, respectively. This approach accurately identified patients with COVID-19 who experienced postoperative morbidity and mortality, and may provide a more objective approach to risk stratification.
Speaker:
Sean McDermott, MD
University of Pittsburgh
Authors:
Sean McDermott, MD - University of Pittsburgh; Leming Zhou, PhD, FAMIA - University of Pittsburgh; A. Murat Kaynar, MD, MPH - University of Pittsburgh Medical Center;
Sean
McDermott,
MD - University of Pittsburgh
Insights on Social Isolation from a National Health Information Survey
Poster Number: P07
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Social Media and Connected Health, Population Health, Surveys and Needs Analysis
Working Group: Public Health Informatics Working Group
Primary Track: Applications
Programmatic Theme: Public Health Informatics
US Surgeon General’s Advisory in 2023 highlighted the loneliness epidemic and importance of social connections on health. The Health Information National Trends Survey data from 2022 was analyzed and PROMIS isolation scores (>=60 high isolation) were used to characterize association with 3 variables. 20% with low social isolation scores had never used social media. There was a significant association between perceived quality of care and trust in the healthcare system with PROMIS social isolation scores.
Speaker:
Madhur Thakur, MS Health Informatics
University of Minnesota
Authors:
Madhur Thakur, MS Health Informatics - University of Minnesota; Michelle Mathiason, MS - University of Minnesota; Chanhee Kim, PhD - University of Minnesota; Robin Austin, PhD, DNP, DC, RN, NI-BC, FAMIA, FAAN - University of Minnesota, School of Nursing; David Pieczkiewicz, PhD - University of Minnesota; Sripriya Rajamani, MBBS, MPH, PhD, FAMIA - University of Minnesota;
Poster Number: P07
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Social Media and Connected Health, Population Health, Surveys and Needs Analysis
Working Group: Public Health Informatics Working Group
Primary Track: Applications
Programmatic Theme: Public Health Informatics
US Surgeon General’s Advisory in 2023 highlighted the loneliness epidemic and importance of social connections on health. The Health Information National Trends Survey data from 2022 was analyzed and PROMIS isolation scores (>=60 high isolation) were used to characterize association with 3 variables. 20% with low social isolation scores had never used social media. There was a significant association between perceived quality of care and trust in the healthcare system with PROMIS social isolation scores.
Speaker:
Madhur Thakur, MS Health Informatics
University of Minnesota
Authors:
Madhur Thakur, MS Health Informatics - University of Minnesota; Michelle Mathiason, MS - University of Minnesota; Chanhee Kim, PhD - University of Minnesota; Robin Austin, PhD, DNP, DC, RN, NI-BC, FAMIA, FAAN - University of Minnesota, School of Nursing; David Pieczkiewicz, PhD - University of Minnesota; Sripriya Rajamani, MBBS, MPH, PhD, FAMIA - University of Minnesota;
Madhur
Thakur,
MS Health Informatics - University of Minnesota
Mapping the Use of Social Media Analytics in Firearm Injury Exposure Research: A Scoping Review
Poster Number: P08
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Social Media and Connected Health, Natural Language Processing, Public Health, Data Mining, Evaluation, Quantitative Methods
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This scoping review examined how social media analytics have been used to study firearm injury exposure. Sixteen English-language studies published between 2014–2023, primarily studying Twitter/X, used applied methods such as natural language processing and sentiment analysis. Findings highlight limited platform diversity, methodological inconsistencies, and suggest that social media analytics offer untapped potential to strengthen public health surveillance of firearm injury exposure.
Speaker:
Michele Flynch, PHD, RN
Columbia University School of Nursing
Authors:
Lu He, PhD - University of Wisconsin-Milwaukee; Morgan Badurak, MA - Manship School of Mass Communication, Louisiana State University, Baton Rouge, LA; Suzanne Bakken, RN, PhD - Columbia University School of Nursing;
Poster Number: P08
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Social Media and Connected Health, Natural Language Processing, Public Health, Data Mining, Evaluation, Quantitative Methods
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This scoping review examined how social media analytics have been used to study firearm injury exposure. Sixteen English-language studies published between 2014–2023, primarily studying Twitter/X, used applied methods such as natural language processing and sentiment analysis. Findings highlight limited platform diversity, methodological inconsistencies, and suggest that social media analytics offer untapped potential to strengthen public health surveillance of firearm injury exposure.
Speaker:
Michele Flynch, PHD, RN
Columbia University School of Nursing
Authors:
Lu He, PhD - University of Wisconsin-Milwaukee; Morgan Badurak, MA - Manship School of Mass Communication, Louisiana State University, Baton Rouge, LA; Suzanne Bakken, RN, PhD - Columbia University School of Nursing;
Michele
Flynch,
PHD, RN - Columbia University School of Nursing
Computational approaches to understanding and countering substance use stigma in online communities
Poster Number: P09
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Social Media and Connected Health, Natural Language Processing, Large Language Models (LLMs), Public Health, Health Equity
Primary Track: Foundations
Programmatic Theme: Public Health Informatics
This presentation introduces two computational approaches to understand and address substance use stigma in online spaces. We present a system that identifies and transforms stigmatizing language while preserving authorial voice and reveal three distinct phenotypes of stigma expressions identified through analyzing over 1.2 million Reddit posts. These methods offer insights into experiences of people who use drugs while providing practical tools for mitigating harmful language in communication.
Speaker:
Layla Bouzoubaa, MSPH
Drexel University
Authors:
Elham Aghakhani, MS - Drexel University; Rezvaneh Rezapour;
Poster Number: P09
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Social Media and Connected Health, Natural Language Processing, Large Language Models (LLMs), Public Health, Health Equity
Primary Track: Foundations
Programmatic Theme: Public Health Informatics
This presentation introduces two computational approaches to understand and address substance use stigma in online spaces. We present a system that identifies and transforms stigmatizing language while preserving authorial voice and reveal three distinct phenotypes of stigma expressions identified through analyzing over 1.2 million Reddit posts. These methods offer insights into experiences of people who use drugs while providing practical tools for mitigating harmful language in communication.
Speaker:
Layla Bouzoubaa, MSPH
Drexel University
Authors:
Elham Aghakhani, MS - Drexel University; Rezvaneh Rezapour;
Layla
Bouzoubaa,
MSPH - Drexel University
Correcting Informative Censoring in Treatment Effect Estimation with Large Scale, Real-World Health Data
Poster Number: P10
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Real-World Evidence Generation, Quantitative Methods, Causal Inference
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Observational studies play a crucial role in evaluating the effectiveness and safety of medical treatments, but are prone to various biases which can distort causal effect estimates. While Inverse Probability of Treatment Weighting (IPTW) adjusts for confounding, Inverse Probability of Censoring Weighting (IPCW) accounts for bias introduced by informative censoring. In this study, we systematically evaluate the performance of IPTW, IPCW, and their combination using both simulation experiments and real-world health data.
Speaker:
Hsin Yi Chen, B.S.
Columbia University
Authors:
Hsin Yi Chen, B.S. - Columbia University; Ruochong Fan, MA - Washington University in St. Louis; Linying Zhang, PhD - Washington University in St. Louis; George Hripcsak, MD - Columbia University Irving Medical Center;
Poster Number: P10
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Real-World Evidence Generation, Quantitative Methods, Causal Inference
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Observational studies play a crucial role in evaluating the effectiveness and safety of medical treatments, but are prone to various biases which can distort causal effect estimates. While Inverse Probability of Treatment Weighting (IPTW) adjusts for confounding, Inverse Probability of Censoring Weighting (IPCW) accounts for bias introduced by informative censoring. In this study, we systematically evaluate the performance of IPTW, IPCW, and their combination using both simulation experiments and real-world health data.
Speaker:
Hsin Yi Chen, B.S.
Columbia University
Authors:
Hsin Yi Chen, B.S. - Columbia University; Ruochong Fan, MA - Washington University in St. Louis; Linying Zhang, PhD - Washington University in St. Louis; George Hripcsak, MD - Columbia University Irving Medical Center;
Hsin Yi
Chen,
B.S. - Columbia University
Evaluating Federated Learning’s Application in Epidemiology: Identifying Risk Factors for Treatment-Resistant Depression
Poster Number: P11
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Real-World Evidence Generation, Privacy and Security, Causal Inference, Fairness and elimination of bias, Machine Learning
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Federated learning (FL) approach enables analysis across data sources while preserving privacy. Despite its increasing adoption in machine learning tasks, FL’s application in statistical inferences remain limited. We piloted federated algorithms for statistical modeling to identify risk factors for treatment-resistant depression and compare the federated results from those obtained on centralized data. Our study piloted and evaluated a federated analysis framework for common healthcare statistical models, presenting a repeatable process for federated epidemiological analyses. Our results showed high concordance between the federated and centralized analyses in different scenarios, demonstrating the reliability of federated approaches.
Speaker:
Echo Wang, DrPH
Merck
Authors:
Yaki Sacuik, MPH - Maccabi Healthcare Services; Hongtao Zhang, PhD - Merck; Lawrence Gould, PhD - Merck; Moshe Hoshen, PhD - Maccabi Healthcare Services; Alan Apter, MD - Maccabi Healthcare Services; Tal Patalon, MD MBA - Maccabi Healthcare Services; Mehmet Burcu, PhD, MS, FISPE - Merck; Julia Dibello, PhD - Merck; Sam Joo, PhD - Merck; Sivan Gazit, MD, MA - Maccabi Healthcare Services;
Poster Number: P11
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Real-World Evidence Generation, Privacy and Security, Causal Inference, Fairness and elimination of bias, Machine Learning
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Federated learning (FL) approach enables analysis across data sources while preserving privacy. Despite its increasing adoption in machine learning tasks, FL’s application in statistical inferences remain limited. We piloted federated algorithms for statistical modeling to identify risk factors for treatment-resistant depression and compare the federated results from those obtained on centralized data. Our study piloted and evaluated a federated analysis framework for common healthcare statistical models, presenting a repeatable process for federated epidemiological analyses. Our results showed high concordance between the federated and centralized analyses in different scenarios, demonstrating the reliability of federated approaches.
Speaker:
Echo Wang, DrPH
Merck
Authors:
Yaki Sacuik, MPH - Maccabi Healthcare Services; Hongtao Zhang, PhD - Merck; Lawrence Gould, PhD - Merck; Moshe Hoshen, PhD - Maccabi Healthcare Services; Alan Apter, MD - Maccabi Healthcare Services; Tal Patalon, MD MBA - Maccabi Healthcare Services; Mehmet Burcu, PhD, MS, FISPE - Merck; Julia Dibello, PhD - Merck; Sam Joo, PhD - Merck; Sivan Gazit, MD, MA - Maccabi Healthcare Services;
Echo
Wang,
DrPH - Merck
A Multilevel Source-of-Bias Model in Real-World Healthcare Evidence: A Scoping Review
Poster Number: P12
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Real-World Evidence Generation, Clinical Decision Support, Informatics Implementation
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
To organize knowledge about biases in the development of evidence based on Real World Data (EHR in particular), we developed a multi-level framework spanning healthcare, data management, and research, analyzing 153 bias sources. Biases at patient, provider, IT, informatics, and analytics levels were found to interconnect, transfer, and amplify across healthcare systems. This framework can provide a foundation for developing level-specific debiasing models to enhance reliability real-world evidence derived from individual centers and collaborative networks.
Speaker:
Haeun Lee, MS
Johns Hopkins University
Authors:
Harold Lehmann, MD, PhD - Johns Hopkins University; Paul Nagy, PhD - Johns Hopkins University SOM ICTR;
Poster Number: P12
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Real-World Evidence Generation, Clinical Decision Support, Informatics Implementation
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
To organize knowledge about biases in the development of evidence based on Real World Data (EHR in particular), we developed a multi-level framework spanning healthcare, data management, and research, analyzing 153 bias sources. Biases at patient, provider, IT, informatics, and analytics levels were found to interconnect, transfer, and amplify across healthcare systems. This framework can provide a foundation for developing level-specific debiasing models to enhance reliability real-world evidence derived from individual centers and collaborative networks.
Speaker:
Haeun Lee, MS
Johns Hopkins University
Authors:
Harold Lehmann, MD, PhD - Johns Hopkins University; Paul Nagy, PhD - Johns Hopkins University SOM ICTR;
Haeun
Lee,
MS - Johns Hopkins University
Uncovering Bias in Real-World Data: Challenges in Inpatient Mortality
Poster Number: P13
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Real-World Evidence Generation, Causal Inference, Data Standards
Primary Track: Foundations
This study highlights the challenges of using electronic health record (EHR) data for real-world evidence generation, focusing on potential discrepancies of inpatient mortality. By emulating a clinical trial on dexamethasone, researchers showed that the inconsistencies in mortality documentation, specifically when coinciding with discharge, can influence outcome interpretations. Despite modest overall effects, the findings stress the need for standardized data practices to ensure accurate, bias-free analyses in observational research.
Speaker:
Wesley Anderson, PhD.
Critical Path Institute
Authors:
Will Roddy, BS - Critical Path Institute; Smith Heavner, PhD, RN - Critical Path Institute;
Poster Number: P13
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Real-World Evidence Generation, Causal Inference, Data Standards
Primary Track: Foundations
This study highlights the challenges of using electronic health record (EHR) data for real-world evidence generation, focusing on potential discrepancies of inpatient mortality. By emulating a clinical trial on dexamethasone, researchers showed that the inconsistencies in mortality documentation, specifically when coinciding with discharge, can influence outcome interpretations. Despite modest overall effects, the findings stress the need for standardized data practices to ensure accurate, bias-free analyses in observational research.
Speaker:
Wesley Anderson, PhD.
Critical Path Institute
Authors:
Will Roddy, BS - Critical Path Institute; Smith Heavner, PhD, RN - Critical Path Institute;
Wesley
Anderson,
PhD. - Critical Path Institute
ECG-Enabled Clustering and Dynamic Time Warping for Cardiovascular and Cardiorenal Patient Stratification
Poster Number: P14
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Quantitative Methods, Precision Medicine, Machine Learning
Primary Track: Applications
Dynamic Time Warping (DTW) was used to cluster ECG features from FinnGen data, identifying HFpEF (heart failure with preserved ejection fraction) phenogroups and CKD (chronic kidney disease) risk. DTW outperformed clustering methods using all or key extracted ECG features (PR interval, QRS duration), yielding more enriched clusters. These findings highlight DTW's potential for improving ECG-based clustering, patient stratification, and risk assessment in cardiovascular and kidney health.
Speaker:
Sally Zhao, MS
Pfizer
Authors:
Sabrina Hsueh, PhD - Pfizer; Zhan Ye, PhD - Pfizer Inc; Bhavna Adhin, MS - Pfizer; Sudeshna Fisch, PhD - Pfizer;
Poster Number: P14
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Quantitative Methods, Precision Medicine, Machine Learning
Primary Track: Applications
Dynamic Time Warping (DTW) was used to cluster ECG features from FinnGen data, identifying HFpEF (heart failure with preserved ejection fraction) phenogroups and CKD (chronic kidney disease) risk. DTW outperformed clustering methods using all or key extracted ECG features (PR interval, QRS duration), yielding more enriched clusters. These findings highlight DTW's potential for improving ECG-based clustering, patient stratification, and risk assessment in cardiovascular and kidney health.
Speaker:
Sally Zhao, MS
Pfizer
Authors:
Sabrina Hsueh, PhD - Pfizer; Zhan Ye, PhD - Pfizer Inc; Bhavna Adhin, MS - Pfizer; Sudeshna Fisch, PhD - Pfizer;
Sally
Zhao,
MS - Pfizer
SOFA Produces Uncertain Mortality Predictions: Reconsidering the Use of SOFA in Triage
Poster Number: P15
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Quantitative Methods, Machine Learning, Critical Care
Primary Track: Applications
Programmatic Theme: Clinical Informatics
In the event of a disaster, many US institutions have guidelines that triage patients for scarce resources. Many of these guidelines rank-order patients based on thresholds in models that predict mortality, such as the Sequential Organ Failure Assessment (SOFA) score. Using conformal prediction, we show that the SOFA score is a highly uncertain predictor of mortality at the patient level, calling into question the use of threshold-based models such as SOFA in crisis triage.
Speaker:
Katarina Pejcinovic, M.S.
Oregon Health & Science University
Authors:
Brenna Park-Egan, M.S. - Oregon Health & Science University; Patrick Lyons, MD, MSc - Oregon Health & Science University;
Poster Number: P15
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Quantitative Methods, Machine Learning, Critical Care
Primary Track: Applications
Programmatic Theme: Clinical Informatics
In the event of a disaster, many US institutions have guidelines that triage patients for scarce resources. Many of these guidelines rank-order patients based on thresholds in models that predict mortality, such as the Sequential Organ Failure Assessment (SOFA) score. Using conformal prediction, we show that the SOFA score is a highly uncertain predictor of mortality at the patient level, calling into question the use of threshold-based models such as SOFA in crisis triage.
Speaker:
Katarina Pejcinovic, M.S.
Oregon Health & Science University
Authors:
Brenna Park-Egan, M.S. - Oregon Health & Science University; Patrick Lyons, MD, MSc - Oregon Health & Science University;
Katarina
Pejcinovic,
M.S. - Oregon Health & Science University
Patient Perspectives on Clinical Artificial Intelligence
Poster Number: P16
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Qualitative Methods, Workflow, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Informatics
In health care, artificial intelligence (AI) resources are often developed and implemented without patient or community input. Community engagement approaches need to adapt to the complex topic of AI. We urgently need to engage patients in the processes of developing and implementing AI tools in health care. Using semi-structured interviews and co-design workshops, this study aims to understand participants’ information needs and communication preferences related to AI in general and AI use in their care.
Speaker:
Joyce Harris, MA
Vanderbilt University Medical Center
Authors:
Shilo Anders, PhD - University of Kansas Medical Center; Laurie Novak, PhD, MHSA - Vanderbilt University Medical Center Dept of Biomedical Informatics;
Poster Number: P16
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Qualitative Methods, Workflow, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Informatics
In health care, artificial intelligence (AI) resources are often developed and implemented without patient or community input. Community engagement approaches need to adapt to the complex topic of AI. We urgently need to engage patients in the processes of developing and implementing AI tools in health care. Using semi-structured interviews and co-design workshops, this study aims to understand participants’ information needs and communication preferences related to AI in general and AI use in their care.
Speaker:
Joyce Harris, MA
Vanderbilt University Medical Center
Authors:
Shilo Anders, PhD - University of Kansas Medical Center; Laurie Novak, PhD, MHSA - Vanderbilt University Medical Center Dept of Biomedical Informatics;
Joyce
Harris,
MA - Vanderbilt University Medical Center
Methodological reporting of mixed-methods studies of health informatics interventions: A systematic review
Poster Number: P17
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Qualitative Methods, Quantitative Methods, Real-World Evidence Generation, Education and Training
Primary Track: Applications
This systematic review aims to characterize the types of informatics studies that have utilized a mixed-methods study for evaluating clinician-facing informatics interventions. We found that 24% of studies that reported using mixed-methods design did not show integration of their quantitative and qualitative data. Some studies also incorrectly described or omitted to report their core mixed-methods design. These findings suggest a need for educating the informatics research community about implementing best practices for mixed methods research.
Speaker:
Oliver Nguyen, MSHI
University of Wisconsin at Madison
Authors:
Arsalan Ahmad, MS - University of Wisconsin at Madison; Michelle Doering, MLIS - Washington University in St. Louis; Joanna Abraham, PhD, FACMI, FAMIA - Department of Anesthesiology and Institute for Informatics, Data Science and Biostatistics at Washington University in St. Louis, School of Medicine;
Poster Number: P17
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Qualitative Methods, Quantitative Methods, Real-World Evidence Generation, Education and Training
Primary Track: Applications
This systematic review aims to characterize the types of informatics studies that have utilized a mixed-methods study for evaluating clinician-facing informatics interventions. We found that 24% of studies that reported using mixed-methods design did not show integration of their quantitative and qualitative data. Some studies also incorrectly described or omitted to report their core mixed-methods design. These findings suggest a need for educating the informatics research community about implementing best practices for mixed methods research.
Speaker:
Oliver Nguyen, MSHI
University of Wisconsin at Madison
Authors:
Arsalan Ahmad, MS - University of Wisconsin at Madison; Michelle Doering, MLIS - Washington University in St. Louis; Joanna Abraham, PhD, FACMI, FAMIA - Department of Anesthesiology and Institute for Informatics, Data Science and Biostatistics at Washington University in St. Louis, School of Medicine;
Oliver
Nguyen,
MSHI - University of Wisconsin at Madison
Claims-Based Identification of Primary Care Providers in Georgia
Poster Number: P18
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Public Health, Real-World Evidence Generation, Delivering Health Information and Knowledge to the Public
Primary Track: Foundations
Programmatic Theme: Public Health Informatics
Claims data is a valuable data source to understand provider availability. Claims and eligibility information from the Georgia APCD were analyzed to determine annual primary care provider (PCP) counts and ratios by provider type, day-of-week, and county for 2018-2023. PCP-to-members ratios were lower for urban counties; both urban and rural PCP ratios declined from 2018-2022 and increased in 2023. Additionally, PCP availability was lower on weekends and nurse practitioners expanded in the PCP workforce.
Speaker:
Caleb Hightower, MSPH
Georgia Tech Research Institute
Authors:
Caleb Hightower, MSPH - Georgia Tech Research Institute; Charity Hilton - Georgia Tech Research Institute; Micaela Siraj; Richard Starr, BS - Georgia Institute of Technology; Bonita Paschal, MSBT - Georgia Tech Research Institute; Jon Duke, MD - Georgia Tech Research Institute;
Poster Number: P18
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Public Health, Real-World Evidence Generation, Delivering Health Information and Knowledge to the Public
Primary Track: Foundations
Programmatic Theme: Public Health Informatics
Claims data is a valuable data source to understand provider availability. Claims and eligibility information from the Georgia APCD were analyzed to determine annual primary care provider (PCP) counts and ratios by provider type, day-of-week, and county for 2018-2023. PCP-to-members ratios were lower for urban counties; both urban and rural PCP ratios declined from 2018-2022 and increased in 2023. Additionally, PCP availability was lower on weekends and nurse practitioners expanded in the PCP workforce.
Speaker:
Caleb Hightower, MSPH
Georgia Tech Research Institute
Authors:
Caleb Hightower, MSPH - Georgia Tech Research Institute; Charity Hilton - Georgia Tech Research Institute; Micaela Siraj; Richard Starr, BS - Georgia Institute of Technology; Bonita Paschal, MSBT - Georgia Tech Research Institute; Jon Duke, MD - Georgia Tech Research Institute;
Caleb
Hightower,
MSPH - Georgia Tech Research Institute
Clustering County-level Overdose Mortality Trajectories in Ohio: A Latent Profile Analysis Approach
Poster Number: P19
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Public Health, Population Health, Quantitative Methods
Primary Track: Policy
Programmatic Theme: Public Health Informatics
To analyze county-level differences in opioid mortality, a Latent Profile Analysis was used to cluster opioid overdose trajectories in Ohio counties from 2010-2020, finding four classes. After justifying clusters via opioid seizure data, significant social determinants of health variables were analyzed and only “Economic Context” variables were found to be statistically overrepresented. Therefore, policy makers should consider targeting underlying economic disparities in addition to other common public health interventions.
Speaker:
Jaeho Lee, N/A
Stanford Stats for Social Good
Authors:
Jaeho Lee, N/A - Stanford Stats for Social Good; Avery Zheng, High School - Phillips Academy Andover; Tianyi Evans Gu, High School - Phillips Academy Andover; William Kazanis, Ph.D. - Stanford University; Suzanne Tamang, PhD - Stanford University;
Poster Number: P19
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Public Health, Population Health, Quantitative Methods
Primary Track: Policy
Programmatic Theme: Public Health Informatics
To analyze county-level differences in opioid mortality, a Latent Profile Analysis was used to cluster opioid overdose trajectories in Ohio counties from 2010-2020, finding four classes. After justifying clusters via opioid seizure data, significant social determinants of health variables were analyzed and only “Economic Context” variables were found to be statistically overrepresented. Therefore, policy makers should consider targeting underlying economic disparities in addition to other common public health interventions.
Speaker:
Jaeho Lee, N/A
Stanford Stats for Social Good
Authors:
Jaeho Lee, N/A - Stanford Stats for Social Good; Avery Zheng, High School - Phillips Academy Andover; Tianyi Evans Gu, High School - Phillips Academy Andover; William Kazanis, Ph.D. - Stanford University; Suzanne Tamang, PhD - Stanford University;
Jaeho
Lee,
N/A - Stanford Stats for Social Good
3D Modeling of Objects Responsible for Foreign Body Injuries in Children: A Novel Web-Based Approach for Targeted Public Health Prevention and Pediatric Safety
Poster Number: P20
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Precision Medicine, Imaging Informatics, Public Health, Healthcare Quality, Information Extraction, Patient Safety, Pediatrics, Information Visualization
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Injuries caused by foreign bodies in children represent a significant public health concern. This project develops a novel web-based platform using 3D scanning of foreign bodies from Susy Safe registry, enabling risk profiling by linking object morphology, biomechanical dynamics of aspiration or ingestion, child demographics, socio-familial context, clinical outcomes. This approach supports the identification of object-specific risk profiles and fosters targeted prevention strategies to improve pediatric safety and public awareness.
Speaker:
Cinzia Anna Maria Papappicco, I have 2 Degrees: 1) Science in Nursing; 2) Computer Science
Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padua, Padua, Italy
Authors:
Cinzia Anna Maria Papappicco, I have 2 Degrees: 1) Science in Nursing; 2) Computer Science - Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padua, Padua, Italy; Solidea Baldas, MA - Prochild Protecting Children NPO, Trieste; Fuad Brkic, Medicine and Surgery - University Clinical Center Tuzla, Tuzla, Bosnia and Herzegovina; Giulia Lorenzoni, Epidemiology - Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy; Dario Gregori, Biostatistics - Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy;
Poster Number: P20
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Precision Medicine, Imaging Informatics, Public Health, Healthcare Quality, Information Extraction, Patient Safety, Pediatrics, Information Visualization
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Injuries caused by foreign bodies in children represent a significant public health concern. This project develops a novel web-based platform using 3D scanning of foreign bodies from Susy Safe registry, enabling risk profiling by linking object morphology, biomechanical dynamics of aspiration or ingestion, child demographics, socio-familial context, clinical outcomes. This approach supports the identification of object-specific risk profiles and fosters targeted prevention strategies to improve pediatric safety and public awareness.
Speaker:
Cinzia Anna Maria Papappicco, I have 2 Degrees: 1) Science in Nursing; 2) Computer Science
Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padua, Padua, Italy
Authors:
Cinzia Anna Maria Papappicco, I have 2 Degrees: 1) Science in Nursing; 2) Computer Science - Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padua, Padua, Italy; Solidea Baldas, MA - Prochild Protecting Children NPO, Trieste; Fuad Brkic, Medicine and Surgery - University Clinical Center Tuzla, Tuzla, Bosnia and Herzegovina; Giulia Lorenzoni, Epidemiology - Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy; Dario Gregori, Biostatistics - Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy;
Cinzia Anna Maria
Papappicco,
I have 2 Degrees: 1) Science in Nursing; 2) Computer Science - Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padua, Padua, Italy
Age- and Sex-Specific Cutoffs for Obesity-Related Indices to Improve Early Detection of Metabolic Syndrome Risk in Taiwanese Adults
Poster Number: P21
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Population Health, Public Health, Precision Medicine
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This study identified optimal age- and sex-specific cutoffs for BMI, TyG index, and CVAI in predicting metabolic syndrome (MetS) in Taiwanese adults, based on data from 30,382 subjects. Most identified cutoffs for these obesity-related indices demonstrated AUCs and sensitivity ranging from acceptable to outstanding (>0.70–0.90). Integrating these cutoffs into screening protocols can enhance early detection, prevention, and management of MetS risk.
Speaker:
Chien-Yeh Hsu, PhD
National Taipei University of Nursing and Health Sciences
Authors:
Chien-Yeh Hsu, PhD - National Taipei University of Nursing and Health Sciences; Jia-Wen Guo, PhD, RN, FAMIA - University of Utah;
Poster Number: P21
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Population Health, Public Health, Precision Medicine
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This study identified optimal age- and sex-specific cutoffs for BMI, TyG index, and CVAI in predicting metabolic syndrome (MetS) in Taiwanese adults, based on data from 30,382 subjects. Most identified cutoffs for these obesity-related indices demonstrated AUCs and sensitivity ranging from acceptable to outstanding (>0.70–0.90). Integrating these cutoffs into screening protocols can enhance early detection, prevention, and management of MetS risk.
Speaker:
Chien-Yeh Hsu, PhD
National Taipei University of Nursing and Health Sciences
Authors:
Chien-Yeh Hsu, PhD - National Taipei University of Nursing and Health Sciences; Jia-Wen Guo, PhD, RN, FAMIA - University of Utah;
Chien-Yeh
Hsu,
PhD - National Taipei University of Nursing and Health Sciences
Clustering Health Indicators: A Framework for Evaluating Population Health
Poster Number: P22
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Population Health, Public Health, Quantitative Methods
Primary Track: Applications
Programmatic Theme: Public Health Informatics
In this study, we developed a practical health indicator index, or HII, as a means of assessing population health, by assigning clusters to 29 health indicators. The data we used are from a previous survey study on public perception of health. These indicators are clustered by hierarchical clustering and K-means (K=7). Cluster designations were determined by identifying patterns in preventative health, metabolic health, behavioral factors, and social determinants. The results highlight a need to include both clinical and nonclinical factors for comprehensive health assessments. In future work, we will seek to refine the HII using additional clustering methods and adding the validation of real-world data.
Speaker:
Yujia sun, MS
Clemson University
Authors:
Xia Jing, MD, PhD - Clemson University; Jihad Obeid, MD - Medical University of South Carolina; Yujia sun, MS - Clemson University; Md Tareq Khan, PhD - Clemson University; Ronald Gimbel - Clemson University;
Poster Number: P22
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Population Health, Public Health, Quantitative Methods
Primary Track: Applications
Programmatic Theme: Public Health Informatics
In this study, we developed a practical health indicator index, or HII, as a means of assessing population health, by assigning clusters to 29 health indicators. The data we used are from a previous survey study on public perception of health. These indicators are clustered by hierarchical clustering and K-means (K=7). Cluster designations were determined by identifying patterns in preventative health, metabolic health, behavioral factors, and social determinants. The results highlight a need to include both clinical and nonclinical factors for comprehensive health assessments. In future work, we will seek to refine the HII using additional clustering methods and adding the validation of real-world data.
Speaker:
Yujia sun, MS
Clemson University
Authors:
Xia Jing, MD, PhD - Clemson University; Jihad Obeid, MD - Medical University of South Carolina; Yujia sun, MS - Clemson University; Md Tareq Khan, PhD - Clemson University; Ronald Gimbel - Clemson University;
Yujia
sun,
MS - Clemson University
Reducing Order Friction in Pediatric EHR Systems: A Quality Improvement Initiative
Poster Number: P23
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Pediatrics, Workflow, Qualitative Methods
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Efficient electronic health record (EHR) systems are critical for optimizing workflow and ensuring patient safety. However, many EHR systems are plagued with inefficiencies and excessive alerts. Order friction (OF) is an EHR vendor-derived metric describing how difficult it is for clinicians to enter orders, and includes the sum of all interruptive alerts, procedure duplicate checks, medication warnings, and changed fields within the order composer. Preliminary data shows high OF correlates with increased clinician stress, burnout, and delays in patient care. We devised a framework to prioritize order optimization. We focused on total order changes, or how many times a user changes a default, to prioritize orders with most OF and most volume. Oral solutions comprised most of the top medications, as pediatric care relies on weight-based dosing. We streamlined the order process for acetaminophen and ibuprofen oral solutions, reducing the previous requirement of four clicks for every-six-hour dosing as needed to zero clicks or CPO. Our data also reveals that the friction of preference lists is greater than the friction for order sets. User configured preference lists and order sets further reduced OF. In specific department preference lists, order design can be improved by setting default values for commonly used settings, thereby reducing the number of clicks and adjustments required to complete an order. The next steps are to focus on preference list orders and departments with the highest OF scores. We aim for an impactful reduction in clinician burden and improvement in patient care efficiency.
Speaker:
Bayley Bennett, MD
Emory University
Authors:
Brittany Brennan, MSN, PNP-AC - CHOA; Julia Yarahuan, MD, MBI - Children's Healthcare of Atlanta/Emory University; Swaminathan Kandaswamy, PhD - Emory University School of Medicine; Sarah Thompson, MSHIMI, BSN, RN - Children's Healthcare of Atlanta; Vedant Gupta, DO - Emory University/Children's Healthcare of Atlanta; Courtney Byrd, MD - Emory University/Children's Healthcare of Atlanta; Sunita Hemani, MD - Emory University/Children's Healthcare of Atlanta; Gargi Mukherjee, MD - Emory University/Children's Healthcare of Atlanta; Ryosuke Takei, MD - Children's Healthcare of Atlanta; Colin Packard, MD - Children's Healthcare of Atlanta; Bayley Bennett, MD - Emory University;
Poster Number: P23
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Pediatrics, Workflow, Qualitative Methods
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Efficient electronic health record (EHR) systems are critical for optimizing workflow and ensuring patient safety. However, many EHR systems are plagued with inefficiencies and excessive alerts. Order friction (OF) is an EHR vendor-derived metric describing how difficult it is for clinicians to enter orders, and includes the sum of all interruptive alerts, procedure duplicate checks, medication warnings, and changed fields within the order composer. Preliminary data shows high OF correlates with increased clinician stress, burnout, and delays in patient care. We devised a framework to prioritize order optimization. We focused on total order changes, or how many times a user changes a default, to prioritize orders with most OF and most volume. Oral solutions comprised most of the top medications, as pediatric care relies on weight-based dosing. We streamlined the order process for acetaminophen and ibuprofen oral solutions, reducing the previous requirement of four clicks for every-six-hour dosing as needed to zero clicks or CPO. Our data also reveals that the friction of preference lists is greater than the friction for order sets. User configured preference lists and order sets further reduced OF. In specific department preference lists, order design can be improved by setting default values for commonly used settings, thereby reducing the number of clicks and adjustments required to complete an order. The next steps are to focus on preference list orders and departments with the highest OF scores. We aim for an impactful reduction in clinician burden and improvement in patient care efficiency.
Speaker:
Bayley Bennett, MD
Emory University
Authors:
Brittany Brennan, MSN, PNP-AC - CHOA; Julia Yarahuan, MD, MBI - Children's Healthcare of Atlanta/Emory University; Swaminathan Kandaswamy, PhD - Emory University School of Medicine; Sarah Thompson, MSHIMI, BSN, RN - Children's Healthcare of Atlanta; Vedant Gupta, DO - Emory University/Children's Healthcare of Atlanta; Courtney Byrd, MD - Emory University/Children's Healthcare of Atlanta; Sunita Hemani, MD - Emory University/Children's Healthcare of Atlanta; Gargi Mukherjee, MD - Emory University/Children's Healthcare of Atlanta; Ryosuke Takei, MD - Children's Healthcare of Atlanta; Colin Packard, MD - Children's Healthcare of Atlanta; Bayley Bennett, MD - Emory University;
Bayley
Bennett,
MD - Emory University
HELPeR: A Novel Digital-Health Librarian Supporting Ovarian Cancer Patients and Caregivers Through Tailored Health Information
Poster Number: P24
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Patient Engagement and Preferences, Nursing Informatics, Information Retrieval, Delivering Health Information and Knowledge to the Public
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Background: Ovarian cancer (OvCa) presents significant informational challenges for patients and caregivers. HELPeR (Health E-Librarian with Personalized Recommendations) is a digital platform designed to provide tailored health information based on disease trajectory, user needs, and preferences. This study examined user characteristics, online information-seeking behaviors, and perceptions of the recommended content.
Methods: A 12-week study recruited OvCa patients and caregivers in person and online. Participants completed surveys at baseline, week 6, and week 12. They received a guided demonstration of HELPeR and were encouraged to use the system independently. Engagement metrics, including accessed articles, bookmarks, and feedback submissions, were analyzed.
Results: Among 20 participants, most relied on search engines (patients: 71.5%, caregivers: 69.3%) but used online recommendation systems infrequently. Patients accessed an average of 29 articles, bookmarked 11, and submitted 19 feedback responses, whereas caregivers accessed 13 articles, bookmarked 2, and submitted 19 responses. Both groups rated recommended articles highly for relevance and clarity, with symptom/treatment-related content being the most accessed.
Conclusion: Findings suggest that OvCa patients and caregivers actively seek online health information and benefit from tailored recommendations. However, the sample was skewed toward users with higher education and digital literacy. Patients engaged more than caregivers, potentially reflecting differing informational needs or caregiving burdens. Future research should explore barriers to caregiver engagement and assess HELPeR’s impact on long-term health outcomes. Digital platforms like HELPeR have the potential to bridge informational gaps and enhance the care experience.
Speaker:
Youjia Wang, BSN, RN
University of Pittsburgh School of Nursing
Authors:
Youjia Wang, BSN, RN - University of Pittsburgh School of Nursing; Khushboo M Thaker, PhD Candidate - University of Pittsburgh; Peter Brusilovsky, PHD - University of Pittsburgh; Daqing He, PhD - University of Pittsburgh; Heidi Donovan, PhD, RN; Young Ji Lee, PhD, MSN , RN - University of Pittsburgh;
Poster Number: P24
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Patient Engagement and Preferences, Nursing Informatics, Information Retrieval, Delivering Health Information and Knowledge to the Public
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Background: Ovarian cancer (OvCa) presents significant informational challenges for patients and caregivers. HELPeR (Health E-Librarian with Personalized Recommendations) is a digital platform designed to provide tailored health information based on disease trajectory, user needs, and preferences. This study examined user characteristics, online information-seeking behaviors, and perceptions of the recommended content.
Methods: A 12-week study recruited OvCa patients and caregivers in person and online. Participants completed surveys at baseline, week 6, and week 12. They received a guided demonstration of HELPeR and were encouraged to use the system independently. Engagement metrics, including accessed articles, bookmarks, and feedback submissions, were analyzed.
Results: Among 20 participants, most relied on search engines (patients: 71.5%, caregivers: 69.3%) but used online recommendation systems infrequently. Patients accessed an average of 29 articles, bookmarked 11, and submitted 19 feedback responses, whereas caregivers accessed 13 articles, bookmarked 2, and submitted 19 responses. Both groups rated recommended articles highly for relevance and clarity, with symptom/treatment-related content being the most accessed.
Conclusion: Findings suggest that OvCa patients and caregivers actively seek online health information and benefit from tailored recommendations. However, the sample was skewed toward users with higher education and digital literacy. Patients engaged more than caregivers, potentially reflecting differing informational needs or caregiving burdens. Future research should explore barriers to caregiver engagement and assess HELPeR’s impact on long-term health outcomes. Digital platforms like HELPeR have the potential to bridge informational gaps and enhance the care experience.
Speaker:
Youjia Wang, BSN, RN
University of Pittsburgh School of Nursing
Authors:
Youjia Wang, BSN, RN - University of Pittsburgh School of Nursing; Khushboo M Thaker, PhD Candidate - University of Pittsburgh; Peter Brusilovsky, PHD - University of Pittsburgh; Daqing He, PhD - University of Pittsburgh; Heidi Donovan, PhD, RN; Young Ji Lee, PhD, MSN , RN - University of Pittsburgh;
Youjia
Wang,
BSN, RN - University of Pittsburgh School of Nursing
Effectiveness of Participant Support in a Digital Health Trial: Insights from the DIAMANTE Study
Poster Number: P25
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Patient Engagement and Preferences, Mobile Health, User-centered Design Methods, Health Equity
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Digital health interventions can improve chronic disease management, particularly for those from historically underrepresented populations. A secondary analysis of the DIAMANTE RCT quantified and described the impact of automated text messages and human support outreach on participant retention. Our findings suggest that built-in digital and human support greatly improved retention (with over half of the participants re-engaging in the trial through this outreach), underscoring the need to tailor intervention engagement in the future.
Speaker:
Courtney Lyles, PhD
UC Davis Center for Healthcare Policy and Research
Authors:
Sara Guzman-Estrada, Bachleor of Science - University of California, San Francisco; Lynn Leng, BS - UC Berkeley-UCSF Joint Medical Program; Marvyn Arevalo Avalos, PhD - University of California, Berkeley; Adrian Aguilera, PhD - University of California, Berkeley; Courtney Lyles, PhD - UC Davis Center for Healthcare Policy and Research;
Poster Number: P25
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Patient Engagement and Preferences, Mobile Health, User-centered Design Methods, Health Equity
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Digital health interventions can improve chronic disease management, particularly for those from historically underrepresented populations. A secondary analysis of the DIAMANTE RCT quantified and described the impact of automated text messages and human support outreach on participant retention. Our findings suggest that built-in digital and human support greatly improved retention (with over half of the participants re-engaging in the trial through this outreach), underscoring the need to tailor intervention engagement in the future.
Speaker:
Courtney Lyles, PhD
UC Davis Center for Healthcare Policy and Research
Authors:
Sara Guzman-Estrada, Bachleor of Science - University of California, San Francisco; Lynn Leng, BS - UC Berkeley-UCSF Joint Medical Program; Marvyn Arevalo Avalos, PhD - University of California, Berkeley; Adrian Aguilera, PhD - University of California, Berkeley; Courtney Lyles, PhD - UC Davis Center for Healthcare Policy and Research;
Courtney
Lyles,
PhD - UC Davis Center for Healthcare Policy and Research
Quantifying Information Needs of Patients and Caregivers Managing Dementia through Classification of Patient-Initiated Messages
Poster Number: P26
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Patient Engagement and Preferences, Large Language Models (LLMs), Chronic Care Management
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
This study examines how patients with dementia and care partners use patient portal messaging to manage care. Using an expert-derived taxonomy of information needs, we classified the content of all messages sent after a dementia diagnosis. Among over 35000 messages, the majority discussed medications and managing clinical concerns, including changes to treatment plans. Understanding information needs is a critical step to providing resources and proactive support that improve the work of caregiving, coordination, and self-management.
Speaker:
Bryan Steitz, PhD
Vanderbilt University Medical Center
Authors:
Emily Morrow, PhD - Vanderbilt University Medical Center; Leslie Gaynor, PhD - Vanderbilt University Medical Center; Raymond Romano, PhD - Vanderbilt University Medical Center; Amanda Lazar, PhD - University of Maryland; Adam Wright, PhD - Vanderbilt University Medical Center;
Poster Number: P26
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Patient Engagement and Preferences, Large Language Models (LLMs), Chronic Care Management
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
This study examines how patients with dementia and care partners use patient portal messaging to manage care. Using an expert-derived taxonomy of information needs, we classified the content of all messages sent after a dementia diagnosis. Among over 35000 messages, the majority discussed medications and managing clinical concerns, including changes to treatment plans. Understanding information needs is a critical step to providing resources and proactive support that improve the work of caregiving, coordination, and self-management.
Speaker:
Bryan Steitz, PhD
Vanderbilt University Medical Center
Authors:
Emily Morrow, PhD - Vanderbilt University Medical Center; Leslie Gaynor, PhD - Vanderbilt University Medical Center; Raymond Romano, PhD - Vanderbilt University Medical Center; Amanda Lazar, PhD - University of Maryland; Adam Wright, PhD - Vanderbilt University Medical Center;
Bryan
Steitz,
PhD - Vanderbilt University Medical Center
Uncovering Pharmacogenomic Variability in Prostate cancer Using Big Data
Poster Number: P27
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Omics (genomics, metabolomics, proteomics, transcriptomics, etc.) and Integrative Analyses, Real-World Evidence Generation, Precision Medicine, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Pharmacogenomics improves clinical outcomes by enabling personalized medication, particularly for cancer patients. We analyzed 16 key pharmacogenes, including CYP2D6, in ~3,500 prostate cancer patients from the All of Us Program using WGS and EHR data. All patients had at least one actionable PGx phenotype (median is 5). 61.1% had both an actionable phenotype and exposure to an affected medication. These findings support integrating PGx into clinical care to optimize treatment outcomes for prostate cancer patients.
Speaker:
Huiyi Yang, PhD Student
University of Utah
Authors:
Huiyi Yang, PhD Student - University of Utah; Joseph Finkelstein, MD, PhD - University of Utah;
Poster Number: P27
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Omics (genomics, metabolomics, proteomics, transcriptomics, etc.) and Integrative Analyses, Real-World Evidence Generation, Precision Medicine, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Pharmacogenomics improves clinical outcomes by enabling personalized medication, particularly for cancer patients. We analyzed 16 key pharmacogenes, including CYP2D6, in ~3,500 prostate cancer patients from the All of Us Program using WGS and EHR data. All patients had at least one actionable PGx phenotype (median is 5). 61.1% had both an actionable phenotype and exposure to an affected medication. These findings support integrating PGx into clinical care to optimize treatment outcomes for prostate cancer patients.
Speaker:
Huiyi Yang, PhD Student
University of Utah
Authors:
Huiyi Yang, PhD Student - University of Utah; Joseph Finkelstein, MD, PhD - University of Utah;
Huiyi
Yang,
PhD Student - University of Utah
Clinical Nurses’ Readiness to Interpret AI-Powered Predictive Models: A Quantitative Assessment
Poster Number: P28
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Nursing Informatics, Surveys and Needs Analysis, Workforce Development, Artificial Intelligence, Education and Training
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
This study examined the statistical knowledge and health numeracy related to probability and likelihood, which are essential for understanding these AI-based models and systems. The findings revealed specific areas of weakness that need to be addressed.
Speaker:
INSOOK CHO, PhD
Inha University
Authors:
Jeong Hee Hong, PhD. - Samsung Medical Center; Sookhyun Park, PhD. - Samsung Medical Center; Eun Man Kim, Dr - Sun Moon University;
Poster Number: P28
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Nursing Informatics, Surveys and Needs Analysis, Workforce Development, Artificial Intelligence, Education and Training
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
This study examined the statistical knowledge and health numeracy related to probability and likelihood, which are essential for understanding these AI-based models and systems. The findings revealed specific areas of weakness that need to be addressed.
Speaker:
INSOOK CHO, PhD
Inha University
Authors:
Jeong Hee Hong, PhD. - Samsung Medical Center; Sookhyun Park, PhD. - Samsung Medical Center; Eun Man Kim, Dr - Sun Moon University;
INSOOK
CHO,
PhD - Inha University
The effectiveness of AI chatbots in alleviating mental distress and promoting health behaviors among Adolescents and Young Adults (AYAs): a systematic review and meta-analysis.
Poster Number: P29
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Nursing Informatics, Population Health, Delivering Health Information and Knowledge to the Public, Personal Health Informatics, Patient Engagement and Preferences, Public Health, Artificial Intelligence, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This study evaluates chatbot effectiveness in alleviating mental distress and promoting health behaviors among adolescents and young adults. A meta-analysis revealed small-to-moderate improvements in depressive, anxiety, stress, psychosomatic symptoms; negative affect, life satisfaction, self-ambivalence, and health behaviors. Dialogue system methods, deployment formats, and the use of reminders were identified as key moderators of success. While generative AI chatbots show promise, further research is required to validate their long-term impacts and establish robust safety protocols.
Speaker:
Xinyu Feng, Master
The Hong Kong Polytechnic University
Authors:
Grace Ho, PhD - The Hong Kong Polytechnic University; Janelle Yorke, PhD - The Hong Kong Polytechnic University; Vivian Hui, RN, PhD - The Hong Kong Polytechnic University;
Poster Number: P29
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Nursing Informatics, Population Health, Delivering Health Information and Knowledge to the Public, Personal Health Informatics, Patient Engagement and Preferences, Public Health, Artificial Intelligence, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This study evaluates chatbot effectiveness in alleviating mental distress and promoting health behaviors among adolescents and young adults. A meta-analysis revealed small-to-moderate improvements in depressive, anxiety, stress, psychosomatic symptoms; negative affect, life satisfaction, self-ambivalence, and health behaviors. Dialogue system methods, deployment formats, and the use of reminders were identified as key moderators of success. While generative AI chatbots show promise, further research is required to validate their long-term impacts and establish robust safety protocols.
Speaker:
Xinyu Feng, Master
The Hong Kong Polytechnic University
Authors:
Grace Ho, PhD - The Hong Kong Polytechnic University; Janelle Yorke, PhD - The Hong Kong Polytechnic University; Vivian Hui, RN, PhD - The Hong Kong Polytechnic University;
Xinyu
Feng,
Master - The Hong Kong Polytechnic University
An NLP Pipeline for Structuring Tremor Severity Ratings from Semi-Structured Clinical Notes
Poster Number: P30
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Large Language Models (LLMs), Information Extraction
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Tremor assessment relies on subjective clinician observations, limiting large-scale machine learning applications. We evaluated LLaMA3 (70B) for automated tremor classification, converting clinician notes into binary labels across seven body parts. Benchmarking against structured annotations (2,980 labels), the model achieved a macro F1 score of 0.893. Performance varied by body part, with strong agreement for hand tremors but lower accuracy for face and trunk. Future work will refine misclassifications and assess generalizability to less structured documentation.
Speaker:
Jeanne Powell, PhD
Emory University
Authors:
Christine Esper, M.D. - Emory University; Richa Tripathi, M.D. - Emory University; Mark Saad, M.S. - Emory University; Douglas Bernhard, M.S. - Emory University; Abeed Sarker, PhD - Emory University School of Medicine; J. Lucas Mckay, PhD, MSCR - Emory University;
Poster Number: P30
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Large Language Models (LLMs), Information Extraction
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Tremor assessment relies on subjective clinician observations, limiting large-scale machine learning applications. We evaluated LLaMA3 (70B) for automated tremor classification, converting clinician notes into binary labels across seven body parts. Benchmarking against structured annotations (2,980 labels), the model achieved a macro F1 score of 0.893. Performance varied by body part, with strong agreement for hand tremors but lower accuracy for face and trunk. Future work will refine misclassifications and assess generalizability to less structured documentation.
Speaker:
Jeanne Powell, PhD
Emory University
Authors:
Christine Esper, M.D. - Emory University; Richa Tripathi, M.D. - Emory University; Mark Saad, M.S. - Emory University; Douglas Bernhard, M.S. - Emory University; Abeed Sarker, PhD - Emory University School of Medicine; J. Lucas Mckay, PhD, MSCR - Emory University;
Jeanne
Powell,
PhD - Emory University
Identifying palliative care needs and cardiovascular symptoms in Dutch clinical notes: An evaluation of the text mining application NimbleMiner
Poster Number: P31
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Information Extraction, Nursing Informatics
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
The early identification of palliative care needs in cardiovascular patients is hindered by an administrative burden of patient identification tools. Natural language processing can be leveraged to extract information indicating palliative care needs from clinical notes in the electronic health record. This study evaluated the clinical text mining application NimbleMiner for the identification of symptoms indicating palliative care needs in Dutch clinical notes. We achieved excellent performance, demonstrating potential for future applications.
Speaker:
Chloé Desmedt, Master of Science in Nursing and Midwifery
KU Leuven
Authors:
Chloé Desmedt, Master of Science in Nursing - KU Leuven; Liesbet Van Bulck, PhD - KU Leuven; Meghan Reading Turchioe, PhD, MPH, RN - Columbia University School of Nursing; Kalina Parthoens, Master of Science in Nursing - KU Leuven; Siebe Vanhoutte, Master of Science in Nursing - KU Leuven; Philip Moons, PhD - KU Leuven, Faculty of Medicine;
Poster Number: P31
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Information Extraction, Nursing Informatics
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
The early identification of palliative care needs in cardiovascular patients is hindered by an administrative burden of patient identification tools. Natural language processing can be leveraged to extract information indicating palliative care needs from clinical notes in the electronic health record. This study evaluated the clinical text mining application NimbleMiner for the identification of symptoms indicating palliative care needs in Dutch clinical notes. We achieved excellent performance, demonstrating potential for future applications.
Speaker:
Chloé Desmedt, Master of Science in Nursing and Midwifery
KU Leuven
Authors:
Chloé Desmedt, Master of Science in Nursing - KU Leuven; Liesbet Van Bulck, PhD - KU Leuven; Meghan Reading Turchioe, PhD, MPH, RN - Columbia University School of Nursing; Kalina Parthoens, Master of Science in Nursing - KU Leuven; Siebe Vanhoutte, Master of Science in Nursing - KU Leuven; Philip Moons, PhD - KU Leuven, Faculty of Medicine;
Chloé
Desmedt,
Master of Science in Nursing and Midwifery - KU Leuven
An NLP Method to Identify Macro Guideline Sentences in Radiology Reports
Poster Number: P32
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Information Extraction, Deep Learning
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
Radiology reports contain system-generated macro guidelines alongside patient-specific findings, which can interfere with information extraction (IE) models. This study presents a ClinicalBERT-based approach to identify and filter macro guideline sentences, improving patient-specific data extraction. Using an annotated dataset of 8,860 sentences, our model achieved superior performance over a TF-IDF + SVM baseline, with an F1-score increase from 0.9544 to 0.9705. Filtering macros enhances dataset quality for downstream NLP tasks, though further refinements are needed to address misclassification challenges.
Speaker:
Zhaoyi Sun, Master of Science
University of Washington
Authors:
Zhaoyi Sun, Master of Science - University of Washington; Namu Park, MS - Biomedical Informatics and Medical Education, University of Washington; Ozlem Uzuner, PhD - George Mason University; Martin Gunn, MBChB - University of Washington; Meliha Yetisgen, PhD - University of Washington;
Poster Number: P32
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Information Extraction, Deep Learning
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
Radiology reports contain system-generated macro guidelines alongside patient-specific findings, which can interfere with information extraction (IE) models. This study presents a ClinicalBERT-based approach to identify and filter macro guideline sentences, improving patient-specific data extraction. Using an annotated dataset of 8,860 sentences, our model achieved superior performance over a TF-IDF + SVM baseline, with an F1-score increase from 0.9544 to 0.9705. Filtering macros enhances dataset quality for downstream NLP tasks, though further refinements are needed to address misclassification challenges.
Speaker:
Zhaoyi Sun, Master of Science
University of Washington
Authors:
Zhaoyi Sun, Master of Science - University of Washington; Namu Park, MS - Biomedical Informatics and Medical Education, University of Washington; Ozlem Uzuner, PhD - George Mason University; Martin Gunn, MBChB - University of Washington; Meliha Yetisgen, PhD - University of Washington;
Zhaoyi
Sun,
Master of Science - University of Washington
Association between no-shows to scheduled appointments and 30-day Risk of Overdose in Patients Prescribed Methadone for Opioid Use Disorder
Poster Number: P113
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Patient Safety, Healthcare Quality
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
In the context of the ongoing opioid crisis, this study examined the association between no-shows in the past 30 days and 30-day overdose risk among patients prescribed methadone for opioid use disorder. Analysis of 1,071 patients showed Z-scored no-shows were associated with 30-day overdose risk when adjusting for temporal variables and patient characteristics (OR=1.23, 95%CI:1.19-1.27, p<0.001). These findings suggest attendance tracking could contribute to risk stratification in opioid treatment programs.
Speaker:
Henry Philofsky, MD
University of Rochester Medical Center
Authors:
Henry Philofsky, MD - University of Rochester Medical Center; Ian Cero, Ph.D. - University of Rochester Medical Center; Dukjae Maeng, Ph.D - University of Rochester Medical Center; Myra Mathis, MD - University of Rochester Medical Center;
Poster Number: P113
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Patient Safety, Healthcare Quality
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
In the context of the ongoing opioid crisis, this study examined the association between no-shows in the past 30 days and 30-day overdose risk among patients prescribed methadone for opioid use disorder. Analysis of 1,071 patients showed Z-scored no-shows were associated with 30-day overdose risk when adjusting for temporal variables and patient characteristics (OR=1.23, 95%CI:1.19-1.27, p<0.001). These findings suggest attendance tracking could contribute to risk stratification in opioid treatment programs.
Speaker:
Henry Philofsky, MD
University of Rochester Medical Center
Authors:
Henry Philofsky, MD - University of Rochester Medical Center; Ian Cero, Ph.D. - University of Rochester Medical Center; Dukjae Maeng, Ph.D - University of Rochester Medical Center; Myra Mathis, MD - University of Rochester Medical Center;
Henry
Philofsky,
MD - University of Rochester Medical Center
Seize the Moment: Clinical Decision Support for Timely Seizure Rescue Medication Orders
Poster Number: P114
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Patient Safety, Human-computer Interaction, Pediatrics
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Effective management of in-hospital seizures require timely recognition and intervention, presenting an opportunity for implementation of Clinical Decision Support (CDS) tools to facilitate necessary orders. Despite providers’ recognition of the potential benefit of CDS to prevent potential care delays, post implementation monitoring reveals a persistent discrepancy between alert activation and acceptance. This highlights the need for ongoing research and refinement to improve CDS efficacy in addressing patient safety concerns.
Speaker:
Sarah Thompson, MSHIMI, BSN, RN
Children's Healthcare of Atlanta
Authors:
Lauren Albor, MD - Children's Healthcare of Atlanta/Emory University; Martha Hummer, BSN,RN - Children's Healthcare of Atlanta; Swaminathan Kandaswamy, PhD - Emory University School of Medicine; Julia Yarahuan, MD, MBI - Children's Healthcare of Atlanta/Emory University;
Poster Number: P114
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Patient Safety, Human-computer Interaction, Pediatrics
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Effective management of in-hospital seizures require timely recognition and intervention, presenting an opportunity for implementation of Clinical Decision Support (CDS) tools to facilitate necessary orders. Despite providers’ recognition of the potential benefit of CDS to prevent potential care delays, post implementation monitoring reveals a persistent discrepancy between alert activation and acceptance. This highlights the need for ongoing research and refinement to improve CDS efficacy in addressing patient safety concerns.
Speaker:
Sarah Thompson, MSHIMI, BSN, RN
Children's Healthcare of Atlanta
Authors:
Lauren Albor, MD - Children's Healthcare of Atlanta/Emory University; Martha Hummer, 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;
Sarah
Thompson,
MSHIMI, BSN, RN - Children's Healthcare of Atlanta
Electronic Strategies for Tailored Exercise to Prevent Falls (eSTEPS): Implementation in a Tertiary Academic Health System
Poster Number: P115
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Patient Safety, Healthcare Quality
Primary Track: Applications
Falls are the leading cause of injury and injury-related death in older adults. The Electronic Strategies for Tailored Exercise to Prevent Falls (eSTEPS) study evaluates an EHR-based clinical decision support (CDS) tool that promotes evidence-based exercise referrals to reduce fall risk. Launched in April 2023 within a tertiary academic health system, the CDS triggered in 92% of eligible visits between July 2023 to June 2024, with referrals placed 26% of the time. This report examines reach, adoption, and implementation in year one, and explores patient and clinician factors influencing referral decisions.
Speaker:
Jenna Reisler, MD
University of Texas Medical Branch
Authors:
Erin Hommel, MD MS - University of Texas Medical Branch at Galveston; Biai Digbeu, MPH - University of Texas Medical Branch; Thien Thai, BS - Brigham and Women’s Hospital; Michael Sainlaire - Brigham and Women's Health; Mackenzie Kiesman, BA - Brigham and Women's Hospital; Nancy Latham, PhD PT - Brigham and Women's Hosptial; Patricia Dykes, RN, PhD - Brigham and Women’s Hospital;
Poster Number: P115
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Patient Safety, Healthcare Quality
Primary Track: Applications
Falls are the leading cause of injury and injury-related death in older adults. The Electronic Strategies for Tailored Exercise to Prevent Falls (eSTEPS) study evaluates an EHR-based clinical decision support (CDS) tool that promotes evidence-based exercise referrals to reduce fall risk. Launched in April 2023 within a tertiary academic health system, the CDS triggered in 92% of eligible visits between July 2023 to June 2024, with referrals placed 26% of the time. This report examines reach, adoption, and implementation in year one, and explores patient and clinician factors influencing referral decisions.
Speaker:
Jenna Reisler, MD
University of Texas Medical Branch
Authors:
Erin Hommel, MD MS - University of Texas Medical Branch at Galveston; Biai Digbeu, MPH - University of Texas Medical Branch; Thien Thai, BS - Brigham and Women’s Hospital; Michael Sainlaire - Brigham and Women's Health; Mackenzie Kiesman, BA - Brigham and Women's Hospital; Nancy Latham, PhD PT - Brigham and Women's Hosptial; Patricia Dykes, RN, PhD - Brigham and Women’s Hospital;
Jenna
Reisler,
MD - University of Texas Medical Branch
Clinical Decision Support Tool to Increase the Diagnosis of Pediatric Acute Respiratory Distress Syndrome to Improve Adherence with Lung Protective Ventilation
Poster Number: P112
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Pediatrics, Critical Care, Clinical Guidelines
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This feasibility study evaluated a silently implemented CDS system for identifying PARDS in PICU patients per PALICC-2 guidelines using OI/OSI criteria. Over 47 days in 2024, 415 patients were screened; the CDS flagged 9.88% (41 patients), with 46% (19) meeting PARDS criteria (4.5% of PICU patients). Only 1 patient (5.2%) received lung-protective ventilation per guidelines. Findings suggest under-recognition of PARDS and limited adherence to recommended ventilation strategies.
Speaker:
Ryan Winter, Physician Assistant
Childrens Healthcare of Atlanta
Authors:
Ryan Winter, Physician Assistant - Childrens Healthcare of Atlanta; Mark Mai, MD, MHS - Children's Healthcare of Atlanta; Natalie Bishop, MD, MS - Emory University; Jocelyn Grunwell, MD, PhD - Emory University / Children's Healthcare of Atlanta; Prakadeshwari Rajapreyar, MD, MBA - Children's Healthcare of Atlanta;
Poster Number: P112
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Pediatrics, Critical Care, Clinical Guidelines
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This feasibility study evaluated a silently implemented CDS system for identifying PARDS in PICU patients per PALICC-2 guidelines using OI/OSI criteria. Over 47 days in 2024, 415 patients were screened; the CDS flagged 9.88% (41 patients), with 46% (19) meeting PARDS criteria (4.5% of PICU patients). Only 1 patient (5.2%) received lung-protective ventilation per guidelines. Findings suggest under-recognition of PARDS and limited adherence to recommended ventilation strategies.
Speaker:
Ryan Winter, Physician Assistant
Childrens Healthcare of Atlanta
Authors:
Ryan Winter, Physician Assistant - Childrens Healthcare of Atlanta; Mark Mai, MD, MHS - Children's Healthcare of Atlanta; Natalie Bishop, MD, MS - Emory University; Jocelyn Grunwell, MD, PhD - Emory University / Children's Healthcare of Atlanta; Prakadeshwari Rajapreyar, MD, MBA - Children's Healthcare of Atlanta;
Ryan
Winter,
Physician Assistant - Childrens Healthcare of Atlanta
Assessment of Inclusion in USCDI v5 of Query Data Elements Required to Execute a Clinical Decision Support Knowledge Base Encoded in Arden Syntax
Poster Number: P111
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Standards, Interoperability and Health Information Exchange
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Context: Arden Syntax encodes computable clinical knowledge as Medical Logic Modules (MLMs). USCDI delineates data classes and elements for health data exchange. Objective: Assess the extent to which data shared pursuant to USCDI is adequate for execution of an Arden Syntax knowledge base (KB). Methods: 325 MLMs containing 3268 queries were examined. Result: USCDI includes all queried data elements. Conclusion: Data exchanged pursuant to USCDI would be adequate to execute a KB of MLMs.
Speaker:
Robert Jenders, MD, MS, FACP, FACMI, FHL7, FAMIA
Charles Drew University/UCLA
Author:
Robert Jenders, MD, MS, FACP, FACMI, FHL7, FAMIA - Charles Drew University/UCLA;
Poster Number: P111
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Standards, Interoperability and Health Information Exchange
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Context: Arden Syntax encodes computable clinical knowledge as Medical Logic Modules (MLMs). USCDI delineates data classes and elements for health data exchange. Objective: Assess the extent to which data shared pursuant to USCDI is adequate for execution of an Arden Syntax knowledge base (KB). Methods: 325 MLMs containing 3268 queries were examined. Result: USCDI includes all queried data elements. Conclusion: Data exchanged pursuant to USCDI would be adequate to execute a KB of MLMs.
Speaker:
Robert Jenders, MD, MS, FACP, FACMI, FHL7, FAMIA
Charles Drew University/UCLA
Author:
Robert Jenders, MD, MS, FACP, FACMI, FHL7, FAMIA - Charles Drew University/UCLA;
Robert
Jenders,
MD, MS, FACP, FACMI, FHL7, FAMIA - Charles Drew University/UCLA
Use of Advanced Design Methods for End-of-Life Care
Poster Number: P110
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Transitions of Care, Machine Learning, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
With breast cancer and the progression to metastatic breast cancer affecting millions of women annually, artificial intelligence (AI) and machine learning methodologies have been utilized to pursue the understanding of cellular or biological disease components, promote therapeutic advancements, improve the analysis of radiology procedures and images, and advance prognostication accuracies. However, in the context of end-of-life (EOL) care and supportive care interventions, AI has been used in a limited capacity.
Speaker:
Rachel Brazee, PhD
University of Pittsburgh
Authors:
Rachel Brazee, PhD - University of Pittsburgh; Karen Alsbrook, PhD, RN, OCN - University of Texas MD Anderson Cancer Center; Isabel Martin, MPH - University of Pittsburgh; Jennifer Seaman, PhD, RN, CHPN - University of Pittsburgh; Young Ji Lee, PhD, MSN , RN - University of Pittsburgh; Margaret Rosenzweig, PhD, CRNP-C, AOCNP, FAAN - University of Pittsburgh;
Poster Number: P110
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Transitions of Care, Machine Learning, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
With breast cancer and the progression to metastatic breast cancer affecting millions of women annually, artificial intelligence (AI) and machine learning methodologies have been utilized to pursue the understanding of cellular or biological disease components, promote therapeutic advancements, improve the analysis of radiology procedures and images, and advance prognostication accuracies. However, in the context of end-of-life (EOL) care and supportive care interventions, AI has been used in a limited capacity.
Speaker:
Rachel Brazee, PhD
University of Pittsburgh
Authors:
Rachel Brazee, PhD - University of Pittsburgh; Karen Alsbrook, PhD, RN, OCN - University of Texas MD Anderson Cancer Center; Isabel Martin, MPH - University of Pittsburgh; Jennifer Seaman, PhD, RN, CHPN - University of Pittsburgh; Young Ji Lee, PhD, MSN , RN - University of Pittsburgh; Margaret Rosenzweig, PhD, CRNP-C, AOCNP, FAAN - University of Pittsburgh;
Rachel
Brazee,
PhD - University of Pittsburgh
Impact Of Close Encounter Checkpoint Notifications on G2211 Billing Accuracy in Primary Care Practices
Poster Number: P109
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, User-centered Design Methods, Workflow, Chronic Care Management, Healthcare Economics/Cost of Care, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Effective January 1, 2024, Healthcare Common Procedure Coding System (HCPCS) billing code G2211 marks a significant advancement in recognizing the complexity and continuity of outpatient visits in primary care. Accurate billing is essential for appropriate reimbursement, benefiting both clinicians and patients. We implemented an alert system to notify clinicians of eligible encounters, significantly improving G2211 utilization and accuracy. These findings highlight the role of automated notifications in optimizing reimbursement and enhancing documentation precision in primary care.
Speaker:
Stefan Mathews, MD, MSHI, FAAFP
Virtua
Authors:
Stefan Mathews, MD, MSHI, FAAFP - Virtua; Kevin Wiley, PhD - Medical University of South Carolina;
Poster Number: P109
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, User-centered Design Methods, Workflow, Chronic Care Management, Healthcare Economics/Cost of Care, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Effective January 1, 2024, Healthcare Common Procedure Coding System (HCPCS) billing code G2211 marks a significant advancement in recognizing the complexity and continuity of outpatient visits in primary care. Accurate billing is essential for appropriate reimbursement, benefiting both clinicians and patients. We implemented an alert system to notify clinicians of eligible encounters, significantly improving G2211 utilization and accuracy. These findings highlight the role of automated notifications in optimizing reimbursement and enhancing documentation precision in primary care.
Speaker:
Stefan Mathews, MD, MSHI, FAAFP
Virtua
Authors:
Stefan Mathews, MD, MSHI, FAAFP - Virtua; Kevin Wiley, PhD - Medical University of South Carolina;
Stefan
Mathews,
MD, MSHI, FAAFP - Virtua
Impact of Radiology Order on ED Orthopedic Consult Turnaround Time
Poster Number: P108
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Workflow, Healthcare Quality, Data Mining
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study analyzes one year of Emergency Department (ED) orthopedic consult data (N = 1568) to assess the effects of radiology order timing. Post-consult radiology orders increased turnaround time by 30% (5.42 vs. 3.74 hours, p < 0.001). This pattern was consistent across patient dispositions, suggesting earlier radiology or shifting post-consult radiology to inpatient or observation settings may help reduce delays and improve ED throughput.
Speaker:
Christina Gomez, MIS
Jackson Health Systems
Authors:
Christina Gomez, MIS - Jackson Health Systems; Jia Zeng, PhD - Jackson Health Systems; George Rosello, MS - Jackson Health System; Timothy Tan, MD MPH - Jackson Health Systems;
Poster Number: P108
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Workflow, Healthcare Quality, Data Mining
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study analyzes one year of Emergency Department (ED) orthopedic consult data (N = 1568) to assess the effects of radiology order timing. Post-consult radiology orders increased turnaround time by 30% (5.42 vs. 3.74 hours, p < 0.001). This pattern was consistent across patient dispositions, suggesting earlier radiology or shifting post-consult radiology to inpatient or observation settings may help reduce delays and improve ED throughput.
Speaker:
Christina Gomez, MIS
Jackson Health Systems
Authors:
Christina Gomez, MIS - Jackson Health Systems; Jia Zeng, PhD - Jackson Health Systems; George Rosello, MS - Jackson Health System; Timothy Tan, MD MPH - Jackson Health Systems;
Christina
Gomez,
MIS - Jackson Health Systems
Developing a Comprehensive Vocabulary for Cannabis Use Documentation
Poster Number: P106
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Controlled Terminologies, Ontologies, and Vocabularies, Knowledge Representation and Information Modeling, Information Extraction, Natural Language Processing
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
Cannabis use has steadily increased with medical and recreational legalization in U.S. states, but characterizing use within electronic health records remains challenging. We developed a vocabulary of 135 terms, including names, product forms, routes, reasons for use, and drug-drug interactions through an iterative process based on prior studies, expert recommendations, and analysis of 156,504 clinical notes. This resource aims to fill gaps in existing vocabularies to support cohort identification and decision making in medical contexts.
Speaker:
Wei Wei, PhD
Geisinger
Authors:
Wei Wei, PhD - Geisinger; Matthew Woll, PhD - Geisinger; Antoinette Dicriscio, PhD - Geisinger; Vanessa Troiani, PhD - Geisinger;
Poster Number: P106
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Controlled Terminologies, Ontologies, and Vocabularies, Knowledge Representation and Information Modeling, Information Extraction, Natural Language Processing
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
Cannabis use has steadily increased with medical and recreational legalization in U.S. states, but characterizing use within electronic health records remains challenging. We developed a vocabulary of 135 terms, including names, product forms, routes, reasons for use, and drug-drug interactions through an iterative process based on prior studies, expert recommendations, and analysis of 156,504 clinical notes. This resource aims to fill gaps in existing vocabularies to support cohort identification and decision making in medical contexts.
Speaker:
Wei Wei, PhD
Geisinger
Authors:
Wei Wei, PhD - Geisinger; Matthew Woll, PhD - Geisinger; Antoinette Dicriscio, PhD - Geisinger; Vanessa Troiani, PhD - Geisinger;
Wei
Wei,
PhD - Geisinger
Utilizing Ontologies to Map the Intervention Landscape of Depression
Poster Number: P107
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Controlled Terminologies, Ontologies, and Vocabularies, Knowledge Representation and Information Modeling, Natural Language Processing, Data transformation/ETL
Primary Track: Foundations
Depression is a complex condition that is treated by both pharmacological and non-pharmacological interventions. In this study, we analyzed electronic health records (EHRs) to explore pharmacological and non-pharmacological interventions for depression using an ontology-driven approach. By creating a pharmacological ontology for antidepressants and applying the DREAMDNPTO ontology and QuickUMLS for non-pharmacological treatments, we can find patterns in depression-related treatment use from MIMIC-IV data. This analysis can provide insights into intervention trends.
Speaker:
Catherine Kim, B.S.
University of Washington
Authors:
Ziqing Ji, Biomedical and Health Informatics - University of Washington; Kenneth Yi, Biomedical and Health Informatics - University of Washington; John Gennari, PhD - University of Washington, Dept of Biomedical Informatics & Medical Education;
Poster Number: P107
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Controlled Terminologies, Ontologies, and Vocabularies, Knowledge Representation and Information Modeling, Natural Language Processing, Data transformation/ETL
Primary Track: Foundations
Depression is a complex condition that is treated by both pharmacological and non-pharmacological interventions. In this study, we analyzed electronic health records (EHRs) to explore pharmacological and non-pharmacological interventions for depression using an ontology-driven approach. By creating a pharmacological ontology for antidepressants and applying the DREAMDNPTO ontology and QuickUMLS for non-pharmacological treatments, we can find patterns in depression-related treatment use from MIMIC-IV data. This analysis can provide insights into intervention trends.
Speaker:
Catherine Kim, B.S.
University of Washington
Authors:
Ziqing Ji, Biomedical and Health Informatics - University of Washington; Kenneth Yi, Biomedical and Health Informatics - University of Washington; John Gennari, PhD - University of Washington, Dept of Biomedical Informatics & Medical Education;
Catherine
Kim,
B.S. - University of Washington
Mapping Nursing Flowsheet Data to Common Data Models: A Pilot Study
Poster Number: P105
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Controlled Terminologies, Ontologies, and Vocabularies, Nursing Informatics, Data Standards, Interoperability and Health Information Exchange
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Nurses, the largest healthcare workforce, generate most data through flowsheets, yet national data initiatives generally focus on physician-centered data. This study examines nurse-sensitive data representation in a common data model (CDM). Similar to previous studies, 65.5% of flowsheet rows were mapped to SNOMED CT and LOINC, highlighting a gap in nurse data representation. Further work is needed to identify key flowsheet rows influencing nursing outcomes and develop a more representative CDM.
Speaker:
Katy Stewart, Biomedical Informatics BS
Mayo Clinic
Authors:
Malin Britt Lalich, BSN, RN, PHN - University of Minnesota; Elizabeth Umberfield, PhD, RN, NI-BC - Office of Data Science and Sharing (ODSS), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NIH/NICHD); Melissa Pinto, PhD, RN, FAAN - Mayo Clinic; Robin Austin, PhD, DNP, DC, RN, NI-BC, FAMIA, FAAN - University of Minnesota, School of Nursing;
Poster Number: P105
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Controlled Terminologies, Ontologies, and Vocabularies, Nursing Informatics, Data Standards, Interoperability and Health Information Exchange
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Nurses, the largest healthcare workforce, generate most data through flowsheets, yet national data initiatives generally focus on physician-centered data. This study examines nurse-sensitive data representation in a common data model (CDM). Similar to previous studies, 65.5% of flowsheet rows were mapped to SNOMED CT and LOINC, highlighting a gap in nurse data representation. Further work is needed to identify key flowsheet rows influencing nursing outcomes and develop a more representative CDM.
Speaker:
Katy Stewart, Biomedical Informatics BS
Mayo Clinic
Authors:
Malin Britt Lalich, BSN, RN, PHN - University of Minnesota; Elizabeth Umberfield, PhD, RN, NI-BC - Office of Data Science and Sharing (ODSS), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NIH/NICHD); Melissa Pinto, PhD, RN, FAAN - Mayo Clinic; Robin Austin, PhD, DNP, DC, RN, NI-BC, FAMIA, FAAN - University of Minnesota, School of Nursing;
Katy
Stewart,
Biomedical Informatics BS - Mayo Clinic
A Reciprocal Approach to Understanding ICU Stays in Alcohol-associated Liver Disease: Bridging Disease-specific Modeling and Clinical Knowledge
Poster Number: P104
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Critical Care, Real-World Evidence Generation, Machine Learning, Healthcare Quality, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Decompensated alcohol-associated liver disease presents with severe systemic manifestations that often necessitate critical care. ICU length of stay influences patient outcomes, caregiver communication, healthcare costs, and resource allocation. This study compares various machine learning algorithms to determine the best-performing model for predicting ICU length of stay among patients with alcohol-associated liver disease, emphasizing the importance of disease-specific features in model optimization and the bidirectional relationship between machine learning model interpretation and clinical knowledge.
Speaker:
Sue Hyon Kim, MSN, RN
University of Pennsylvania
Authors:
Jiyoun Song, PhD - University of Pennsylvania School of Nursing; Se Hee Min, PhD - University of Pennsylvania; Marina Serper, MD, MS - Hospital of the University of Pennsylvania;
Poster Number: P104
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Critical Care, Real-World Evidence Generation, Machine Learning, Healthcare Quality, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Decompensated alcohol-associated liver disease presents with severe systemic manifestations that often necessitate critical care. ICU length of stay influences patient outcomes, caregiver communication, healthcare costs, and resource allocation. This study compares various machine learning algorithms to determine the best-performing model for predicting ICU length of stay among patients with alcohol-associated liver disease, emphasizing the importance of disease-specific features in model optimization and the bidirectional relationship between machine learning model interpretation and clinical knowledge.
Speaker:
Sue Hyon Kim, MSN, RN
University of Pennsylvania
Authors:
Jiyoun Song, PhD - University of Pennsylvania School of Nursing; Se Hee Min, PhD - University of Pennsylvania; Marina Serper, MD, MS - Hospital of the University of Pennsylvania;
Sue Hyon
Kim,
MSN, RN - University of Pennsylvania
Finding Rare Disease Experts for the Undiagnosed Disease Network
Poster Number: P103
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Data Mining, Informatics Implementation, Information Retrieval, Information Visualization
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
The NIH-funded Undiagnosed Disease Network (UDN) brings together experts from healthcare organizations across the United States to provide answers for patients and families affected by mysterious conditions where clinicians have been unable to discover the cause. We used “Profiles”, an open-source system for generating searchable online profiles of investigators, to create a website to help the UDN find these experts. Profiles imports and links data from PubMed, ClinicalTrials.gov, GeneReports, OMIM, and more.
Speaker:
Griffin Weber, MD, PhD
Harvard Medical School
Author:
Nick Brown, MEng - Harvard Medical School;
Poster Number: P103
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Data Mining, Informatics Implementation, Information Retrieval, Information Visualization
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
The NIH-funded Undiagnosed Disease Network (UDN) brings together experts from healthcare organizations across the United States to provide answers for patients and families affected by mysterious conditions where clinicians have been unable to discover the cause. We used “Profiles”, an open-source system for generating searchable online profiles of investigators, to create a website to help the UDN find these experts. Profiles imports and links data from PubMed, ClinicalTrials.gov, GeneReports, OMIM, and more.
Speaker:
Griffin Weber, MD, PhD
Harvard Medical School
Author:
Nick Brown, MEng - Harvard Medical School;
Griffin
Weber,
MD, PhD - Harvard Medical School
Exploring Data Scraping on ClinicalTrials.gov to Identify Key Variables to Include in an EHR-based Recruitment Tool for Diabetes Clinical Trials
Poster Number: P102
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Data Mining, Population Health, Workflow
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Failure to meet recruitment goals leads to trial termination and delays. We developed an EHR-based recruitment tool by scraping clinicaltrials.gov to identify criteria from 203 diabetes trials. Of 115 terms, 91 were clinically relevant. There was a large overlap in criteria from data scraping and consulting trialists with 3 criteria unique to trialists and 11 criteria uniquely identified through scraping. Automating variable selection via clinicaltrials.gov extraction streamlines recruitment while maintaining clinical relevance.
Speaker:
Sydney Lash, B.S.
University of North Carolina at Chapel Hill
Authors:
Sydney Lash, B.S. - University of North Carolina at Chapel Hill; Emily Pfaff, PhD, MS - UNC Chapel Hill School of Medicine; Klara Klein, MD, PhD - University of North Carolina at Chapel Hill;
Poster Number: P102
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Data Mining, Population Health, Workflow
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Failure to meet recruitment goals leads to trial termination and delays. We developed an EHR-based recruitment tool by scraping clinicaltrials.gov to identify criteria from 203 diabetes trials. Of 115 terms, 91 were clinically relevant. There was a large overlap in criteria from data scraping and consulting trialists with 3 criteria unique to trialists and 11 criteria uniquely identified through scraping. Automating variable selection via clinicaltrials.gov extraction streamlines recruitment while maintaining clinical relevance.
Speaker:
Sydney Lash, B.S.
University of North Carolina at Chapel Hill
Authors:
Sydney Lash, B.S. - University of North Carolina at Chapel Hill; Emily Pfaff, PhD, MS - UNC Chapel Hill School of Medicine; Klara Klein, MD, PhD - University of North Carolina at Chapel Hill;
Sydney
Lash,
B.S. - University of North Carolina at Chapel Hill
Privacy-Preserving Dysphonia Detection based on Distributed Deep Learning
Poster Number: P101
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Data Sharing, Privacy and Security, Machine Learning
Working Group: Ethical, Legal, and Social Issues Working Group
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
This study presents a privacy-preserving dysphonia detection system using distributed deep learning. By integrating speech data from different healthcare systems, we employ federated and split learning to train models without sharing raw data. Split learning, using the SplitFedV1 framework, outperforms federated learning in terms of convergence speed and accuracy. We also adapted the Audio Spectrogram Transformer to the split learning framework, demonstrating its effectiveness across various neural networks.
Speaker:
Jiarui Xu, N/A
ShanghaiTech University
Authors:
Jiarui Xu, N/A - ShanghaiTech University; Zhuohang Li, MS - Vanderbilt University; Yike Li, MD, PhD - Vanderbilt University Medical Center; Zhiyu Wan, PhD - ShanghaiTech University;
Poster Number: P101
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Data Sharing, Privacy and Security, Machine Learning
Working Group: Ethical, Legal, and Social Issues Working Group
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
This study presents a privacy-preserving dysphonia detection system using distributed deep learning. By integrating speech data from different healthcare systems, we employ federated and split learning to train models without sharing raw data. Split learning, using the SplitFedV1 framework, outperforms federated learning in terms of convergence speed and accuracy. We also adapted the Audio Spectrogram Transformer to the split learning framework, demonstrating its effectiveness across various neural networks.
Speaker:
Jiarui Xu, N/A
ShanghaiTech University
Authors:
Jiarui Xu, N/A - ShanghaiTech University; Zhuohang Li, MS - Vanderbilt University; Yike Li, MD, PhD - Vanderbilt University Medical Center; Zhiyu Wan, PhD - ShanghaiTech University;
Jiarui
Xu,
N/A - ShanghaiTech University
Creation of a Novel EHR Provider Utilization Dashboard to Drive Process Improvement and Enhance Provider Well Being
Poster Number: P100
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Data Sharing, Usability, Information Visualization, User-centered Design Methods
Primary Track: Applications
Several electronic health record (EHR) metrics are correlated to provider burnout and job dissatisfaction rates. Enterprise-wide end user metric data review is important to understand EHR burdens and benefits. Cerner Advance (CA) and Lights On Network (LON) metrics provide end user data analytics with limitations on how data can be filtered, disseminated, and viewed. Using HealtheIntent and Tableau analytics a dynamic EHR utilization dashboard was created to present this data in a user friendly format.
Speaker:
Stacey Stokes, MD, MPH
Children's National Hospital
Authors:
Geetanjali Vashist, M.S. - Children's National Hospital; Parissa Safari, M.H.A. - Children's National Hospital; Ranjodh Badh, B.S. - Children's National Hospital; Shivaram Maruga, M.H.A. - Children's National Hospital; Jessica Herstek, MD - Children's National Hospital;
Poster Number: P100
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Data Sharing, Usability, Information Visualization, User-centered Design Methods
Primary Track: Applications
Several electronic health record (EHR) metrics are correlated to provider burnout and job dissatisfaction rates. Enterprise-wide end user metric data review is important to understand EHR burdens and benefits. Cerner Advance (CA) and Lights On Network (LON) metrics provide end user data analytics with limitations on how data can be filtered, disseminated, and viewed. Using HealtheIntent and Tableau analytics a dynamic EHR utilization dashboard was created to present this data in a user friendly format.
Speaker:
Stacey Stokes, MD, MPH
Children's National Hospital
Authors:
Geetanjali Vashist, M.S. - Children's National Hospital; Parissa Safari, M.H.A. - Children's National Hospital; Ranjodh Badh, B.S. - Children's National Hospital; Shivaram Maruga, M.H.A. - Children's National Hospital; Jessica Herstek, MD - Children's National Hospital;
Stacey
Stokes,
MD, MPH - Children's National Hospital
Variable importance cloud for medical image analysis
Poster Number: P97
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Deep Learning, Artificial Intelligence, Clinical Decision Support
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Explainable artificial intelligence (XAI) is critical for developing predictive models in healthcare. Traditional methods focus on a single optimized model, overlooking model uncertainty. We propose VIC-image, a robust ensemble-based XAI method tailored to medical image analysis that leverages nearly-optimal models each with Grad-CAM++ applied. Evaluated on the HAM10000 dataset, VIC-Image outperformed Grad-CAM++ with 6.40% higher IoU and 5.29% higher DSC, better aligning with physician annotations. VIC-image enhances robustness and trustworthiness in medical AI applications.
Speaker:
Mingxuan Liu, PhD student
Duke-NUS Medical School
Authors:
Yilin Ning, PhD; Nan Liu, PhD - National University of Singapore;
Poster Number: P97
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Deep Learning, Artificial Intelligence, Clinical Decision Support
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Explainable artificial intelligence (XAI) is critical for developing predictive models in healthcare. Traditional methods focus on a single optimized model, overlooking model uncertainty. We propose VIC-image, a robust ensemble-based XAI method tailored to medical image analysis that leverages nearly-optimal models each with Grad-CAM++ applied. Evaluated on the HAM10000 dataset, VIC-Image outperformed Grad-CAM++ with 6.40% higher IoU and 5.29% higher DSC, better aligning with physician annotations. VIC-image enhances robustness and trustworthiness in medical AI applications.
Speaker:
Mingxuan Liu, PhD student
Duke-NUS Medical School
Authors:
Yilin Ning, PhD; Nan Liu, PhD - National University of Singapore;
Mingxuan
Liu,
PhD student - Duke-NUS Medical School
PhenoGnet: A Graph-Based Contrastive Learning Framework for Disease Similarity Prediction
Poster Number: P96
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Deep Learning, Machine Learning, Controlled Terminologies, Ontologies, and Vocabularies, Bioinformatics
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Understanding disease similarity is beneficial in biomedical research, aiding in diagnosis, drug discovery, and treatment planning. We propose PhenoGnet, a novel framework integrating gene interaction networks and the Human Phenotype Ontology (HPO) using graph neural networks. PhenoGnet employs contrastive learning to align gene and phenotype representations based on associations, enhancing disease similarity prediction. Evaluated on expert-curated datasets, PhenoGnet achieves high predictive performance, demonstrating its potential for knowledge discovery, particularly in rare disease research.
Speaker:
Ranga Baminiwatte, PhD Candidate
Clemson University
Authors:
Kazi Rana, PhD Student - Clemson University; Aaron Masino, PhD - Clemson University;
Poster Number: P96
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Deep Learning, Machine Learning, Controlled Terminologies, Ontologies, and Vocabularies, Bioinformatics
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Understanding disease similarity is beneficial in biomedical research, aiding in diagnosis, drug discovery, and treatment planning. We propose PhenoGnet, a novel framework integrating gene interaction networks and the Human Phenotype Ontology (HPO) using graph neural networks. PhenoGnet employs contrastive learning to align gene and phenotype representations based on associations, enhancing disease similarity prediction. Evaluated on expert-curated datasets, PhenoGnet achieves high predictive performance, demonstrating its potential for knowledge discovery, particularly in rare disease research.
Speaker:
Ranga Baminiwatte, PhD Candidate
Clemson University
Authors:
Kazi Rana, PhD Student - Clemson University; Aaron Masino, PhD - Clemson University;
Ranga
Baminiwatte,
PhD Candidate - Clemson University
Identifying Pulmonary Embolism from Radiology Reports Using Large Language Models
Poster Number: P95
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Deep Learning, Natural Language Processing, Artificial Intelligence, Large Language Models (LLMs), Data Mining, Critical Care, Data Standards, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Pulmonary embolism (PE) detection in radiology reports is critical for timely care. We evaluate Llama 3.1 8B and GPT-4o large language models using zero-shot, few-shot, and fine-tuned strategies on the MIMIC-IV-Ext-PE dataset. GPT-4o achieved 99.3% accuracy and 95.75% F1-score, outperforming all Llama variants. Our results highlight the potential of LLMs to automate PE identification, reducing reliance on manual chart review.
Speaker:
Farzad Ahmed, Ph.D. Student in Computer Science
George Mason University
Authors:
Barbara Lam, MD - University of Washington; Ozlem Uzuner, PhD - George Mason University; Meliha Yetisgen, PhD - University of Washington;
Poster Number: P95
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Deep Learning, Natural Language Processing, Artificial Intelligence, Large Language Models (LLMs), Data Mining, Critical Care, Data Standards, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Pulmonary embolism (PE) detection in radiology reports is critical for timely care. We evaluate Llama 3.1 8B and GPT-4o large language models using zero-shot, few-shot, and fine-tuned strategies on the MIMIC-IV-Ext-PE dataset. GPT-4o achieved 99.3% accuracy and 95.75% F1-score, outperforming all Llama variants. Our results highlight the potential of LLMs to automate PE identification, reducing reliance on manual chart review.
Speaker:
Farzad Ahmed, Ph.D. Student in Computer Science
George Mason University
Authors:
Barbara Lam, MD - University of Washington; Ozlem Uzuner, PhD - George Mason University; Meliha Yetisgen, PhD - University of Washington;
Farzad
Ahmed,
Ph.D. Student in Computer Science - George Mason University
Transfer Learning Enhances Outcome Prediction for Out-of-Hospital Cardiac Arrest: Validation Across Diverse Geographic Contexts
Poster Number: P94
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Diversity, Equity, Inclusion, and Accessibility (DEIA), Fairness and elimination of bias, Privacy and Security, Health Equity, Artificial Intelligence, Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Accurate risk stratification is vital in clinical care but challenging in low-resource settings due to limited data. This study applies transfer learning (TL) to adapt a neurological outcome prediction model for out-of-hospital cardiac arrest (OHCA) from a Japanese cohort to Vietnam (243 patients) and Singapore (15,916 patients). TL significantly improved performance in Vietnam (AUROC: 0.807 vs. 0.467), demonstrating its potential as a scalable solution for enhancing clinical models in data-limited settings.
Speaker:
Siqi Li, Bachelor of Science
Duke-NUS Medical School
Authors:
Yohei Okada, PhD, MD - Duke-NUS Medical School; Nan Liu, PhD - National University of Singapore;
Poster Number: P94
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Diversity, Equity, Inclusion, and Accessibility (DEIA), Fairness and elimination of bias, Privacy and Security, Health Equity, Artificial Intelligence, Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Accurate risk stratification is vital in clinical care but challenging in low-resource settings due to limited data. This study applies transfer learning (TL) to adapt a neurological outcome prediction model for out-of-hospital cardiac arrest (OHCA) from a Japanese cohort to Vietnam (243 patients) and Singapore (15,916 patients). TL significantly improved performance in Vietnam (AUROC: 0.807 vs. 0.467), demonstrating its potential as a scalable solution for enhancing clinical models in data-limited settings.
Speaker:
Siqi Li, Bachelor of Science
Duke-NUS Medical School
Authors:
Yohei Okada, PhD, MD - Duke-NUS Medical School; Nan Liu, PhD - National University of Singapore;
Siqi
Li,
Bachelor of Science - Duke-NUS Medical School
Using Big Data to Assess Social and Individual Factors Associated with Pediatric Essential Hypertension: A Cosmos Database Study
Poster Number: P93
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Diversity, Equity, Inclusion, and Accessibility (DEIA), Racial disparities, Health Equity, Pediatrics, Public Health
Primary Track: Policy
Programmatic Theme: Public Health Informatics
This study aims to leverage large-scale data analytics to elucidate the relationship between individual, social, and economic factors in pediatric HTN, utilizing the largest database study to date. We identified 148,581 patients with HTN and 16,973,958 control patients. Patients with HTN were more likely to be obese, live in rural areas, and be black or multiracial. Our findings identify specific risk factors for hypertension and underscore the potential for targeted mitigation efforts.
Speaker:
Zachary West, MD
Children's Healthcare of Atlanta
Authors:
Zachary West, MD - Children's Healthcare of Atlanta; Paula Patel, MD - Emory University/Children's Healthcare of Atlanta; Yaw Owusu, MD - Emory University/Children's Healthcare of Atlanta; Andrew Jergel, MPH - Emory University; Hui Huang, MS - Emory University; Michelle Wallace, MD - Emory University/Children's Healthcare of Atlanta;
Poster Number: P93
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Diversity, Equity, Inclusion, and Accessibility (DEIA), Racial disparities, Health Equity, Pediatrics, Public Health
Primary Track: Policy
Programmatic Theme: Public Health Informatics
This study aims to leverage large-scale data analytics to elucidate the relationship between individual, social, and economic factors in pediatric HTN, utilizing the largest database study to date. We identified 148,581 patients with HTN and 16,973,958 control patients. Patients with HTN were more likely to be obese, live in rural areas, and be black or multiracial. Our findings identify specific risk factors for hypertension and underscore the potential for targeted mitigation efforts.
Speaker:
Zachary West, MD
Children's Healthcare of Atlanta
Authors:
Zachary West, MD - Children's Healthcare of Atlanta; Paula Patel, MD - Emory University/Children's Healthcare of Atlanta; Yaw Owusu, MD - Emory University/Children's Healthcare of Atlanta; Andrew Jergel, MPH - Emory University; Hui Huang, MS - Emory University; Michelle Wallace, MD - Emory University/Children's Healthcare of Atlanta;
Zachary
West,
MD - Children's Healthcare of Atlanta
Leveraging Large Language Models to Develop Automatic AI System for Real-time Feedback in Healthcare Education
Poster Number: P92
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Education and Training, Teaching Innovation, Large Language Models (LLMs), Usability
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
Effective communication is critical for nurse-led innovation, yet nursing curricula often lack such training. This study developed an AI system using Large Language Models to provide real-time feedback on nursing students' elevator pitches. We analyzed 410 pitch scripts with GPT-3.5 and GPT-4o-mini, comparing fine-tuned and few-shot approaches. GPT-4o-mini achieved the lowest RMSE (2.81), with students rating feedback accuracy at 84–97%. Findings show LLMs can deliver objective, scalable feedback, enhancing nursing education without extensive fine-tuning.
Speaker:
Shaowei GUAN, Bachelor
The Hong Kong Polytechnic University
Authors:
Vivian Hui, RN, PhD - The Hong Kong Polytechnic University; Xinyu Feng, Master - The Hong Kong Polytechnic University; Kitty CHAN, PhD - The Hong Kong Polytechnic University;
Poster Number: P92
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Education and Training, Teaching Innovation, Large Language Models (LLMs), Usability
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
Effective communication is critical for nurse-led innovation, yet nursing curricula often lack such training. This study developed an AI system using Large Language Models to provide real-time feedback on nursing students' elevator pitches. We analyzed 410 pitch scripts with GPT-3.5 and GPT-4o-mini, comparing fine-tuned and few-shot approaches. GPT-4o-mini achieved the lowest RMSE (2.81), with students rating feedback accuracy at 84–97%. Findings show LLMs can deliver objective, scalable feedback, enhancing nursing education without extensive fine-tuning.
Speaker:
Shaowei GUAN, Bachelor
The Hong Kong Polytechnic University
Authors:
Vivian Hui, RN, PhD - The Hong Kong Polytechnic University; Xinyu Feng, Master - The Hong Kong Polytechnic University; Kitty CHAN, PhD - The Hong Kong Polytechnic University;
Shaowei
GUAN,
Bachelor - The Hong Kong Polytechnic University
EHR Use and Academic Performance Among General Surgery Residents: An Exploratory Analysis Using Clustering and Audit Logs
Poster Number: P91
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Education and Training, Workflow, Machine Learning
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
Using a machine learning approach, electronic health record (EHR) audit logs of general surgery residents were clustered to identify workflow patterns and evaluate associations with resident academic performance. While no significant associations were found, this study serves as an introductory approach to evaluating EHR workflow efficiency and performance for surgical trainees. Future work will involve supervised machine learning approaches and expand on current methods with additional log patterns and continued refinement of resident performance scoring.
Speaker:
Catherine Pratt, MD
University of Cincinnati College of Medicine
Authors:
Catherine Pratt, MD - University of Cincinnati College of Medicine; Roxy Huang, MS - University of North Carolina at Chapel Hill; Suguna Kotte, Pharm - University of North Carolina at Chapel Hill; Ralph Culter Quillin, MD - University of Cincinnati College of Medicine; Danny Wu, PhD - University of North Carolina at Chapel Hill;
Poster Number: P91
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Education and Training, Workflow, Machine Learning
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
Using a machine learning approach, electronic health record (EHR) audit logs of general surgery residents were clustered to identify workflow patterns and evaluate associations with resident academic performance. While no significant associations were found, this study serves as an introductory approach to evaluating EHR workflow efficiency and performance for surgical trainees. Future work will involve supervised machine learning approaches and expand on current methods with additional log patterns and continued refinement of resident performance scoring.
Speaker:
Catherine Pratt, MD
University of Cincinnati College of Medicine
Authors:
Catherine Pratt, MD - University of Cincinnati College of Medicine; Roxy Huang, MS - University of North Carolina at Chapel Hill; Suguna Kotte, Pharm - University of North Carolina at Chapel Hill; Ralph Culter Quillin, MD - University of Cincinnati College of Medicine; Danny Wu, PhD - University of North Carolina at Chapel Hill;
Catherine
Pratt,
MD - University of Cincinnati College of Medicine
Interactive Clinical Utility Decision Analytic (iCUDA) Dashboard – Sensitivity Analysis of Predictive Model Performance and Clinical Utility
Poster Number: P90
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Evaluation, Information Visualization, Machine Learning, Diagnostic Systems, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
The Interactive Clinical Utility Decision Analytic (iCUDA) dashboard is an open-source and hosted tool (URL) designed to assess a predictive model's value for local use. It enables users to quantify expected treatment harms and benefits under local disease prevalence scenarios and tradeoffs. Through interactive sliders, users can explore model cut-off points (empiric and ideal), expected utility, partial areas under the ROC curve, and prevalence and tradeoff ranges making the model useful.
Speaker:
Star Liu, M.S. in Biomedical Informatics and Data Science
Johns Hopkins School of Medicine - Biomedical Informatics and Data Science
Author:
Harold Lehmann, MD, PhD - Johns Hopkins University;
Poster Number: P90
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Evaluation, Information Visualization, Machine Learning, Diagnostic Systems, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
The Interactive Clinical Utility Decision Analytic (iCUDA) dashboard is an open-source and hosted tool (URL) designed to assess a predictive model's value for local use. It enables users to quantify expected treatment harms and benefits under local disease prevalence scenarios and tradeoffs. Through interactive sliders, users can explore model cut-off points (empiric and ideal), expected utility, partial areas under the ROC curve, and prevalence and tradeoff ranges making the model useful.
Speaker:
Star Liu, M.S. in Biomedical Informatics and Data Science
Johns Hopkins School of Medicine - Biomedical Informatics and Data Science
Author:
Harold Lehmann, MD, PhD - Johns Hopkins University;
Star
Liu,
M.S. in Biomedical Informatics and Data Science - Johns Hopkins School of Medicine - Biomedical Informatics and Data Science
A Framework for Evaluating and Improving Metadata Quality in the RADx Data Hub Using CEDAR Templates
Poster Number: P89
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Evaluation, Workflow, Fairness and elimination of bias, Data Standards, Controlled Terminologies, Ontologies, and Vocabularies, Interoperability and Health Information Exchange, Knowledge Representation and Information Modeling, Data Sharing
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Metadata quality is essential for FAIR data, but evaluation is time-consuming. We present a semi-automated framework that uses CEDAR templates, encoding expected metadata structure and values, to assess metadata validity, completeness, consistency, accuracy, accessibility, uniqueness, and linguistic quality. Applied in the RADx Data Hub, it identified 586 issues from 181 studies, improving metadata quality and data FAIRness. Our framework supports domain adaptability through custom CEDAR metadata templates alongside pluggable evaluation criteria.
Speaker:
Yan Cao, M.S.
Stanford Center for Biomedical Informatics Research
Authors:
Marcos Martinez-Romero, PhD - Stanford University; Mark Musen, MD, PhD - Stanford University; Matthew Horridge, PhD - Stanford University; Luna Baalbaki, MS - Stanford University; Crystal Han, MS - Stanford University;
Poster Number: P89
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Evaluation, Workflow, Fairness and elimination of bias, Data Standards, Controlled Terminologies, Ontologies, and Vocabularies, Interoperability and Health Information Exchange, Knowledge Representation and Information Modeling, Data Sharing
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Metadata quality is essential for FAIR data, but evaluation is time-consuming. We present a semi-automated framework that uses CEDAR templates, encoding expected metadata structure and values, to assess metadata validity, completeness, consistency, accuracy, accessibility, uniqueness, and linguistic quality. Applied in the RADx Data Hub, it identified 586 issues from 181 studies, improving metadata quality and data FAIRness. Our framework supports domain adaptability through custom CEDAR metadata templates alongside pluggable evaluation criteria.
Speaker:
Yan Cao, M.S.
Stanford Center for Biomedical Informatics Research
Authors:
Marcos Martinez-Romero, PhD - Stanford University; Mark Musen, MD, PhD - Stanford University; Matthew Horridge, PhD - Stanford University; Luna Baalbaki, MS - Stanford University; Crystal Han, MS - Stanford University;
Yan
Cao,
M.S. - Stanford Center for Biomedical Informatics Research
Predicting breast cancer survival with fairness-aware and interpretable machine learning
Poster Number: P88
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Fairness and Elimination of Bias, Artificial Intelligence, Clinical Decision Support
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Ensuring fairness in survival analysis models is critical, particularly in breast cancer prognosis, where disparities exist regarding race, marital status, and socioeconomics. We propose FAIM-Surv, a fairness-aware and interpretable framework that mitigates biases in survival models while preserving predictive performance. Using the SEER database, FAIM-Surv improves both performance-disparity-based and ranking-based fairness metrics by 31.5%–48.4%. By balancing performance and fairness, this approach advances the development of fair survival models in oncology.
Speaker:
Mingxuan Liu, PhD student
Duke-NUS Medical School
Authors:
Mingxuan Liu, PhD student - Duke-NUS Medical School; Yilin Ning, PhD; Danielle Bitterman, MD - Harvard Medical School; William La Cava, PhD - Boston Children's Hospital / Harvard Medical School; Nan Liu, PhD - National University of Singapore;
Poster Number: P88
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Fairness and Elimination of Bias, Artificial Intelligence, Clinical Decision Support
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Ensuring fairness in survival analysis models is critical, particularly in breast cancer prognosis, where disparities exist regarding race, marital status, and socioeconomics. We propose FAIM-Surv, a fairness-aware and interpretable framework that mitigates biases in survival models while preserving predictive performance. Using the SEER database, FAIM-Surv improves both performance-disparity-based and ranking-based fairness metrics by 31.5%–48.4%. By balancing performance and fairness, this approach advances the development of fair survival models in oncology.
Speaker:
Mingxuan Liu, PhD student
Duke-NUS Medical School
Authors:
Mingxuan Liu, PhD student - Duke-NUS Medical School; Yilin Ning, PhD; Danielle Bitterman, MD - Harvard Medical School; William La Cava, PhD - Boston Children's Hospital / Harvard Medical School; Nan Liu, PhD - National University of Singapore;
Mingxuan
Liu,
PhD student - Duke-NUS Medical School
Exposure and Control matching in Electronic Health Record Studies using Grouped Marching
Poster Number: P87
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Fairness and Elimination of Bias, Causal Inference, Quantitative Methods
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Control selection for COVID cases is confounded by requiring a negative test. Instead, we propose using a routine care enrollment event for all patients; we match a control to each case in increasing time from enrollment to exposure, blind to future events. Negative controls, specifically pap smears and non-pathological fractures, have a reduced or non-significant difference using grouped marching to find controls.
Speaker:
Margaret Hall, MS
Emory University
Authors:
Richard Moffitt, Ph.D. - Emory University; Daniel Brannock, MS - RTI International;
Poster Number: P87
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Fairness and Elimination of Bias, Causal Inference, Quantitative Methods
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Control selection for COVID cases is confounded by requiring a negative test. Instead, we propose using a routine care enrollment event for all patients; we match a control to each case in increasing time from enrollment to exposure, blind to future events. Negative controls, specifically pap smears and non-pathological fractures, have a reduced or non-significant difference using grouped marching to find controls.
Speaker:
Margaret Hall, MS
Emory University
Authors:
Richard Moffitt, Ph.D. - Emory University; Daniel Brannock, MS - RTI International;
Margaret
Hall,
MS - Emory University
Informative Missingness in EHR Labs: A Retrospective Study of Biomarker Trajectories Following SARS-CoV-2 Infection
Poster Number: P86
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Fairness and Elimination of Bias, Laboratory Systems and Reporting, Machine Learning, Real-World Evidence Generation, Infectious Diseases and Epidemiology
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study analyzes electronic health records (EHR) to track biomarker changes following SARS-CoV-2 infection, focusing on informed missingness to enhance clinical prediction models for Long COVID. Using the electronic health records data from National COVID Cohort Collaborative, it examines lymphocyte levels as predictors of Long COVID through multivariate regression. The findings suggest that including missing data deliberately can improve accuracy and clinical applicability.
Speaker:
Saaya Patel, B.S.
Emory University
Authors:
Saaya Patel, B.S. - Emory University; Richard Moffitt, Ph.D. - Emory University;
Poster Number: P86
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Fairness and Elimination of Bias, Laboratory Systems and Reporting, Machine Learning, Real-World Evidence Generation, Infectious Diseases and Epidemiology
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study analyzes electronic health records (EHR) to track biomarker changes following SARS-CoV-2 infection, focusing on informed missingness to enhance clinical prediction models for Long COVID. Using the electronic health records data from National COVID Cohort Collaborative, it examines lymphocyte levels as predictors of Long COVID through multivariate regression. The findings suggest that including missing data deliberately can improve accuracy and clinical applicability.
Speaker:
Saaya Patel, B.S.
Emory University
Authors:
Saaya Patel, B.S. - Emory University; Richard Moffitt, Ph.D. - Emory University;
Saaya
Patel,
B.S. - Emory University
Using Reweighting to Reduce Bias in Adverse Event Prediction Models
Poster Number: P85
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Fairness and Elimination of Bias, Machine Learning, Health Equity, Artificial Intelligence, Quantitative Methods, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Algorithmic bias significantly impacts scientific research, particularly in biomedicine, where it may lead to harmful treatments and outcomes for historically disadvantaged groups. One approach to mitigating bias in machine learning pipelines is reweighting, which assigns different weights to training data points to enhance the representation of underrepresented individuals. In this study, we introduce a novel model that employs reweighting to reduce racial disparities in predicting adverse events. We compare its performance against a baseline model and a proprietary model currently used in hospitals. Our findings suggest that a diverse training dataset can achieve high model accuracy without requiring additional fairness interventions.
Speaker:
Amalia Ionescu, PhD
Cedars-Sinai
Authors:
Amalia Ionescu, PhD - Cedars-Sinai; Emily Wong, PhD - Cedars-Sinai Medical Center; Priyanka Merchant, MS - Cedars-Sinai Medical Center; Nicole Beyrouthy, MS, RN-BC - Cedars-Sinai Medical Center; Michael Stange, BS - Cedars-Sinai Medical Center; Matthew Moore, MS - Cedars-Sinai Medical Center; Andrew Hudson, MD - Cedars-Sinai Medical Center; Tiffani Bright, PhD - Cedars-Sinai Medical Center;
Poster Number: P85
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Fairness and Elimination of Bias, Machine Learning, Health Equity, Artificial Intelligence, Quantitative Methods, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Algorithmic bias significantly impacts scientific research, particularly in biomedicine, where it may lead to harmful treatments and outcomes for historically disadvantaged groups. One approach to mitigating bias in machine learning pipelines is reweighting, which assigns different weights to training data points to enhance the representation of underrepresented individuals. In this study, we introduce a novel model that employs reweighting to reduce racial disparities in predicting adverse events. We compare its performance against a baseline model and a proprietary model currently used in hospitals. Our findings suggest that a diverse training dataset can achieve high model accuracy without requiring additional fairness interventions.
Speaker:
Amalia Ionescu, PhD
Cedars-Sinai
Authors:
Amalia Ionescu, PhD - Cedars-Sinai; 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;
Amalia
Ionescu,
PhD - Cedars-Sinai
Tracking Human Mobility and Behavior During Pandemics: A GPS-Based Analysis of Movement Patterns and Social Distancing
Poster Number: P84
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Geospatial (GIS) Data/Analysis, Public Health, Infectious Diseases and Epidemiology, Data Mining
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This study examines mobility patterns during COVID-19 using GPS data from 196 participants, generating over 232 million data points. Data preprocessing included filtering invalid locations, noise reduction, and matching with SafeGraph POIs. A 250-meter radius was optimal for visit detection. Findings highlight key locations visited, aiding public health efforts. The study demonstrates GPS-based mobility tracking’s potential for understanding disease transmission patterns and adherence to policy.
Speaker:
Sri Surya Krishna Rama Taraka Naren Durbha, Master's in Health Informatics
George Mason University
Author:
Janusz Wojtusiak, PhD - George Mason University;
Poster Number: P84
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Geospatial (GIS) Data/Analysis, Public Health, Infectious Diseases and Epidemiology, Data Mining
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This study examines mobility patterns during COVID-19 using GPS data from 196 participants, generating over 232 million data points. Data preprocessing included filtering invalid locations, noise reduction, and matching with SafeGraph POIs. A 250-meter radius was optimal for visit detection. Findings highlight key locations visited, aiding public health efforts. The study demonstrates GPS-based mobility tracking’s potential for understanding disease transmission patterns and adherence to policy.
Speaker:
Sri Surya Krishna Rama Taraka Naren Durbha, Master's in Health Informatics
George Mason University
Author:
Janusz Wojtusiak, PhD - George Mason University;
Sri Surya Krishna Rama Taraka Naren
Durbha,
Master's in Health Informatics - George Mason University
Digital Voices: A Comprehensive Analysis of Google Reviews in Rural High- and Low-Health Ranked Counties
Poster Number: P81
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Natural Language Processing, Social Media and Connected Health, Data Mining
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This study investigates the correlation between county health rankings and Google review scores of rural Eastern North Carolina healthcare facilities. Using county health status and extracted Google review data, the research compares average review scores between high- and low-ranked counties. Preliminary results suggest potential disparities in perceived healthcare quality. Further analysis will employ natural language processing to understand patient sentiment and inform strategies for improving healthcare experiences in underserved areas.
Speaker:
Carlos Perez-Aldana, PhD
ECU
Author:
Ashley Burch, PhD - ECU;
Poster Number: P81
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Natural Language Processing, Social Media and Connected Health, Data Mining
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This study investigates the correlation between county health rankings and Google review scores of rural Eastern North Carolina healthcare facilities. Using county health status and extracted Google review data, the research compares average review scores between high- and low-ranked counties. Preliminary results suggest potential disparities in perceived healthcare quality. Further analysis will employ natural language processing to understand patient sentiment and inform strategies for improving healthcare experiences in underserved areas.
Speaker:
Carlos Perez-Aldana, PhD
ECU
Author:
Ashley Burch, PhD - ECU;
Carlos
Perez-Aldana,
PhD - ECU
Adaptation of IHI Triple Aim Framework to Reflect Need for Improved Documentation in Adverse Contrast Reactions
Poster Number: P80
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Patient Engagement and Preferences, Patient Safety
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Documentation of adverse contrast reactions (ACR’s) in radiology departments is non-standardized and sometimes subpar or incomplete. ACR’s have been a rising concern with the ever-increasing number of medical imaging especially in the setting of cancer diagnosis and treatment. Quality documentation of these events has been shown to improve management programs for patients undergoing additional imaging. We created a framework that promotes consistent better-quality adverse event documentation.
Speaker:
Evan Ratkus, Bachelor of Science
University of South Florida
Authors:
Cesar Lam, MD - H. Lee Moffitt Cancer Center & Research Institute; James Andrews, PhD, MLIS, FAMIA - University of South Florida, School of Information; Isabella Mouradian, Bachelor of Science - University of South Florida; Christina Eldredge, MD, PhD, FAMIA - University of South Florida;
Poster Number: P80
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Patient Engagement and Preferences, Patient Safety
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Documentation of adverse contrast reactions (ACR’s) in radiology departments is non-standardized and sometimes subpar or incomplete. ACR’s have been a rising concern with the ever-increasing number of medical imaging especially in the setting of cancer diagnosis and treatment. Quality documentation of these events has been shown to improve management programs for patients undergoing additional imaging. We created a framework that promotes consistent better-quality adverse event documentation.
Speaker:
Evan Ratkus, Bachelor of Science
University of South Florida
Authors:
Cesar Lam, MD - H. Lee Moffitt Cancer Center & Research Institute; James Andrews, PhD, MLIS, FAMIA - University of South Florida, School of Information; Isabella Mouradian, Bachelor of Science - University of South Florida; Christina Eldredge, MD, PhD, FAMIA - University of South Florida;
Evan
Ratkus,
Bachelor of Science - University of South Florida
Examining the Impact of Healthcare Team Effectiveness on Mortality Outcomes of Heart Failure Patients Using EHR Note Social Network Analysis
Poster Number: P79
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Patient Safety, Quantitative Methods
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
High-quality teamwork has been established as a major contributor to patient safety, but resource-intensive measurement approaches present a barrier to evaluating teamwork interventions, particularly in cross-institutional settings. To measure teamwork, we adapted an algorithm to unobtrusively measure aspects of teamwork using electronic health record note metadata. Analyses indicated that increased shared team experience is associated with lower ICU mortality in patients with heart failure, after controlling for clinical and demographic characteristics.
Speaker:
Claire Layton, MS
University of Florida
Authors:
Miad Alfaqih, Phd - University of Florida; Nicole Hammer, B.A. - University of Florida - Department of Health Outcomes and Biomedical Informatics; Henry Philofsky, MD - University of Rochester Medical Center; David Vawdrey, PhD - Geisinger; Grant DeLong, BA - Geisinger; Megan Gregory, Ph.D. - University of Florida;
Poster Number: P79
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Patient Safety, Quantitative Methods
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
High-quality teamwork has been established as a major contributor to patient safety, but resource-intensive measurement approaches present a barrier to evaluating teamwork interventions, particularly in cross-institutional settings. To measure teamwork, we adapted an algorithm to unobtrusively measure aspects of teamwork using electronic health record note metadata. Analyses indicated that increased shared team experience is associated with lower ICU mortality in patients with heart failure, after controlling for clinical and demographic characteristics.
Speaker:
Claire Layton, MS
University of Florida
Authors:
Miad Alfaqih, Phd - University of Florida; Nicole Hammer, B.A. - University of Florida - Department of Health Outcomes and Biomedical Informatics; Henry Philofsky, MD - University of Rochester Medical Center; David Vawdrey, PhD - Geisinger; Grant DeLong, BA - Geisinger; Megan Gregory, Ph.D. - University of Florida;
Claire
Layton,
MS - University of Florida
Building a Healthcare Data Science Platform in the Cloud
Poster Number: P78
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Privacy and Security, Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This case study details the Geisinger AI Lab's cloud-based data science platform, designed for portability, scalability, and security. By using open-source tools and serverless architectures, the platform ensures flexibility, efficient resource use, and HIPAA compliance. We offer strategies and best practices for leveraging cloud computing in healthcare data science.
Speaker:
Grant DeLong, BA
Geisinger
Authors:
Elliot Mitchell, PhD - Geisinger; Grant DeLong, BA - Geisinger;
Poster Number: P78
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Privacy and Security, Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This case study details the Geisinger AI Lab's cloud-based data science platform, designed for portability, scalability, and security. By using open-source tools and serverless architectures, the platform ensures flexibility, efficient resource use, and HIPAA compliance. We offer strategies and best practices for leveraging cloud computing in healthcare data science.
Speaker:
Grant DeLong, BA
Geisinger
Authors:
Elliot Mitchell, PhD - Geisinger; Grant DeLong, BA - Geisinger;
Grant
DeLong,
BA - Geisinger
Addressing healthcare disparities for Arab immigrants and refugees in the U.S.
Poster Number: P77
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Qualitative Methods, Patient Engagement and Preferences
Working Group: Student Working Group
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Every year, countless Arab immigrants and refugees arrive in the United States, seeking safety, stability, and a better future. From navigating complex insurance policies to overcoming cultural and linguistic barriers, they often struggle to access the care they need. The Galileo Project aims to design the perfect health system, by identifying gaps in current healthcare policies, enhancing cultural competency among providers, and leveraging technology to bridge communication barriers.
Speaker:
Carine Yehya, MMCi
Duke University School of Medicine
Authors:
William Hammond, PhD - Duke Center for Health Informatics; Elizabeth Thermos, B.S. - Duke University; Marina Yacoub, MMCI - Duke University School of Medicine;
Poster Number: P77
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Qualitative Methods, Patient Engagement and Preferences
Working Group: Student Working Group
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Every year, countless Arab immigrants and refugees arrive in the United States, seeking safety, stability, and a better future. From navigating complex insurance policies to overcoming cultural and linguistic barriers, they often struggle to access the care they need. The Galileo Project aims to design the perfect health system, by identifying gaps in current healthcare policies, enhancing cultural competency among providers, and leveraging technology to bridge communication barriers.
Speaker:
Carine Yehya, MMCi
Duke University School of Medicine
Authors:
William Hammond, PhD - Duke Center for Health Informatics; Elizabeth Thermos, B.S. - Duke University; Marina Yacoub, MMCI - Duke University School of Medicine;
Carine
Yehya,
MMCi - Duke University School of Medicine
Second Signs and Wrong Patient Orders–a Natural Experiment
Poster Number: P76
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Quantitative Methods, Clinical Decision Support
Primary Track: Applications
We analyzed data from our institution to determine whether the implementation of a “re-enter signature” workflow for opioid orders led to a reduction in wrong patient orders as measured by retract and resubmission rates. In an interrupted time series analysis, we found that implementation of the 2nd order verification had no significant impact on retraction and resubmission rates for opioids orders, either on their own or in comparison to the control of non-opioid orders.
Speaker:
Sophia Hsu, High School Diploma
University of California, San Francisco
Authors:
Sophia Hsu, High School Diploma - University of California, San Francisco; Raman Khanna, MD, MAS - University of California, San Francisco;
Poster Number: P76
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Healthcare Quality, Quantitative Methods, Clinical Decision Support
Primary Track: Applications
We analyzed data from our institution to determine whether the implementation of a “re-enter signature” workflow for opioid orders led to a reduction in wrong patient orders as measured by retract and resubmission rates. In an interrupted time series analysis, we found that implementation of the 2nd order verification had no significant impact on retraction and resubmission rates for opioids orders, either on their own or in comparison to the control of non-opioid orders.
Speaker:
Sophia Hsu, High School Diploma
University of California, San Francisco
Authors:
Sophia Hsu, High School Diploma - University of California, San Francisco; Raman Khanna, MD, MAS - University of California, San Francisco;
Sophia
Hsu,
High School Diploma - University of California, San Francisco
Eye Tracking to Measure Cognitive Workload During EHR Use
Poster Number: P75
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Human-computer Interaction, Usability, Clinical Decision Support, Evaluation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
We used eye tracking to measure cognitive workload while using EHR. Participants (N=17) underwent 3 simulated tasks related to prescribing antibiotics. Eye-tracking data indicated 85 area of interests (AOI). The top 20% of AOIs held 80% of the attention. We also identified differences across scenarios and participants. Eye tracking is a feasible way to measure cognitive workload. AI applications could personalize EHR, reduce cognitive load associated with common tasks and provide suggestions on automated entry.
Speaker:
Mustafa Ozkaynak, PhD
University of Colorado-Denver | Anschutz Medical Campus
Authors:
Steve Conrad, PhD - Colorado State University; Cristian Sarabia, MPH; Josh Rodriguez, MS - Colorado State University; Michelle Vasquez, Student - Colorado State University; Steve Simske, PhD - Colorado State University; Joseph Grubenhoff, MD - Childrens Hospital Colorado;
Poster Number: P75
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Human-computer Interaction, Usability, Clinical Decision Support, Evaluation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
We used eye tracking to measure cognitive workload while using EHR. Participants (N=17) underwent 3 simulated tasks related to prescribing antibiotics. Eye-tracking data indicated 85 area of interests (AOI). The top 20% of AOIs held 80% of the attention. We also identified differences across scenarios and participants. Eye tracking is a feasible way to measure cognitive workload. AI applications could personalize EHR, reduce cognitive load associated with common tasks and provide suggestions on automated entry.
Speaker:
Mustafa Ozkaynak, PhD
University of Colorado-Denver | Anschutz Medical Campus
Authors:
Steve Conrad, PhD - Colorado State University; Cristian Sarabia, MPH; Josh Rodriguez, MS - Colorado State University; Michelle Vasquez, Student - Colorado State University; Steve Simske, PhD - Colorado State University; Joseph Grubenhoff, MD - Childrens Hospital Colorado;
Mustafa
Ozkaynak,
PhD - University of Colorado-Denver | Anschutz Medical Campus
Longitudinal Patient Journey Mapping to Inform the Design of Technology to Support Breast Cancer Patients
Poster Number: P74
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Human-computer Interaction, Workflow, Information Visualization, Usability
Primary Track: Foundations
Programmatic Theme: Consumer Health Informatics
This study applies a SEIPS framework-based patient journey mapping technique to analyze the interactions between breast cancer patients and the healthcare system, focusing on technology use. Researchers observed 12 patients, 3 longitudinally, coding for macrocognitive processes, barriers, and facilitators. These maps revealed how technology use intersected with clinical processes across stakeholders. This novel mapping approach offers insights into patient experiences and digital processes, informing future system improvements to address information gaps in cancer care delivery.
Speaker:
Uday Suresh, MS
Vanderbilt University Department of Biomedical Informatics
Author:
Megan Salwei, PhD - Vanderbilt University Medical Center;
Poster Number: P74
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Human-computer Interaction, Workflow, Information Visualization, Usability
Primary Track: Foundations
Programmatic Theme: Consumer Health Informatics
This study applies a SEIPS framework-based patient journey mapping technique to analyze the interactions between breast cancer patients and the healthcare system, focusing on technology use. Researchers observed 12 patients, 3 longitudinally, coding for macrocognitive processes, barriers, and facilitators. These maps revealed how technology use intersected with clinical processes across stakeholders. This novel mapping approach offers insights into patient experiences and digital processes, informing future system improvements to address information gaps in cancer care delivery.
Speaker:
Uday Suresh, MS
Vanderbilt University Department of Biomedical Informatics
Author:
Megan Salwei, PhD - Vanderbilt University Medical Center;
Uday
Suresh,
MS - Vanderbilt University Department of Biomedical Informatics
Developing a Visual Analytics Dashboard to Explore Relationships between Statin Use and Parkinson’s Disease Severity
Poster Number: P73
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Information Visualization, Informatics Implementation, Chronic Care Management
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
When it comes to gaining meaningful insights from large patient data sets such as the Parkinson’s Progressive Markers Initiative, visualization can provide valuable information for clinicians and aid in making informed decisions with patients. Conflicting findings exist regarding the relationship between statin use and Parkinson’s disease severity. A visual analytics dashboard depicting the correlation between the variables and a multiple regression with demographic and clinically relevant variables may aid researchers in exploring relationships between variables quickly to better design larger studies or clinical trials. This proposal outlines the design and implementation of such a visual analytics dashboard guided by the Munzner Nested Model.
Speaker:
Mallika Desai, Student
University of Cincinnati College of Medicine
Authors:
Mallika Desai, Student - University of Cincinnati College of Medicine; Emily Hill, MD - University of Cincinnati College of Medicine; Danny Wu, PhD - University of North Carolina at Chapel Hill;
Poster Number: P73
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Information Visualization, Informatics Implementation, Chronic Care Management
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
When it comes to gaining meaningful insights from large patient data sets such as the Parkinson’s Progressive Markers Initiative, visualization can provide valuable information for clinicians and aid in making informed decisions with patients. Conflicting findings exist regarding the relationship between statin use and Parkinson’s disease severity. A visual analytics dashboard depicting the correlation between the variables and a multiple regression with demographic and clinically relevant variables may aid researchers in exploring relationships between variables quickly to better design larger studies or clinical trials. This proposal outlines the design and implementation of such a visual analytics dashboard guided by the Munzner Nested Model.
Speaker:
Mallika Desai, Student
University of Cincinnati College of Medicine
Authors:
Mallika Desai, Student - University of Cincinnati College of Medicine; Emily Hill, MD - University of Cincinnati College of Medicine; Danny Wu, PhD - University of North Carolina at Chapel Hill;
Mallika
Desai,
Student - University of Cincinnati College of Medicine
Time-aware Dimension Reduction for Exploring Trends in Literature
Poster Number: P72
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Information Visualization, Natural Language Processing, Machine Learning
Primary Track: Foundations
With a million scientific papers yearly, methods for organizing and exploring research are more important than ever. Though semantic embeddings combined with manifold learning algorithms like t-SNE produce useful maps, these ignore publication date, which is crucial for understanding research progression. As a remedy, we develop a time-aware dimension reduction algorithm that groups similar papers while directly encoding date using radius in polar plots, revealing trends that would not be visible with standard t-SNE-based maps.
Speaker:
Brian Ondov, PhD
Yale School of Medicine
Authors:
Xueqing Peng, PhD - Yale University; Huan He, Ph.D. - Yale University; Hua Xu, Ph.D - Yale University;
Poster Number: P72
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Information Visualization, Natural Language Processing, Machine Learning
Primary Track: Foundations
With a million scientific papers yearly, methods for organizing and exploring research are more important than ever. Though semantic embeddings combined with manifold learning algorithms like t-SNE produce useful maps, these ignore publication date, which is crucial for understanding research progression. As a remedy, we develop a time-aware dimension reduction algorithm that groups similar papers while directly encoding date using radius in polar plots, revealing trends that would not be visible with standard t-SNE-based maps.
Speaker:
Brian Ondov, PhD
Yale School of Medicine
Authors:
Xueqing Peng, PhD - Yale University; Huan He, Ph.D. - Yale University; Hua Xu, Ph.D - Yale University;
Brian
Ondov,
PhD - Yale School of Medicine
Do EHRs help or hinder coordination: Examining facilitators and barriers of EHR to CRC Screening Process
Poster Number: P71
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Interoperability and Health Information Exchange, Cancer Prevention, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Electronic health records (EHR) and patient portals play a crucial role in improving colorectal cancer screening workflows. This study explores EHR-related facilitators and barriers by conducting and analyzing semi-structured interviews with clinicians and patients. Findings highlight key EHR functionalities that support screening coordination, including Epic Chat and pending orders, while digital health literacy and interoperability issues hinder effectiveness. Addressing these barriers can enhance screening effectiveness and lead to better patient outcomes.
Speaker:
Miad Alfaqih, Phd
University of Florida
Authors:
Miad Alfaqih, Phd - University of Florida; Nicole Hammer, B.A. - University of Florida - Department of Health Outcomes and Biomedical Informatics; Trang Pham, MS, MD - University of Illinois at Chicago; Sean Pajak, BS - University of Florida; Jessica Friedman, MPH - university of florida; Ashley Hughes, PhD, MS - MetroHealth System/Case Western Reserve University; Mas Jimbo, MD - University of Illinois; Keith Naylor, MD - University of Illinois; Yamile Molina, PHD - University of Illinois; Nathan Stackhouse, MD - University of Illinois; Sean McClellan, MD - University of Illinois; Megan Gregory, Ph.D. - University of Florida;
Poster Number: P71
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Interoperability and Health Information Exchange, Cancer Prevention, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Electronic health records (EHR) and patient portals play a crucial role in improving colorectal cancer screening workflows. This study explores EHR-related facilitators and barriers by conducting and analyzing semi-structured interviews with clinicians and patients. Findings highlight key EHR functionalities that support screening coordination, including Epic Chat and pending orders, while digital health literacy and interoperability issues hinder effectiveness. Addressing these barriers can enhance screening effectiveness and lead to better patient outcomes.
Speaker:
Miad Alfaqih, Phd
University of Florida
Authors:
Miad Alfaqih, Phd - University of Florida; Nicole Hammer, B.A. - University of Florida - Department of Health Outcomes and Biomedical Informatics; Trang Pham, MS, MD - University of Illinois at Chicago; Sean Pajak, BS - University of Florida; Jessica Friedman, MPH - university of florida; Ashley Hughes, PhD, MS - MetroHealth System/Case Western Reserve University; Mas Jimbo, MD - University of Illinois; Keith Naylor, MD - University of Illinois; Yamile Molina, PHD - University of Illinois; Nathan Stackhouse, MD - University of Illinois; Sean McClellan, MD - University of Illinois; Megan Gregory, Ph.D. - University of Florida;
Miad
Alfaqih,
Phd - University of Florida
Scaling a Patient Portal Integrated Diabetes Application Using FHIR: A Multisite Experience
Poster Number: P70
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Interoperability and Health Information Exchange, Data Sharing, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Introduction: We built My Diabetes Care, a patient-facing app using SMART on FHIR for cross-EHR interoperability. Methods: We used the Consolidated Framework for Implementation Research to study facilitators and barriers to implementation across two organizations. Results: Key themes include differences in user authentication (intervention), focus on 21st Century Cures Act (outer setting), competing priorities (inner setting), and limited experience with FHIR (individuals, process). Conclusions: Addressing barriers can optimize digital solutions, improving outcomes and reducing disparities.
Speaker:
Nicolás Prada-Rey, MA
Brigham and Women's Hospital
Authors:
Nicolás Prada-Rey, MA - Brigham and Women's Hospital; William Martinez, MD, MS - Vanderbilt University Medical Center; S. Trent Rosenbloom, MD, MPH, FACMI, FAMIA - Vanderbilt University Medical Center Dept of Biomedical Informatics; Richard Fay, MA - Brigham and Women's Hospital; Zhou Yan, MS - Vanderbilt University Medical Center; Brandon Oglesby, MS - Vanderbilt University Medical Center; Amit Battu, MS - Vanderbilt University Medical Center; Matthew Wien, MS - Brigham and Women's Hospital; Frank Chang, MS - Brigham and Women's Hospital; Adam Wright, PhD - Vanderbilt University Medical Center; Lipika Samal, MD - Brigham and Women's Hospital; Jorge Rodriguez, MD - Brigham and Women's Hospital/Harvard Medical School;
Poster Number: P70
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Interoperability and Health Information Exchange, Data Sharing, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Introduction: We built My Diabetes Care, a patient-facing app using SMART on FHIR for cross-EHR interoperability. Methods: We used the Consolidated Framework for Implementation Research to study facilitators and barriers to implementation across two organizations. Results: Key themes include differences in user authentication (intervention), focus on 21st Century Cures Act (outer setting), competing priorities (inner setting), and limited experience with FHIR (individuals, process). Conclusions: Addressing barriers can optimize digital solutions, improving outcomes and reducing disparities.
Speaker:
Nicolás Prada-Rey, MA
Brigham and Women's Hospital
Authors:
Nicolás Prada-Rey, MA - Brigham and Women's Hospital; William Martinez, MD, MS - Vanderbilt University Medical Center; S. Trent Rosenbloom, MD, MPH, FACMI, FAMIA - Vanderbilt University Medical Center Dept of Biomedical Informatics; Richard Fay, MA - Brigham and Women's Hospital; Zhou Yan, MS - Vanderbilt University Medical Center; Brandon Oglesby, MS - Vanderbilt University Medical Center; Amit Battu, MS - Vanderbilt University Medical Center; Matthew Wien, MS - Brigham and Women's Hospital; Frank Chang, MS - Brigham and Women's Hospital; Adam Wright, PhD - Vanderbilt University Medical Center; Lipika Samal, MD - Brigham and Women's Hospital; Jorge Rodriguez, MD - Brigham and Women's Hospital/Harvard Medical School;
Nicolás
Prada-Rey,
MA - Brigham and Women's Hospital
Enhancing Dietary Data Interoperability: LLM-Assisted Ontology Expansion for Dietary Lifestyle Information
Poster Number: P69
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Interoperability and Health Information Exchange, Large Language Models (LLMs), Patient / Person Generated Health Data (Patient Reported Outcomes)
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
This study explores the potential of Large Language Models (LLMs) in expanding the Dietary Lifestyle Ontology (DILON) to improve dietary data interoperability. Using in-context two-shot learning for semantic parsing and ReACT-based prompting for ontology curation, Claude 3.7 Sonnet achieved 93% accuracy in integrating new dietary concepts. The findings demonstrate the effectiveness of LLM-human collaboration in semi-automating lifelog ontology expansion, facilitating the interoperability of dietary lifelog data.
Speaker:
Hyeoneui Kim, PhD
Seoul National University
Author:
Hyeoneui Kim, PhD - Seoul National University;
Poster Number: P69
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Interoperability and Health Information Exchange, Large Language Models (LLMs), Patient / Person Generated Health Data (Patient Reported Outcomes)
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
This study explores the potential of Large Language Models (LLMs) in expanding the Dietary Lifestyle Ontology (DILON) to improve dietary data interoperability. Using in-context two-shot learning for semantic parsing and ReACT-based prompting for ontology curation, Claude 3.7 Sonnet achieved 93% accuracy in integrating new dietary concepts. The findings demonstrate the effectiveness of LLM-human collaboration in semi-automating lifelog ontology expansion, facilitating the interoperability of dietary lifelog data.
Speaker:
Hyeoneui Kim, PhD
Seoul National University
Author:
Hyeoneui Kim, PhD - Seoul National University;
Hyeoneui
Kim,
PhD - Seoul National University
North Carolina Health Data Utility (HDU) Initiative
Poster Number: P68
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Interoperability and Health Information Exchange, Population Health, Data Sharing
Primary Track: Applications
Programmatic Theme: Public Health Informatics
North Carolina is developing a health data utility (HDU) to enhance data sharing across health care, public health, and social services, while upholding strict data privacy and security standards. Expanding on NC HealthConnex, the statewide health information exchange (HIE), an HDU integrates health-related, non-clinical data to improve care coordination and address community health needs. Role-based access controls ensure necessary, secure, and standardized data exchange to support whole-person care and population health efforts.
Speaker:
Shayan Sarmadi, Master of Management in Clinical Informatics
NC Department of Information Technology
Authors:
Shayan Sarmadi, Master of Management in Clinical Informatics - NC Department of Information Technology; Olivia Kim, Bachelor of Science, Biology - North Carolina Department of Information Technology; Samuel Thompson, Master of Social Work - North Carolina Department of Information Technology; Jessica Tenenbaum, PhD - Duke University;
Poster Number: P68
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Interoperability and Health Information Exchange, Population Health, Data Sharing
Primary Track: Applications
Programmatic Theme: Public Health Informatics
North Carolina is developing a health data utility (HDU) to enhance data sharing across health care, public health, and social services, while upholding strict data privacy and security standards. Expanding on NC HealthConnex, the statewide health information exchange (HIE), an HDU integrates health-related, non-clinical data to improve care coordination and address community health needs. Role-based access controls ensure necessary, secure, and standardized data exchange to support whole-person care and population health efforts.
Speaker:
Shayan Sarmadi, Master of Management in Clinical Informatics
NC Department of Information Technology
Authors:
Shayan Sarmadi, Master of Management in Clinical Informatics - NC Department of Information Technology; Olivia Kim, Bachelor of Science, Biology - North Carolina Department of Information Technology; Samuel Thompson, Master of Social Work - North Carolina Department of Information Technology; Jessica Tenenbaum, PhD - Duke University;
Shayan
Sarmadi,
Master of Management in Clinical Informatics - NC Department of Information Technology
Detecting Manuscripts Related to Computable Phenotypes Using a Transformer-based Language Model
Poster Number: P67
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Knowledge Representation and Information Modeling, Artificial Intelligence, Phenomics and Phenome-wide Association Studies, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Identifying relevant manuscripts for phenomics knowledgebases is a complex and time-consuming task. We developed a Transformer-based language model using a fine-tuned BioBERT model to detect manuscripts related to computable phenotypes. To address BioBERT’s 512-token limit, we introduced a sliding-window method, segmenting documents into multiple segments and aggregating classification scores. Our model significantly outperformed the default approach (AUC: 0.99 vs. 0.83, Accuracy: 0.95 vs. 0.72). This method enhances automated phenotyping literature identification, improving knowledgebase development efficiency.
Speaker:
Junghoon Chae, PhD
Oak Ridge National Laboratory
Authors:
Junghoon Chae, PhD - Oak Ridge National Laboratory; David Heise; Keith Connatser; Jacqueline Honerlaw, RN, MPH - VA Boston Healthcare System; Monika Maripuri, MBBS, MPH - VA Boston Healthcare System; Yuk-Lam Ho, MPH - VA Boston Healthcare System; Kelly Cho, PhD - VA Boston Healthcare/Harvard Medical School;
Poster Number: P67
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Knowledge Representation and Information Modeling, Artificial Intelligence, Phenomics and Phenome-wide Association Studies, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Identifying relevant manuscripts for phenomics knowledgebases is a complex and time-consuming task. We developed a Transformer-based language model using a fine-tuned BioBERT model to detect manuscripts related to computable phenotypes. To address BioBERT’s 512-token limit, we introduced a sliding-window method, segmenting documents into multiple segments and aggregating classification scores. Our model significantly outperformed the default approach (AUC: 0.99 vs. 0.83, Accuracy: 0.95 vs. 0.72). This method enhances automated phenotyping literature identification, improving knowledgebase development efficiency.
Speaker:
Junghoon Chae, PhD
Oak Ridge National Laboratory
Authors:
Junghoon Chae, PhD - Oak Ridge National Laboratory; David Heise; Keith Connatser; Jacqueline Honerlaw, RN, MPH - VA Boston Healthcare System; Monika Maripuri, MBBS, MPH - VA Boston Healthcare System; Yuk-Lam Ho, MPH - VA Boston Healthcare System; Kelly Cho, PhD - VA Boston Healthcare/Harvard Medical School;
Junghoon
Chae,
PhD - Oak Ridge National Laboratory
Knowledge Graph for Propositional Reasoning: A Multi-Case Study of Clinical Classifications Software Refined (CCSR) for ICD-10-PCS
Poster Number: P66
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Knowledge Representation and Information Modeling, Controlled Terminologies, Ontologies, and Vocabularies, Interoperability and Health Information Exchange, Standards, Information Retrieval
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Knowledge graph excels in handling sophisticated relationships which are integral to forming and validating propositions. We used a knowledge graph to represent the intricacy between ICD-10-PCS concepts with their compositional character codes, which enabled reasoning on the inclusion criteria for CCSR clinical categories to identifying qualified concepts. The discovered pitfalls of applying the inclusion criteria and the new rule exploration demonstrates the critical role of knowledge graph in propositional reasoning.
Speaker:
Zheng Milgrom, M.D., M.P.H.
Semedy Inc.
Author:
Roberto Rocha, MD, PhD, FACMI - Semedy Inc.;
Poster Number: P66
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Knowledge Representation and Information Modeling, Controlled Terminologies, Ontologies, and Vocabularies, Interoperability and Health Information Exchange, Standards, Information Retrieval
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Knowledge graph excels in handling sophisticated relationships which are integral to forming and validating propositions. We used a knowledge graph to represent the intricacy between ICD-10-PCS concepts with their compositional character codes, which enabled reasoning on the inclusion criteria for CCSR clinical categories to identifying qualified concepts. The discovered pitfalls of applying the inclusion criteria and the new rule exploration demonstrates the critical role of knowledge graph in propositional reasoning.
Speaker:
Zheng Milgrom, M.D., M.P.H.
Semedy Inc.
Author:
Roberto Rocha, MD, PhD, FACMI - Semedy Inc.;
Zheng
Milgrom,
M.D., M.P.H. - Semedy Inc.
Evaluation of Semantic Models for Representing Biospecimen and Data Sharing Permissions in Biobank Consent Forms
Poster Number: P65
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Knowledge Representation and Information Modeling, Data Sharing, Large Language Models (LLMs)
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Concern for potential misuse of biospecimens and genomic data contributes to donation hesitancy. While consent forms delimit biospecimen and data use agreements, missingness of permissions metadata complicates large-scale stewardship. Therefore, we will evaluate two semantic models, HL7 FHIR and W3C ODRL, using 20 biobank consent forms to assess their effectiveness in making biospecimen sharing permissions machine-readable towards engendering trust between consenting contributors and investigators seeking permission.
Speaker:
Taylor Harrison, MS, MBS
Mayo Clinic
Authors:
Michelle McGowan, PhD - Mayo Clinic; Elizabeth Umberfield, PhD - NA;
Poster Number: P65
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Knowledge Representation and Information Modeling, Data Sharing, Large Language Models (LLMs)
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Concern for potential misuse of biospecimens and genomic data contributes to donation hesitancy. While consent forms delimit biospecimen and data use agreements, missingness of permissions metadata complicates large-scale stewardship. Therefore, we will evaluate two semantic models, HL7 FHIR and W3C ODRL, using 20 biobank consent forms to assess their effectiveness in making biospecimen sharing permissions machine-readable towards engendering trust between consenting contributors and investigators seeking permission.
Speaker:
Taylor Harrison, MS, MBS
Mayo Clinic
Authors:
Michelle McGowan, PhD - Mayo Clinic; Elizabeth Umberfield, PhD - NA;
Taylor
Harrison,
MS, MBS - Mayo Clinic
Exploring Variables Through CIPHER Data Dictionaries
Poster Number: P63
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Knowledge Representation and Information Modeling, Phenomics and Phenome-wide Association Studies, Data Standards
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Phenotype libraries expedite research using electronic health records data by allowing reuse of curated definitions.
Variables from publicly available datasets and surveys are also used to define phenotypes, however these variables may be difficult to find and cite. The Centralized Interactive Phenomics Resource (CIPHER) phenotype library
developed a data dictionary interface to enable browsing of variable descriptions and referencing of variables in phenotype definitions using the Million Veteran Program biobank as a test case.
Speaker:
Jacqueline Honerlaw, RN, MPH
VA Boston Healthcare System
Authors:
Michael Murray, MS - VA Boston Healthcare System; Yuk-Lam Ho, MPH - VA Boston Healthcare System; Edward Zielinksi, BS - US Department of Veterans Affairs; Tiffany Sim, MPH - Veterans Affairs; David Heise; Keith Connatser; Kelly Cho, PhD - VA Boston Healthcare/Harvard Medical School;
Poster Number: P63
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Knowledge Representation and Information Modeling, Phenomics and Phenome-wide Association Studies, Data Standards
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Phenotype libraries expedite research using electronic health records data by allowing reuse of curated definitions.
Variables from publicly available datasets and surveys are also used to define phenotypes, however these variables may be difficult to find and cite. The Centralized Interactive Phenomics Resource (CIPHER) phenotype library
developed a data dictionary interface to enable browsing of variable descriptions and referencing of variables in phenotype definitions using the Million Veteran Program biobank as a test case.
Speaker:
Jacqueline Honerlaw, RN, MPH
VA Boston Healthcare System
Authors:
Michael Murray, MS - VA Boston Healthcare System; Yuk-Lam Ho, MPH - VA Boston Healthcare System; Edward Zielinksi, BS - US Department of Veterans Affairs; Tiffany Sim, MPH - Veterans Affairs; David Heise; Keith Connatser; Kelly Cho, PhD - VA Boston Healthcare/Harvard Medical School;
Jacqueline
Honerlaw,
RN, MPH - VA Boston Healthcare System
Multi-Agent-Based Automated Clinical Data Extraction and Validation from Breast Cancer Pathology Reports Using Llama 3 Language Models
Poster Number: P62
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Extraction, Artificial Intelligence
Primary Track: Applications
This study assesses Llama 3.1-70B and Llama 3.3-70B models for automated extraction of clinical data from 1,200 breast cancer pathology reports. Using a multi-agent system involving Extraction and Validation agents with iterative cycles and majority voting, the models achieved ~98% accuracy in extracting tumor size and site under zero-shot conditions. Ongoing analyses aim to validate extraction of metastasis, lymphatic invasion, and pTNM stage.
Speaker:
Sunghyeon Park, MA
The Catholic University of Korea
Authors:
Wona Choi, Ph.D - The Catholic University of Korea; In Young Choi, PhD. - Catholic University of Korea;
Poster Number: P62
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Extraction, Artificial Intelligence
Primary Track: Applications
This study assesses Llama 3.1-70B and Llama 3.3-70B models for automated extraction of clinical data from 1,200 breast cancer pathology reports. Using a multi-agent system involving Extraction and Validation agents with iterative cycles and majority voting, the models achieved ~98% accuracy in extracting tumor size and site under zero-shot conditions. Ongoing analyses aim to validate extraction of metastasis, lymphatic invasion, and pTNM stage.
Speaker:
Sunghyeon Park, MA
The Catholic University of Korea
Authors:
Wona Choi, Ph.D - The Catholic University of Korea; In Young Choi, PhD. - Catholic University of Korea;
Sunghyeon
Park,
MA - The Catholic University of Korea
Evaluating LLM-based reranking method for medication term normalization
Poster Number: P61
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Retrieval, Evaluation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study evaluates the use of large language models (LLMs) for selecting accurate RXCUI codes from IMO Precision Normalize search results via retrieval-augmented generation. By testing three configurations of Normalize1 and reranking top candidates using GPT-4o and GPT-4o-mini, we assessed performance on 436 medication terms. GPT-4o improved RXCUI selection accuracy by 12.4% over IMO Precision Normalize alone. These findings highlight the potential of LLMs to enhance medication normalization and reduce manual review in clinical workflows.
Speaker:
Tavleen Singh, PhD
IMO Health
Authors:
Qiang Wei, Ph.D. - IMO Health; Chuck Levecke, B.S. - IMO Health; Jingqi Wang - Melax Technologies, Inc;
Poster Number: P61
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Information Retrieval, Evaluation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study evaluates the use of large language models (LLMs) for selecting accurate RXCUI codes from IMO Precision Normalize search results via retrieval-augmented generation. By testing three configurations of Normalize1 and reranking top candidates using GPT-4o and GPT-4o-mini, we assessed performance on 436 medication terms. GPT-4o improved RXCUI selection accuracy by 12.4% over IMO Precision Normalize alone. These findings highlight the potential of LLMs to enhance medication normalization and reduce manual review in clinical workflows.
Speaker:
Tavleen Singh, PhD
IMO Health
Authors:
Qiang Wei, Ph.D. - IMO Health; Chuck Levecke, B.S. - IMO Health; Jingqi Wang - Melax Technologies, Inc;
Tavleen
Singh,
PhD - IMO Health
Enhancing Transparency in Large Language Model-Based Research with PromptLog: A Tool for Prompt History Tracking and Reporting
Poster Number: P50
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Human-computer Interaction
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
LLMs assist researchers with various tasks, but success depends on prompt quality, which requires iterative refinements. However, the frequent lack of transparency in these iterations hinders research replicability. To address this, we propose PromptLog, a tool for tracking and reporting prompt history. By documenting prompt iterations, PromptLog contributes to establishing best practices in LLM-based research at AMIA.
Speaker:
Lu Wang, Master
Stevens Institute of Technology
Authors:
Ananya Shrivastava, Undergraduate Student - Stevens Institute of Technology; Elham Aghakhani, Master - Drexel University; Rezvaneh Rezapour; Rezvaneh Rezapour, Ph.D. - Drexel University; Jina Huh-Yoo, Ph.D. - Stevens Institute of Technology;
Poster Number: P50
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Human-computer Interaction
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
LLMs assist researchers with various tasks, but success depends on prompt quality, which requires iterative refinements. However, the frequent lack of transparency in these iterations hinders research replicability. To address this, we propose PromptLog, a tool for tracking and reporting prompt history. By documenting prompt iterations, PromptLog contributes to establishing best practices in LLM-based research at AMIA.
Speaker:
Lu Wang, Master
Stevens Institute of Technology
Authors:
Ananya Shrivastava, Undergraduate Student - Stevens Institute of Technology; Elham Aghakhani, Master - Drexel University; Rezvaneh Rezapour; Rezvaneh Rezapour, Ph.D. - Drexel University; Jina Huh-Yoo, Ph.D. - Stevens Institute of Technology;
Lu
Wang,
Master - Stevens Institute of Technology
Large Language Models for Generative Mental Health Tasks: A Scoping Review
Poster Number: P51
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, User-centered Design Methods, Diversity, Equity, Inclusion, and Accessibility, Legal, Ethical, Social and Regulatory Issues, Fairness and elimination of bias, Real-World Evidence Generation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Large language models (LLMs) demonstrate potential in generative mental health tasks, such as therapy and counseling, yet face challenges in evaluation and clinical validation. This scoping review of 16 studies (2020–2024) highlights applications including Cognitive Behavioral Therapy (CBT) interventions, clinical assistance, and emotional support, predominantly using GPT models. While showing short-term efficacy in anxiety and loneliness, studies relied on heterogeneous, non-standardized evaluations and underassessed safety and fairness. Key priorities include adopting validated frameworks, open-source development, and positioning LLMs as adjuncts to clinicians. Multimodal interfaces and ethical collaboration are essential to ensure equitable, safe integration before clinical deployment.
Speaker:
Yining Hua, MSc
Harvard T.H. Chan School of Public Health
Authors:
Hongbin Na, MSc - Australian Artificial Intelligence Institute, University of Technology Sydney; Zehan Li, MSc - UTHealth Houston; Fenglin Liu, MSc - 4University of Oxford; Xiao Fang, MSc - Massachusetts Institute of Technology; David Clifton, PhD - University of Oxford; John Torous, MD - Harvard Medical School;
Poster Number: P51
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, User-centered Design Methods, Diversity, Equity, Inclusion, and Accessibility, Legal, Ethical, Social and Regulatory Issues, Fairness and elimination of bias, Real-World Evidence Generation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Large language models (LLMs) demonstrate potential in generative mental health tasks, such as therapy and counseling, yet face challenges in evaluation and clinical validation. This scoping review of 16 studies (2020–2024) highlights applications including Cognitive Behavioral Therapy (CBT) interventions, clinical assistance, and emotional support, predominantly using GPT models. While showing short-term efficacy in anxiety and loneliness, studies relied on heterogeneous, non-standardized evaluations and underassessed safety and fairness. Key priorities include adopting validated frameworks, open-source development, and positioning LLMs as adjuncts to clinicians. Multimodal interfaces and ethical collaboration are essential to ensure equitable, safe integration before clinical deployment.
Speaker:
Yining Hua, MSc
Harvard T.H. Chan School of Public Health
Authors:
Hongbin Na, MSc - Australian Artificial Intelligence Institute, University of Technology Sydney; Zehan Li, MSc - UTHealth Houston; Fenglin Liu, MSc - 4University of Oxford; Xiao Fang, MSc - Massachusetts Institute of Technology; David Clifton, PhD - University of Oxford; John Torous, MD - Harvard Medical School;
Yining
Hua,
MSc - Harvard T.H. Chan School of Public Health
NutriRAG: Unleashing the Power of Large Language Models for Food Identification and Classification through Retrieval Methods
Poster Number: P52
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Precision Medicine
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Accurate classification of unstructured food entries from diet-tracking apps remains challenging due to natural language variability and limited database coverage. This study developed NutriRAG, a retrieval-augmented LLM framework, to automate food classification and analyze dietary patterns from free-text entries. RAG-enhanced GPT-4 achieved superior accuracy (Micro F1: 82.24) over fine-tuned BERT and non-RAG LLMs. Applied to an RCT (NCT04259632), NutriRAG revealed metabolic links between meal timing (p<0.05), demonstrating its potential for precision nutrition research.
Speaker:
Huixue Zhou, PhD
University of Minnesota
Authors:
Huixue Zhou, PhD - University of Minnesota; Lisa Chow, MD - University of Minnesota; Lisa Harnack, DrPH, RD, MPH - University of Minnesota; Satchin Pando, PhD - Salk Institute for Biological Studies; Emily Manoogian, PhD - Salk Institute for Biological Studies; Mingchen Li, Ms - University of Minnesota; Yongkang Xiao, Ms - University of Minnesota; Rui Zhang, PhD, FAMIA, FACMI - University of Minnesota, Twin Cities;
Poster Number: P52
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Precision Medicine
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Accurate classification of unstructured food entries from diet-tracking apps remains challenging due to natural language variability and limited database coverage. This study developed NutriRAG, a retrieval-augmented LLM framework, to automate food classification and analyze dietary patterns from free-text entries. RAG-enhanced GPT-4 achieved superior accuracy (Micro F1: 82.24) over fine-tuned BERT and non-RAG LLMs. Applied to an RCT (NCT04259632), NutriRAG revealed metabolic links between meal timing (p<0.05), demonstrating its potential for precision nutrition research.
Speaker:
Huixue Zhou, PhD
University of Minnesota
Authors:
Huixue Zhou, PhD - University of Minnesota; Lisa Chow, MD - University of Minnesota; Lisa Harnack, DrPH, RD, MPH - University of Minnesota; Satchin Pando, PhD - Salk Institute for Biological Studies; Emily Manoogian, PhD - Salk Institute for Biological Studies; Mingchen Li, Ms - University of Minnesota; Yongkang Xiao, Ms - University of Minnesota; Rui Zhang, PhD, FAMIA, FACMI - University of Minnesota, Twin Cities;
Huixue
Zhou,
PhD - University of Minnesota
Understanding Large Language Models’ Rating Behaviors on Novel Psychoactive Substances using Reddit
Poster Number: P53
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Social Media and Connected Health, Public Health, Information Retrieval
Primary Track: Applications
Programmatic Theme: Public Health Informatics
We used Reddit discussions on six novel psychoactive substances to generate answers to expert questions and evaluated these responses using four large language models (LLMs). Larger LLMs were more decisive in their ratings, particularly when objective evaluation metrics were present. Self-judging models showed stronger agreement with their own answers under objective criteria but were less consistent under subjective metrics. These results highlight model size and metric objectivity as key factors in LLM judgment patterns.
Speaker:
Swati Rajwal, PhD
Emory University
Authors:
Avinash Kumar Pandey, PhD - Emory University; JaMor Hairston, MSHI, MS - Emory University; Sudeshna Das, PhD - Emory University; Abeed Sarker, PhD - Emory University School of Medicine;
Poster Number: P53
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Social Media and Connected Health, Public Health, Information Retrieval
Primary Track: Applications
Programmatic Theme: Public Health Informatics
We used Reddit discussions on six novel psychoactive substances to generate answers to expert questions and evaluated these responses using four large language models (LLMs). Larger LLMs were more decisive in their ratings, particularly when objective evaluation metrics were present. Self-judging models showed stronger agreement with their own answers under objective criteria but were less consistent under subjective metrics. These results highlight model size and metric objectivity as key factors in LLM judgment patterns.
Speaker:
Swati Rajwal, PhD
Emory University
Authors:
Avinash Kumar Pandey, PhD - Emory University; JaMor Hairston, MSHI, MS - Emory University; Sudeshna Das, PhD - Emory University; Abeed Sarker, PhD - Emory University School of Medicine;
Swati
Rajwal,
PhD - Emory University
Evaluating Large Language Models for Summarizing Long Clinical Texts and Longitudinal Patient Trajectories
Poster Number: P54
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Artificial Intelligence, Clinical Decision Support
Working Group: Natural Language Processing Working Group
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study evaluates the capabilities of large language models (LLMs) in summarizing multi-modal longitudinal Electronic Health Records (EHR). We investigate the impact of input modalities, timestamps, and long-context settings on LLM performance using three tasks: discharge summarization, daily progress note assessment and plan generation, and binary diagnosis prediction. Results show that while Retrieval-Augmented-Generation (RAG) improved performance, models still struggled with temporal reasoning and long-context clinical reasoning remains a challenge.
Speaker:
Maya Kruse, MS
University of Colorado
Authors:
Maya Kruse, MS - University of Colorado; Shiyue HU, BS - University of Colorado; Nicholas Derby, BS - University of Colorado; Yifu Wu, PhD - University of Colorado; Samantha Stonbraker, PhD, MPH, RN, FAAN - University of Colorado Anschutz Medical Campus; Bingsheng Yao, Ph.D. - Northeastern University; Dakuo Wang, PhD - Northeastern University; Elizabeth Goldberg, MD, ScM - University of Colorado; Yanjun Gao, PhD - University of Colorado;
Poster Number: P54
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Artificial Intelligence, Clinical Decision Support
Working Group: Natural Language Processing Working Group
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study evaluates the capabilities of large language models (LLMs) in summarizing multi-modal longitudinal Electronic Health Records (EHR). We investigate the impact of input modalities, timestamps, and long-context settings on LLM performance using three tasks: discharge summarization, daily progress note assessment and plan generation, and binary diagnosis prediction. Results show that while Retrieval-Augmented-Generation (RAG) improved performance, models still struggled with temporal reasoning and long-context clinical reasoning remains a challenge.
Speaker:
Maya Kruse, MS
University of Colorado
Authors:
Maya Kruse, MS - University of Colorado; Shiyue HU, BS - University of Colorado; Nicholas Derby, BS - University of Colorado; Yifu Wu, PhD - University of Colorado; Samantha Stonbraker, PhD, MPH, RN, FAAN - University of Colorado Anschutz Medical Campus; Bingsheng Yao, Ph.D. - Northeastern University; Dakuo Wang, PhD - Northeastern University; Elizabeth Goldberg, MD, ScM - University of Colorado; Yanjun Gao, PhD - University of Colorado;
Maya
Kruse,
MS - University of Colorado
Examining Nurses’ Acceptance of LLM-RR Based Nursing Manual Search Engine Using a Combined TAM and UTAUT Model
Poster Number: P49
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Nursing Informatics, Surveys and Needs Analysis
Primary Track: Applications
This study evaluated nurses' acceptance of LLM-RR-based nursing manual search engine with 111 nurses. PLS-SEM analysis showed effort expectancy, social influence, and performance expectancy significantly influenced attitude, which strongly predicted intention to use (R²=0.39-0.47). Facilitating conditions had no significant effect. Results indicate successful implementation requires user-friendly interfaces, organizational support, and improved technical resources for adoption in nursing practice. The system addresses current limitations including dispersed documentation and inefficient searches that hinder information access.
Speaker:
Ye Eun Park, Bachelor
Seoul National University Bundang Hospital
Authors:
Wooin Seo, Master - Seoul National University Bundang Hospital; Yeonhoon Jang, Master - Seoul National University Bundang Hospital; Donghyoung Lee, Bachelor - Seoul National University Bundang Hospital; Sejin Nam, Doctor - Seoul National University Bundang Hospital; Se Young Jung, Medical doctor - Seoul National University Bundang Hospital;
Poster Number: P49
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Nursing Informatics, Surveys and Needs Analysis
Primary Track: Applications
This study evaluated nurses' acceptance of LLM-RR-based nursing manual search engine with 111 nurses. PLS-SEM analysis showed effort expectancy, social influence, and performance expectancy significantly influenced attitude, which strongly predicted intention to use (R²=0.39-0.47). Facilitating conditions had no significant effect. Results indicate successful implementation requires user-friendly interfaces, organizational support, and improved technical resources for adoption in nursing practice. The system addresses current limitations including dispersed documentation and inefficient searches that hinder information access.
Speaker:
Ye Eun Park, Bachelor
Seoul National University Bundang Hospital
Authors:
Wooin Seo, Master - Seoul National University Bundang Hospital; Yeonhoon Jang, Master - Seoul National University Bundang Hospital; Donghyoung Lee, Bachelor - Seoul National University Bundang Hospital; Sejin Nam, Doctor - Seoul National University Bundang Hospital; Se Young Jung, Medical doctor - Seoul National University Bundang Hospital;
Ye Eun
Park,
Bachelor - Seoul National University Bundang Hospital
Brief Translation Quality Measure for Patient-reported Outcome Measures Using Machine Translations
Poster Number: P48
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Patient / Person Generated Health Data (Patient Reported Outcomes), Evaluation
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
We describe the development and psychometric properties of the Translation Quality Measure (TQM) for the assessment of the quality and readability of translations for patient-reported outcome measures, with a specific focus on language models.
Speaker:
Sheng-Chieh Lu, PhD
The University of Texas MD Anderson Cancer Center
Author:
Chris Gibbons, PhD. - Oracle Health;
Poster Number: P48
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Patient / Person Generated Health Data (Patient Reported Outcomes), Evaluation
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
We describe the development and psychometric properties of the Translation Quality Measure (TQM) for the assessment of the quality and readability of translations for patient-reported outcome measures, with a specific focus on language models.
Speaker:
Sheng-Chieh Lu, PhD
The University of Texas MD Anderson Cancer Center
Author:
Chris Gibbons, PhD. - Oracle Health;
Sheng-Chieh
Lu,
PhD - The University of Texas MD Anderson Cancer Center
LLM-Agent AI for Cancer Fact-Checking on Online Platforms
Poster Number: P47
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Social Media and Connected Health, Artificial Intelligence
Primary Track: Applications
We developed a multi-agent LLM system to detect, verify, and respond to cancer-related misinformation (CRM) on YouTube. Using automated transcription, zero-shot classification, and evidence retrieval via PubMed, the system achieved strong accuracy and precision. This approach enables scalable, real-time CRM mitigation to support evidence-based cancer communication on social media.
Speaker:
Mohammed Al-Garadi, PhD
VUMC
Author:
Shui Mauser, Bachelor's Degree - Vanderbilt University Medical Center;
Poster Number: P47
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Social Media and Connected Health, Artificial Intelligence
Primary Track: Applications
We developed a multi-agent LLM system to detect, verify, and respond to cancer-related misinformation (CRM) on YouTube. Using automated transcription, zero-shot classification, and evidence retrieval via PubMed, the system achieved strong accuracy and precision. This approach enables scalable, real-time CRM mitigation to support evidence-based cancer communication on social media.
Speaker:
Mohammed Al-Garadi, PhD
VUMC
Author:
Shui Mauser, Bachelor's Degree - Vanderbilt University Medical Center;
Mohammed
Al-Garadi,
PhD - VUMC
Machine Learning Enhances Diagnosis of Necrotizing Soft Tissue Infections with Superior Accuracy
Poster Number: P44
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Infectious Diseases and Epidemiology, Diagnostic Systems
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Machine learning enhances necrotizing soft tissue infection (NSTI) diagnosis by leveraging standard clinical data to produce an interpretable risk prediction tool. This approach surpasses the Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC)—a commonly used risk stratification score that underperforms in external validation. The new model shows strong potential for seamless clinical integration, improving accuracy and supporting timely decision-making to help reduce mortality from NSTI.
Speaker:
Anita Subbarao, MD
University of Washington
Authors:
Anita Subbarao, MD - University of Washington; Jane Hall, PhD - University of Washington; Robert Doerning, MD, MBA, MS - University of Washington; Eileen Bulger, MD, FACS - University of Washington; Erika Bisgaard, MD - University of Washington; M. Kennedy Hall, MD MHS - University of Washington School of Medicine;
Poster Number: P44
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Infectious Diseases and Epidemiology, Diagnostic Systems
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Machine learning enhances necrotizing soft tissue infection (NSTI) diagnosis by leveraging standard clinical data to produce an interpretable risk prediction tool. This approach surpasses the Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC)—a commonly used risk stratification score that underperforms in external validation. The new model shows strong potential for seamless clinical integration, improving accuracy and supporting timely decision-making to help reduce mortality from NSTI.
Speaker:
Anita Subbarao, MD
University of Washington
Authors:
Anita Subbarao, MD - University of Washington; Jane Hall, PhD - University of Washington; Robert Doerning, MD, MBA, MS - University of Washington; Eileen Bulger, MD, FACS - University of Washington; Erika Bisgaard, MD - University of Washington; M. Kennedy Hall, MD MHS - University of Washington School of Medicine;
Anita
Subbarao,
MD - University of Washington
Comparison of time series clustering approaches for identifying subphenotypes of acute cardiology patients
Poster Number: P43
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Information Extraction, Personal Health Informatics, Nursing Informatics
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Highly dimensional inpatient time series data are routinely collected but rarely used in clinical decision-making due to computational complexity in modeling. Although various time series clustering approaches aim to reduce dimensions while minimizing information loss, it is unknown whether they identify distinct or similar illness severity trajectories among acute cardiology inpatients. Thus, the aim of this study is to compare clustering agreement and performance across approaches as well as characterize identified subphenotypes.
Speaker:
Reanna Panagides, MS, RN
University of Virginia
Authors:
Sarah Ratcliffe, PhD - University of Virginia; Jamieson Bourque, MD - University of Virginia; Matthew Clark, PhD - Nihon Kohden Digital Health Solutions; Katy Krahn, MS, LCGC - University of Virginia; Jessica Keim-Malpass, PhD, RN, CPNP - University of Virginia;
Poster Number: P43
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Information Extraction, Personal Health Informatics, Nursing Informatics
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Highly dimensional inpatient time series data are routinely collected but rarely used in clinical decision-making due to computational complexity in modeling. Although various time series clustering approaches aim to reduce dimensions while minimizing information loss, it is unknown whether they identify distinct or similar illness severity trajectories among acute cardiology inpatients. Thus, the aim of this study is to compare clustering agreement and performance across approaches as well as characterize identified subphenotypes.
Speaker:
Reanna Panagides, MS, RN
University of Virginia
Authors:
Sarah Ratcliffe, PhD - University of Virginia; Jamieson Bourque, MD - University of Virginia; Matthew Clark, PhD - Nihon Kohden Digital Health Solutions; Katy Krahn, MS, LCGC - University of Virginia; Jessica Keim-Malpass, PhD, RN, CPNP - University of Virginia;
Reanna
Panagides,
MS, RN - University of Virginia
Leveraging Health Data and Machine Learning to Predict No-Show Propensity Factors in Glaucoma Patients: A Health Informatics Approach to Improve Clinic Efficiency and Patient Care
Poster Number: P42
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Personal Health Informatics, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study combines predictive modeling and inference using electronic health records to identify factors influencing no-show risk for glaucoma patients at their first visit. Data from 8,504 patients generated a no-show propensity factor (NSPF), with XGBoost achieving 89% sensitivity. Higher NSPF was linked to younger age, Black race, lower socioeconomic status, and worse clinical conditions. These findings identify at-risk patients and suggest tailored interventions, such as social support, to improve appointment adherence and care continuity.
Speaker:
Hai-Wei Liang, PhD
Department of Ophthalmology, University of Pittsburgh School of Medicine
Authors:
Hai-Wei Liang, PhD - Department of Ophthalmology, University of Pittsburgh School of Medicine; Andrew Williams, MD - Department of Ophthalmology, School of Medicine, University of Pittsburgh;
Poster Number: P42
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Personal Health Informatics, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study combines predictive modeling and inference using electronic health records to identify factors influencing no-show risk for glaucoma patients at their first visit. Data from 8,504 patients generated a no-show propensity factor (NSPF), with XGBoost achieving 89% sensitivity. Higher NSPF was linked to younger age, Black race, lower socioeconomic status, and worse clinical conditions. These findings identify at-risk patients and suggest tailored interventions, such as social support, to improve appointment adherence and care continuity.
Speaker:
Hai-Wei Liang, PhD
Department of Ophthalmology, University of Pittsburgh School of Medicine
Authors:
Hai-Wei Liang, PhD - Department of Ophthalmology, University of Pittsburgh School of Medicine; Andrew Williams, MD - Department of Ophthalmology, School of Medicine, University of Pittsburgh;
Hai-Wei
Liang,
PhD - Department of Ophthalmology, University of Pittsburgh School of Medicine
Machine Learning-Based Prediction of Cancer-Related Cardiac Dysfunction in Cardio-Oncology Patients Using the All-of-Us Research Program
Poster Number: P40
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Quantitative Methods, Cancer Prevention, Chronic Care Management, Delivering Health Information and Knowledge to the Public, Nursing Informatics, Patient / Person Generated Health Data (Patient Reported Outcomes), Precision Medicine
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Background: Cardio-Oncology (CO) patients face overlapping symptoms and complex treatment challenges, which may increase their risk of developing cancer-related cardiac dysfunction (CRCD), including heart failure and cardiomyopathy. Identifying key factors that predict the occurrence of CRCD is crucial for improving prognosis. This study aims to identify the predictors of CRCD in CO patients and assess their impact on clinical outcomes.
Methods: This study used All of Us data, incorporating EHR and survey responses. Final sample included 5,729 participants, with 19% with CRCD and 81% without CRCD. To address class imbalance, class weighting was applied. We implemented three machine learning (ML) models: Logistic Regression, Random Forest, and XGBoost. For preprocesing, the dataset was split into Train - Test (80/20) subsets, and robust scaling and one-hot encoding were applied. Each model was trained using the train dataset and evaluated based on predictions made on the test dataset to assess performance.
Results: XGBoost achieved the most balanced performance with the highest Accuracy (0.8148) and F1 score (0.8962). Feature importance analysis identified number of CO symptoms as the strongest predictor, followed by age, income, and gender, while SHAP analysis highlighted discrimination, neighborhood disorder, and number of CO symptoms as key contributors to CRCD risk. An AUC of 0.75 indicates fair to good predictive accuracy.
Conclusion: These findings underscore the significance of Cardio-Oncology symptoms, sociodemographic factors, and social determinants of health in predicting CRCD risk. Integrating these factors into early screening and risk stratification may enhance clinical decision-making and patient outcomes.
Speaker:
Cheongin "Rachel" Im, MSN, RN
The University of Texas at Austin School of Nursing
Author:
Julie Zuniga, PhD, RN, FAAN - The University of Texas at Austin School of Nursing;
Poster Number: P40
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Quantitative Methods, Cancer Prevention, Chronic Care Management, Delivering Health Information and Knowledge to the Public, Nursing Informatics, Patient / Person Generated Health Data (Patient Reported Outcomes), Precision Medicine
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Background: Cardio-Oncology (CO) patients face overlapping symptoms and complex treatment challenges, which may increase their risk of developing cancer-related cardiac dysfunction (CRCD), including heart failure and cardiomyopathy. Identifying key factors that predict the occurrence of CRCD is crucial for improving prognosis. This study aims to identify the predictors of CRCD in CO patients and assess their impact on clinical outcomes.
Methods: This study used All of Us data, incorporating EHR and survey responses. Final sample included 5,729 participants, with 19% with CRCD and 81% without CRCD. To address class imbalance, class weighting was applied. We implemented three machine learning (ML) models: Logistic Regression, Random Forest, and XGBoost. For preprocesing, the dataset was split into Train - Test (80/20) subsets, and robust scaling and one-hot encoding were applied. Each model was trained using the train dataset and evaluated based on predictions made on the test dataset to assess performance.
Results: XGBoost achieved the most balanced performance with the highest Accuracy (0.8148) and F1 score (0.8962). Feature importance analysis identified number of CO symptoms as the strongest predictor, followed by age, income, and gender, while SHAP analysis highlighted discrimination, neighborhood disorder, and number of CO symptoms as key contributors to CRCD risk. An AUC of 0.75 indicates fair to good predictive accuracy.
Conclusion: These findings underscore the significance of Cardio-Oncology symptoms, sociodemographic factors, and social determinants of health in predicting CRCD risk. Integrating these factors into early screening and risk stratification may enhance clinical decision-making and patient outcomes.
Speaker:
Cheongin "Rachel" Im, MSN, RN
The University of Texas at Austin School of Nursing
Author:
Julie Zuniga, PhD, RN, FAAN - The University of Texas at Austin School of Nursing;
Cheongin "Rachel"
Im,
MSN, RN - The University of Texas at Austin School of Nursing
Impact of Socioeconomic Status on Maternal Mental Health among Pregnant Women Using NIH All of Us Data
Poster Number: P41
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Quantitative Methods, Natural Language Processing, Informatics Implementation, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study examines the impact of socioeconomic factors such as education, marital status, health insurance, employment, and income on maternal mental health (MMH) among pregnant women in the NIH All of Us Research Program. We developed predictive models to identify MMH risks early, with the Random Forest model achieving the highest AUROC (0.90). Our findings highlight the significant impact of socioeconomic status on MMH. These insights can inform targeted policies and interventions to improve maternal well-being and reduce adverse pregnancy outcomes.
Speaker:
Uma Sarder, Ms in Data Science
Meharry Medical College, Nashville, TN
Authors:
Uma Sarder, Ms in Data Science - Meharry Medical College, Nashville, TN; Todd Gary, PhD; Allysceaeioun Britt, PhD - Meharry Medical College; Aize Cao - Meharry Medical College;
Poster Number: P41
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Quantitative Methods, Natural Language Processing, Informatics Implementation, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study examines the impact of socioeconomic factors such as education, marital status, health insurance, employment, and income on maternal mental health (MMH) among pregnant women in the NIH All of Us Research Program. We developed predictive models to identify MMH risks early, with the Random Forest model achieving the highest AUROC (0.90). Our findings highlight the significant impact of socioeconomic status on MMH. These insights can inform targeted policies and interventions to improve maternal well-being and reduce adverse pregnancy outcomes.
Speaker:
Uma Sarder, Ms in Data Science
Meharry Medical College, Nashville, TN
Authors:
Uma Sarder, Ms in Data Science - Meharry Medical College, Nashville, TN; Todd Gary, PhD; Allysceaeioun Britt, PhD - Meharry Medical College; Aize Cao - Meharry Medical College;
Uma
Sarder,
Ms in Data Science - Meharry Medical College, Nashville, TN
Machine Learning of Remote Video Interviews for Quantification of Cognitive Impairment and Psychological Well-Being in Older Adults
Poster Number: P39
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Telemedicine, Clinical Decision Support
Primary Track: Applications
Population aging is increasing the MCI healthcare burden, worsening mental health issues amid a workforce shortage. This study demonstrates the feasibility of monitoring 1) psychological well-being (anxiety, depression, loneliness, and social engagement; 80% F1 score overall) in people with MCI through facial videos and 2) cognitive decline over 12 months (76% F1 score) through speech analysis. Our future work will conduct field studies using our models to develop clinical decision support for dementia care clinics.
Speaker:
Merna Bibars, MSc.
Georgia Institute of Technology
Authors:
Iris Zheng, Undergraduate Student - Georgia Institute of Technology; Bolaji Omofojoye, MS - Emory University; Hiroko Dodge, PhD - Massachusetts General Hospital, Harvard Medical School; Allan Levey, MD/PhD - Emory University; Amy Rodriguez, PhD - Emory University; Gari Clifford, DPhil - Georgia Institute of Technology; Emory University; Hyeokhyen Kwon, Ph.D. - Emory University;
Poster Number: P39
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Telemedicine, Clinical Decision Support
Primary Track: Applications
Population aging is increasing the MCI healthcare burden, worsening mental health issues amid a workforce shortage. This study demonstrates the feasibility of monitoring 1) psychological well-being (anxiety, depression, loneliness, and social engagement; 80% F1 score overall) in people with MCI through facial videos and 2) cognitive decline over 12 months (76% F1 score) through speech analysis. Our future work will conduct field studies using our models to develop clinical decision support for dementia care clinics.
Speaker:
Merna Bibars, MSc.
Georgia Institute of Technology
Authors:
Iris Zheng, Undergraduate Student - Georgia Institute of Technology; Bolaji Omofojoye, MS - Emory University; Hiroko Dodge, PhD - Massachusetts General Hospital, Harvard Medical School; Allan Levey, MD/PhD - Emory University; Amy Rodriguez, PhD - Emory University; Gari Clifford, DPhil - Georgia Institute of Technology; Emory University; Hyeokhyen Kwon, Ph.D. - Emory University;
Merna
Bibars,
MSc. - Georgia Institute of Technology
Developing a Cancer Patient Navigation System: Usability Results
Poster Number: P37
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Mobile Health, Usability, User-centered Design Methods, Personal Health Informatics, Health Equity
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
We co-designed a patient navigation system, consisting of a smartphone app for cancer patients and a web-based dashboard for their navigators. The app collects patient-reported barriers to care and suggests relevant services. Navigators receive alerts and review this data via the dashboard. Usability testing with five cancer patients and six navigators showed both tools were easy to use and demonstrated good usability.
Speaker:
Ming-Yuan Chih, PhD
University of Kentucky
Authors:
Ming-Yuan Chih, PhD - University of Kentucky; Anthony Fiola, PhD, MFA - University of Cincinnati; Joseph Alexander, MHA - University of Kentucky Markey Cancer Center; Christine Stroebel, MS - University of Kentucky Markey Cancer Center; Charles McCann, BS - University of Kentucky Markey Cancer Center; Katie Brown, MSW - University of Kentucky Markey Cancer Center; Pamela Hull, PhD - University of Kentucky Markey Cancer Center; Timothy Mullett, MD, MBA, FACS - University of Kentucky Markey Cancer Center;
Poster Number: P37
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Mobile Health, Usability, User-centered Design Methods, Personal Health Informatics, Health Equity
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
We co-designed a patient navigation system, consisting of a smartphone app for cancer patients and a web-based dashboard for their navigators. The app collects patient-reported barriers to care and suggests relevant services. Navigators receive alerts and review this data via the dashboard. Usability testing with five cancer patients and six navigators showed both tools were easy to use and demonstrated good usability.
Speaker:
Ming-Yuan Chih, PhD
University of Kentucky
Authors:
Ming-Yuan Chih, PhD - University of Kentucky; Anthony Fiola, PhD, MFA - University of Cincinnati; Joseph Alexander, MHA - University of Kentucky Markey Cancer Center; Christine Stroebel, MS - University of Kentucky Markey Cancer Center; Charles McCann, BS - University of Kentucky Markey Cancer Center; Katie Brown, MSW - University of Kentucky Markey Cancer Center; Pamela Hull, PhD - University of Kentucky Markey Cancer Center; Timothy Mullett, MD, MBA, FACS - University of Kentucky Markey Cancer Center;
Ming-Yuan
Chih,
PhD - University of Kentucky
Assessing the Quality and Usability of Mobile Dental Health Apps Using the MARS Framework
Poster Number: P38
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Mobile Health, Usability, Education and Training, User-centered Design Methods
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
This study evaluates the quality and usability of mobile dental health apps using the Mobile App Rating Scale (MARS). After screening over 600 apps across iOS and Android platforms, 27 met the inclusion criteria. Apps were assessed across engagement, functionality, aesthetics, and information quality. Findings highlight top-performing apps and key design gaps, offering actionable insights for developers and healthcare professionals to enhance digital oral health interventions and user experience.
Speaker:
Prathibha Bondili, Masters
University of Pittsburgh
Authors:
DEVA HARSHINI YADAV KALASANI, MASTERS - UNIVERSITY OF PITTSBURGH; Sree Gayatri Anusha Mylavarapu, Masters in Health Informatics - University of Pittsburgh; Hongtao Wang, Bachelor of Arts - University of Pittsburgh; Yong Kyung Choi, PhD, MPH - University of Pittsburgh; Prathibha Bondili, Masters - University of Pittsburgh;
Poster Number: P38
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Mobile Health, Usability, Education and Training, User-centered Design Methods
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
This study evaluates the quality and usability of mobile dental health apps using the Mobile App Rating Scale (MARS). After screening over 600 apps across iOS and Android platforms, 27 met the inclusion criteria. Apps were assessed across engagement, functionality, aesthetics, and information quality. Findings highlight top-performing apps and key design gaps, offering actionable insights for developers and healthcare professionals to enhance digital oral health interventions and user experience.
Speaker:
Prathibha Bondili, Masters
University of Pittsburgh
Authors:
DEVA HARSHINI YADAV KALASANI, MASTERS - UNIVERSITY OF PITTSBURGH; Sree Gayatri Anusha Mylavarapu, Masters in Health Informatics - University of Pittsburgh; Hongtao Wang, Bachelor of Arts - University of Pittsburgh; Yong Kyung Choi, PhD, MPH - University of Pittsburgh; Prathibha Bondili, Masters - University of Pittsburgh;
Prathibha
Bondili,
Masters - University of Pittsburgh
Bridging Gaps in HIV Care: Usability Evaluation of a mHealth App for Identifying and Retaining Individuals with Non-Viral Suppression
Poster Number: P36
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Mobile Health, User-centered Design Methods, Usability, Public Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Despite improved outcomes, geographic disparities persist, with higher rates of people with HIV who have not achieved viral suppression living in the Deep South. In response, we developed the Drive to Zero app, targeting individuals who have not achieved viral suppression or lacked evidence of care within the past 12 months, aligning with 2022-2025 National HIV/AIDS Strategy Goals. Our usability evaluation revealed high user satisfaction, demonstrating that our user-centered design supported a usable app.
Speaker:
Fabiana Dos Santos, PhD, MSN, RN
Columbia University School of Nursing
Authors:
Fabiana Dos Santos, PhD, MSN, RN - Columbia University School of Nursing; Sophia Mclnerney, BS - Columbia University School of Nursing; Aadia Rana, MD - University of Alabama-Birmingham; D. Scott Batey, PhD - Tulane University School of Social Work; Rebecca Schnall, BSN, MPH, PhD - Columbia University;
Poster Number: P36
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Mobile Health, User-centered Design Methods, Usability, Public Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Despite improved outcomes, geographic disparities persist, with higher rates of people with HIV who have not achieved viral suppression living in the Deep South. In response, we developed the Drive to Zero app, targeting individuals who have not achieved viral suppression or lacked evidence of care within the past 12 months, aligning with 2022-2025 National HIV/AIDS Strategy Goals. Our usability evaluation revealed high user satisfaction, demonstrating that our user-centered design supported a usable app.
Speaker:
Fabiana Dos Santos, PhD, MSN, RN
Columbia University School of Nursing
Authors:
Fabiana Dos Santos, PhD, MSN, RN - Columbia University School of Nursing; Sophia Mclnerney, BS - Columbia University School of Nursing; Aadia Rana, MD - University of Alabama-Birmingham; D. Scott Batey, PhD - Tulane University School of Social Work; Rebecca Schnall, BSN, MPH, PhD - Columbia University;
Fabiana
Dos Santos,
PhD, MSN, RN - Columbia University School of Nursing
Streamlining Temporal Information Extraction: Integrating Rule-Based Methods into MedspaCy for Clinical Application
Poster Number: P33
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Information Extraction, Data Mining
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Electronic health records (EHRs) contain detailed records of a patient’s health over time, making it crucial to accurately identify the timing of a clinical event. In this work, we introduce a rule-based temporal entity extraction package for medspaCy, which can adapt to any spaCy-styled tokenizers, as well as handle additional types of implicit expressions.
Speaker:
Mengke Hu, PHD
University of Utah
Authors:
Mengke Hu, PhD - University of Utah; Alec Chapman, MS - University of Utah; Patrick Alba, MS - United States Department of Veterans Affairs; Jianlin Shi, MD, PhD - The Division of Epidemiology, School of Medicine, University of Utah; VA Salt Lake City Healthcare System;
Poster Number: P33
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Information Extraction, Data Mining
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Electronic health records (EHRs) contain detailed records of a patient’s health over time, making it crucial to accurately identify the timing of a clinical event. In this work, we introduce a rule-based temporal entity extraction package for medspaCy, which can adapt to any spaCy-styled tokenizers, as well as handle additional types of implicit expressions.
Speaker:
Mengke Hu, PHD
University of Utah
Authors:
Mengke Hu, PhD - University of Utah; Alec Chapman, MS - University of Utah; Patrick Alba, MS - United States Department of Veterans Affairs; Jianlin Shi, MD, PhD - The Division of Epidemiology, School of Medicine, University of Utah; VA Salt Lake City Healthcare System;
Mengke
Hu,
PHD - University of Utah
Identifying Documented Goals of Care Conversations Using Definition-informed Chain-of-Thought Prompting (DiCoT)
Poster Number: P35
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Bioinformatics, Deep Learning
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Goals-of-care discussions (GoCD) are essential for aligning care with patient priorities, yet their documentation varies, challenging automated detection using Natural Language Processing (NLP). While fine-tuned BERT models perform well in identifying GoCD in clinical notes, their reliance on large annotated datasets and limited context length constrains their utility. Large Language Models (LLMs) offer a promising alternative, requiring minimal labeled data while handling longer text sequences.
In this study, we present the first evaluation of an LLM for GoCD detection, comparing its performance against a fine-tuned BERT model. We assessed both zero-shot performance and the utility of a novel GoCD-focused version of Chain-of-Thought prompting, Definition-informed CoT (DiCOT), on the task of extracting GoCDs. Our findings suggest that LLMs prompted using DiCoT can match end-to-end fine-tuning models in identifying GoCD without requiring annotated training data. This offers a practical alternative as it reduces the need for resource-intensive annotation requirements while maintaining performance.
Speaker:
Kevin Li, PhD Candidate
University of Washington
Authors:
Kevin Li, PhD Candidate - University of Washington; Trevor Cohen, MBChB, PhD - Biomedical Informatics and Medical Education, University of Washington; William Lober, PHD - Biomedical Informatics and Medical Education, University of Washington; Robert Lee, MD - Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington;
Poster Number: P35
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Bioinformatics, Deep Learning
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Goals-of-care discussions (GoCD) are essential for aligning care with patient priorities, yet their documentation varies, challenging automated detection using Natural Language Processing (NLP). While fine-tuned BERT models perform well in identifying GoCD in clinical notes, their reliance on large annotated datasets and limited context length constrains their utility. Large Language Models (LLMs) offer a promising alternative, requiring minimal labeled data while handling longer text sequences.
In this study, we present the first evaluation of an LLM for GoCD detection, comparing its performance against a fine-tuned BERT model. We assessed both zero-shot performance and the utility of a novel GoCD-focused version of Chain-of-Thought prompting, Definition-informed CoT (DiCOT), on the task of extracting GoCDs. Our findings suggest that LLMs prompted using DiCoT can match end-to-end fine-tuning models in identifying GoCD without requiring annotated training data. This offers a practical alternative as it reduces the need for resource-intensive annotation requirements while maintaining performance.
Speaker:
Kevin Li, PhD Candidate
University of Washington
Authors:
Kevin Li, PhD Candidate - University of Washington; Trevor Cohen, MBChB, PhD - Biomedical Informatics and Medical Education, University of Washington; William Lober, PHD - Biomedical Informatics and Medical Education, University of Washington; Robert Lee, MD - Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington;
Kevin
Li,
PhD Candidate - University of Washington
Quantifying the Effects of Social Determinants of Health on Adverse Birth Outcomes in Louisiana using Bayesian Linear Mixed-Effects Models
Poster Number: P83
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Health Equity and Social Determinants of Health (SDoH), Geospatial (GIS) Data/Analysis, Quantitative Methods, Public Health, Racial disparities
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Louisiana is the lowest ranking states in the United States of America in terms of maternal health outcomes. Previous works have highlighted the impact of social determinants of health on the incidence of adverse birth outcomes. That said, we are, to the best of our knowledge, the first to applied Bayesian linear mixed-effects models to this problem. Such models allow us to make more certain conclusions about the effects of social determinants of health on maternal health outcomes. Our models had excellent convergence and performance metrics. From these models we identified several significant effects on the incidence of adverse birth outcomes and explained a large amount of the variance in the rates of adverse birth outcomes. Some of these include Black and/or African American Population and days above 105°F. We also identified several clusters of autocorrelated parishes which seem to affect each other’s incidence of adverse birth outcomes.
Speaker:
José Irizarry Ayala, M.S.
Tulane University School of Medicine
Authors:
José Irizarry Ayala, M.S. - Tulane University School of Medicine; Jian Li, Ph.D. - Tulane University School of Public Health; David Crosslin, Ph.D. - Tulane University School of Medicine; W Susan Cheng, Ph.D., M.P.H. - Tulane University School of Public Health;
Poster Number: P83
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Health Equity and Social Determinants of Health (SDoH), Geospatial (GIS) Data/Analysis, Quantitative Methods, Public Health, Racial disparities
Primary Track: Applications
Programmatic Theme: Public Health Informatics
Louisiana is the lowest ranking states in the United States of America in terms of maternal health outcomes. Previous works have highlighted the impact of social determinants of health on the incidence of adverse birth outcomes. That said, we are, to the best of our knowledge, the first to applied Bayesian linear mixed-effects models to this problem. Such models allow us to make more certain conclusions about the effects of social determinants of health on maternal health outcomes. Our models had excellent convergence and performance metrics. From these models we identified several significant effects on the incidence of adverse birth outcomes and explained a large amount of the variance in the rates of adverse birth outcomes. Some of these include Black and/or African American Population and days above 105°F. We also identified several clusters of autocorrelated parishes which seem to affect each other’s incidence of adverse birth outcomes.
Speaker:
José Irizarry Ayala, M.S.
Tulane University School of Medicine
Authors:
José Irizarry Ayala, M.S. - Tulane University School of Medicine; Jian Li, Ph.D. - Tulane University School of Public Health; David Crosslin, Ph.D. - Tulane University School of Medicine; W Susan Cheng, Ph.D., M.P.H. - Tulane University School of Public Health;
José
Irizarry Ayala,
M.S. - Tulane University School of Medicine
Assessing the Impact of Race, Income, and Air Quality on Hospitalization Risk in Pediatric Asthma Patients: A Retrospective Observational Cohort Study
Poster Number: P82
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Health Equity and Social Determinants of Health (SDoH), Health Equity, Information Retrieval, Population Health, Pediatrics
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study provides insights into the interplay between race, income, air quality, and clinical factors in pediatric asthma hospitalizations. The principal finding of this study is that patients with previous inpatient admissions, Black patients, younger patients, patients residing in areas with lower air quality and lower income have a higher risk of follow-up inpatient admission for asthma within one year. Our results indicate potential gaps and the need for better interventions among vulnerable demographic groups.
Speaker:
Qingrui Wang, BS
JHU
Authors:
Qingrui Wang, BS - JHU; Robert Barrett, BS - Johns Hopkins University; Maria Sanchez, Master's Applied Health Science Informatics - Johns Hopkins University; Hadi Kharrazi, MD, PhD, FAMIA, FACMI - Johns Hopkins University;
Poster Number: P82
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Health Equity and Social Determinants of Health (SDoH), Health Equity, Information Retrieval, Population Health, Pediatrics
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This study provides insights into the interplay between race, income, air quality, and clinical factors in pediatric asthma hospitalizations. The principal finding of this study is that patients with previous inpatient admissions, Black patients, younger patients, patients residing in areas with lower air quality and lower income have a higher risk of follow-up inpatient admission for asthma within one year. Our results indicate potential gaps and the need for better interventions among vulnerable demographic groups.
Speaker:
Qingrui Wang, BS
JHU
Authors:
Qingrui Wang, BS - JHU; Robert Barrett, BS - Johns Hopkins University; Maria Sanchez, Master's Applied Health Science Informatics - Johns Hopkins University; Hadi Kharrazi, MD, PhD, FAMIA, FACMI - Johns Hopkins University;
Qingrui
Wang,
BS - JHU
CDEMapper 2.0: Expediting collaborative review and facilitating consensus building during Common Data Elements mapping
Poster Number: P98
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Data Standards, Interoperability and Health Information Exchange, Controlled Terminologies, Ontologies, and Vocabularies
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
In this study, we developed and evaluated CDEMapper 2.0, a collaborative tool designed to streamline the mapping of study variables to NIH Common Data Elements (CDEs). It supports semantic search, large language model (LLM)-based recommendations, multi-role and multi-user management, multiple experts review, iterative refinement, and consensus building during CDE mapping. Usability evaluation demonstrated its potential to expedite collaborative review, improve consistency, and address key barriers to broader CDE adoption.
Speaker:
Vincent Zhang, MS
Yale University
Authors:
Vincent Zhang, MS - Yale University; Huan He, Ph.D. - Yale University; Lingfei Qian, PHD - Yale University; Yujia Zhou, M.S. - Yale University; Yan Wang, PhD - Yale University; Ruey-Ling Weng, MS. - Yale University; Jihoon Kim, PhD - Yale University; Hua Xu, Ph.D - Yale University; Na Hong, PhD - Yale University;
Poster Number: P98
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Data Standards, Interoperability and Health Information Exchange, Controlled Terminologies, Ontologies, and Vocabularies
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
In this study, we developed and evaluated CDEMapper 2.0, a collaborative tool designed to streamline the mapping of study variables to NIH Common Data Elements (CDEs). It supports semantic search, large language model (LLM)-based recommendations, multi-role and multi-user management, multiple experts review, iterative refinement, and consensus building during CDE mapping. Usability evaluation demonstrated its potential to expedite collaborative review, improve consistency, and address key barriers to broader CDE adoption.
Speaker:
Vincent Zhang, MS
Yale University
Authors:
Vincent Zhang, MS - Yale University; Huan He, Ph.D. - Yale University; Lingfei Qian, PHD - Yale University; Yujia Zhou, M.S. - Yale University; Yan Wang, PhD - Yale University; Ruey-Ling Weng, MS. - Yale University; Jihoon Kim, PhD - Yale University; Hua Xu, Ph.D - Yale University; Na Hong, PhD - Yale University;
Vincent
Zhang,
MS - Yale University
Identifying Genitourinary Symptoms in Medical Notes with Open-Source Large Language Models
Poster Number: P55
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Artificial Intelligence
Primary Track: Foundations
Early detection of diseases relied on the accurate identification of clinical signs and symptoms. However, most of the symptom-related data in electronic health records (EHRs) was in unstructured clinical notes, posing challenges for automated extraction. This study explored the feasibility of using the Llama 3.3-70B large language model (LLM) to extract genitourinary symptoms from clinical text and map them to corresponding ICD-10 codes. Signs and symptoms from ICD-10 code R30-R39 were included. Using clinical notes from the MTSamples database, we evaluated three prompt-engineering strategies with an assumption-free constraints approach achieving the best performance. This method achieved an F1-score of 0.92 for symptom and sign extraction and 0.89 for ICD-10 code mapping. These findings showed the potential of using locally deployed LLMs to improve clinical text processing while maintaining data privacy.
Speaker:
Yunbing Bai, MS
University of Utah
Authors:
Yunbing Bai, MS - University of Utah; Wanting Cui, Masters - University of Utah; Joseph Finkelstein, MD, PhD - University of Utah;
Poster Number: P55
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Artificial Intelligence
Primary Track: Foundations
Early detection of diseases relied on the accurate identification of clinical signs and symptoms. However, most of the symptom-related data in electronic health records (EHRs) was in unstructured clinical notes, posing challenges for automated extraction. This study explored the feasibility of using the Llama 3.3-70B large language model (LLM) to extract genitourinary symptoms from clinical text and map them to corresponding ICD-10 codes. Signs and symptoms from ICD-10 code R30-R39 were included. Using clinical notes from the MTSamples database, we evaluated three prompt-engineering strategies with an assumption-free constraints approach achieving the best performance. This method achieved an F1-score of 0.92 for symptom and sign extraction and 0.89 for ICD-10 code mapping. These findings showed the potential of using locally deployed LLMs to improve clinical text processing while maintaining data privacy.
Speaker:
Yunbing Bai, MS
University of Utah
Authors:
Yunbing Bai, MS - University of Utah; Wanting Cui, Masters - University of Utah; Joseph Finkelstein, MD, PhD - University of Utah;
Yunbing
Bai,
MS - University of Utah
Cross-validation of Machine Learning for All-Cause Mortality and Cancer-Specific Mortality Prediction in Prostate and Breast Cancer
Poster Number: P99
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Data Standards, Evaluation, Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Prostate and breast cancer pose significant health challenges, especially for older and underserved populations. This study utilizes SEER-MHOS patient-reported outcomes to predict one-year and five-year all-cause and cancer-specific mortality, emphasizing standardized machine learning (ML) evaluation. Five of Wojtusiak’s ten criteria guide the analysis to enhance reproducibility and transparency. A two-stage approach was applied to patients aged 65+, incorporating missing data imputation and 30-fold cross-validation for lookback window optimization, followed by predictive modeling with seven ML algorithms. Optimal lookback windows were 1,160 days for prostate cancer, 410 for breast, and 430 for the combined cohort. Random Forest consistently outperformed others (AUC: 0.82 prostate, 0.78 breast). Combining datasets improved sample size and predictive stability (AUC LR: 0.80). Findings highlight the importance of tailored modeling and robust data integration in improving mortality prediction accuracy and mitigating disparities in cancer outcomes.
Speaker:
HUAN-JU (Coco) SHIH, Doctoral Candidate
George Mason University
Authors:
Janusz Wojtusiak, PhD - George Mason University; Hua Min, PhD - George Mason University;
Poster Number: P99
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Data Standards, Evaluation, Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Prostate and breast cancer pose significant health challenges, especially for older and underserved populations. This study utilizes SEER-MHOS patient-reported outcomes to predict one-year and five-year all-cause and cancer-specific mortality, emphasizing standardized machine learning (ML) evaluation. Five of Wojtusiak’s ten criteria guide the analysis to enhance reproducibility and transparency. A two-stage approach was applied to patients aged 65+, incorporating missing data imputation and 30-fold cross-validation for lookback window optimization, followed by predictive modeling with seven ML algorithms. Optimal lookback windows were 1,160 days for prostate cancer, 410 for breast, and 430 for the combined cohort. Random Forest consistently outperformed others (AUC: 0.82 prostate, 0.78 breast). Combining datasets improved sample size and predictive stability (AUC LR: 0.80). Findings highlight the importance of tailored modeling and robust data integration in improving mortality prediction accuracy and mitigating disparities in cancer outcomes.
Speaker:
HUAN-JU (Coco) SHIH, Doctoral Candidate
George Mason University
Authors:
Janusz Wojtusiak, PhD - George Mason University; Hua Min, PhD - George Mason University;
HUAN-JU (Coco)
SHIH,
Doctoral Candidate - George Mason University
Autocomplete Using LLMs for Simplifying Health-related Texts
Poster Number: P34
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Delivering Health Information and Knowledge to the Public, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Health-literacy is critical for improving health outcomes. Unfortunately, it can be challenging to create educational materials that are accessible and few tools help content creators to write materials. In this paper, we explore autocompletion as a tool to help create content. We examine GPT3.5 Davinci for autocompletion on a corpus of simplified medical texts, which achieves ap- proximately 20% accuracy. We conclude with a number of potential options for improving this baseline performance.
Speaker:
David Kauchak, PhD
Pomona College
Authors:
Nick Morgenstein, BA - Pomona College; David Kauchak, PhD - Pomona College; Gondy Leroy, PhD - University of Arizona;
Poster Number: P34
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Natural Language Processing, Delivering Health Information and Knowledge to the Public, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Health-literacy is critical for improving health outcomes. Unfortunately, it can be challenging to create educational materials that are accessible and few tools help content creators to write materials. In this paper, we explore autocompletion as a tool to help create content. We examine GPT3.5 Davinci for autocompletion on a corpus of simplified medical texts, which achieves ap- proximately 20% accuracy. We conclude with a number of potential options for improving this baseline performance.
Speaker:
David Kauchak, PhD
Pomona College
Authors:
Nick Morgenstein, BA - Pomona College; David Kauchak, PhD - Pomona College; Gondy Leroy, PhD - University of Arizona;
David
Kauchak,
PhD - Pomona College
Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models
Poster Number: P56
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Artificial Intelligence, Deep Learning, Diagnostic Systems
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Post-Traumatic Stress Disorder (PTSD) remains underdiagnosed in clinical settings, presenting opportunities for automated detection to identify patients. This study evaluates natural language processing approaches for detecting PTSD from clinical interview transcripts. We compared general and mental health-specific transformer models (BERT/RoBERTa), embedding-based methods (SentenceBERT/LLaMA), and large language model prompting strategies (zero-shot/few-shot/chain-of-thought) using the DAIC-WOZ dataset. Domain-specific models significantly outperformed general models (Mental-RoBERTa F1=0.643 vs. RoBERTa-base 0.485). LLaMA embeddings with neural networks achieved the highest performance (F1=0.700). Zero-shot prompting using DSM-5 criteria yielded competitive results without training data (F1=0.657). Performance varied significantly across symptom severity and comorbidity status, with higher accuracy for severe PTSD cases and patients with comorbid depression. Our findings highlight the potential of domain-adapted embeddings and LLMs for scalable screening while underscoring the need for improved detection of nuanced presentations and offering insights for developing clinically viable AI tools for PTSD assessment.
Speaker:
Feng Chen, MS
University of Washington
Authors:
Feng Chen, MS - University of Washington; Dror Ben-Zeev, PhD - University of Washington; Gillian Sparks, BA - University of Washington; Arya Kadakia, BA - University of Washington; Trevor Cohen, MBChB, PhD - Biomedical Informatics and Medical Education, University of Washington;
Poster Number: P56
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Artificial Intelligence, Deep Learning, Diagnostic Systems
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Post-Traumatic Stress Disorder (PTSD) remains underdiagnosed in clinical settings, presenting opportunities for automated detection to identify patients. This study evaluates natural language processing approaches for detecting PTSD from clinical interview transcripts. We compared general and mental health-specific transformer models (BERT/RoBERTa), embedding-based methods (SentenceBERT/LLaMA), and large language model prompting strategies (zero-shot/few-shot/chain-of-thought) using the DAIC-WOZ dataset. Domain-specific models significantly outperformed general models (Mental-RoBERTa F1=0.643 vs. RoBERTa-base 0.485). LLaMA embeddings with neural networks achieved the highest performance (F1=0.700). Zero-shot prompting using DSM-5 criteria yielded competitive results without training data (F1=0.657). Performance varied significantly across symptom severity and comorbidity status, with higher accuracy for severe PTSD cases and patients with comorbid depression. Our findings highlight the potential of domain-adapted embeddings and LLMs for scalable screening while underscoring the need for improved detection of nuanced presentations and offering insights for developing clinically viable AI tools for PTSD assessment.
Speaker:
Feng Chen, MS
University of Washington
Authors:
Feng Chen, MS - University of Washington; Dror Ben-Zeev, PhD - University of Washington; Gillian Sparks, BA - University of Washington; Arya Kadakia, BA - University of Washington; Trevor Cohen, MBChB, PhD - Biomedical Informatics and Medical Education, University of Washington;
Feng
Chen,
MS - University of Washington
Balanced Cluster Canonical Correlation Analysis for Multimodal Data Integration in Alzheimer's Disease
Poster Number: P46
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Diagnostic Systems, Bioinformatics
Primary Track: Foundations
This study addresses the challenge of dataset class imbalance in Canonical Correlation Analysis (CCA), a fundamental statistical method for elucidating relationships between two sets of variables. We propose Balanced Cluster CCA, a novel approach that mitigates imbalance through re-weighting, ensuring unbiased class representation. Our method enhances the balance of the representations of the majority and minority projections, thereby improving the robustness and accuracy in the context of Alzheimer's Disease (AD) detection studies. The efficacy of Balanced Cluster CCA in addressing imbalance is demonstrated through rigorous experimental evaluation, comprising subsequent classification tasks on two sets of AD datasets. Results indicate that our approach significantly improves the performance of standard CCA in imbalanced scenarios, with potential implications for broader applications in biomedical research. Beyond AD research, Balanced Cluster CCA offers promising applications across biomedical domains where data imbalance commonly undermines analytical precision and clinical utility.
Speaker:
Boning Tong, MSE
University of Pennsylvania
Authors:
Boning Tong, MSE - University of Pennsylvania; Zhuoping Zhou, Master of Art - University of Pennsylvania; Jingxuan Bao, MA; Bojian Hou, PhD - University of Pennsylvania; Jason Moore, PhD, FACMI - Cedars-Sinai; Andrew Saykin, PsyD - Indiana University; Marylyn Ritchie, PhD - University of Pennsylvania, Perelman School of Medicine; Qi Long, Ph.D. - University of Pennsylvania; Li Shen, Ph.D. - University of Pennsylvania;
Poster Number: P46
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Diagnostic Systems, Bioinformatics
Primary Track: Foundations
This study addresses the challenge of dataset class imbalance in Canonical Correlation Analysis (CCA), a fundamental statistical method for elucidating relationships between two sets of variables. We propose Balanced Cluster CCA, a novel approach that mitigates imbalance through re-weighting, ensuring unbiased class representation. Our method enhances the balance of the representations of the majority and minority projections, thereby improving the robustness and accuracy in the context of Alzheimer's Disease (AD) detection studies. The efficacy of Balanced Cluster CCA in addressing imbalance is demonstrated through rigorous experimental evaluation, comprising subsequent classification tasks on two sets of AD datasets. Results indicate that our approach significantly improves the performance of standard CCA in imbalanced scenarios, with potential implications for broader applications in biomedical research. Beyond AD research, Balanced Cluster CCA offers promising applications across biomedical domains where data imbalance commonly undermines analytical precision and clinical utility.
Speaker:
Boning Tong, MSE
University of Pennsylvania
Authors:
Boning Tong, MSE - University of Pennsylvania; Zhuoping Zhou, Master of Art - University of Pennsylvania; Jingxuan Bao, MA; Bojian Hou, PhD - University of Pennsylvania; Jason Moore, PhD, FACMI - Cedars-Sinai; Andrew Saykin, PsyD - Indiana University; Marylyn Ritchie, PhD - University of Pennsylvania, Perelman School of Medicine; Qi Long, Ph.D. - University of Pennsylvania; Li Shen, Ph.D. - University of Pennsylvania;
Boning
Tong,
MSE - University of Pennsylvania
Using Large Language Models for Thematic Analysis of Cognitive Concerns in Subjective Cognitive Decline: An EHR-Based Study
Poster Number: P57
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Data Standards, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Subjective cognitive decline (SCD) often represents the earliest identifiable stage of cognitive impairment. We conducted a retrospective study using electronic health records (EHRs) and large language models (LLMs) to categorize cognitive concerns in patients aged ≥65 with SCD. Our findings highlight the prevalence of specific types of concerns, particularly memory issues, and demonstrate the potential of LLMs for scalable, nuanced phenotyping of early cognitive symptoms to support targeted screening and intervention.
Speaker:
Liqin Wang, PhD
Brigham and Women's Hospital
Authors:
Liqin Wang, PhD - Brigham and Women's Hospital; Rebecca Amariglio, PhD - Brigham and Women's Hospital, Harvard Medical School; Jiazi Tian, Master of Biomedical Informatics - Massachusettes General Hospital; Sheril Varghese, BS - Columbia University Irving Medical Center; Li Zhou, MD, PhD, FACMI, FIAHSI, FAMIA - Brigham and Women's Hospital, Harvard Medical School; Gad Marshall, MD - Brigham and Women's Hospital, Harvard Medical School;
Poster Number: P57
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Data Standards, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Subjective cognitive decline (SCD) often represents the earliest identifiable stage of cognitive impairment. We conducted a retrospective study using electronic health records (EHRs) and large language models (LLMs) to categorize cognitive concerns in patients aged ≥65 with SCD. Our findings highlight the prevalence of specific types of concerns, particularly memory issues, and demonstrate the potential of LLMs for scalable, nuanced phenotyping of early cognitive symptoms to support targeted screening and intervention.
Speaker:
Liqin Wang, PhD
Brigham and Women's Hospital
Authors:
Liqin Wang, PhD - Brigham and Women's Hospital; Rebecca Amariglio, PhD - Brigham and Women's Hospital, Harvard Medical School; Jiazi Tian, Master of Biomedical Informatics - Massachusettes General Hospital; Sheril Varghese, BS - Columbia University Irving Medical Center; Li Zhou, MD, PhD, FACMI, FIAHSI, FAMIA - Brigham and Women's Hospital, Harvard Medical School; Gad Marshall, MD - Brigham and Women's Hospital, Harvard Medical School;
Liqin
Wang,
PhD - Brigham and Women's Hospital
LLM-Match: An Open-Sourced Patient Matching Model Based on Large Language Models and Retrieval-Augmented Generation
Poster Number: P58
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Patient matching is the process of linking patients to appropriate clinical trials by accurately identifying and matching their medical records with trial eligibility criteria. We propose LLM-Match, a novel framework for patient matching leveraging fine-tuned open-source large language models. Our approach consists of four key components. First, a retrieval-augmented generation (RAG) module extracts relevant patient context from a vast pool of electronic health records (EHRs). Second, a prompt generation module constructs input prompts by integrating trial eligibility criteria (both inclusion and exclusion criteria), patient context, and system instructions. Third, a fine-tuning module with a classification head optimizes the model parameters using structured prompts and ground-truth labels. Fourth, an evaluation module assesses the fine-tuned model's performance on the testing datasets. We evaluated LLM-Match on four open datasets—n2c2, SIGIR, TREC 2021, and TREC 2022—using open-source models, comparing it against TrialGPT, Zero-Shot, and GPT-4-based closed models. LLM-Match outperformed all baselines.
Speaker:
Xiaodi Li, Ph.D.
Mayo Clinic
Authors:
Xiaodi Li, Ph.D. - Mayo Clinic; Shaika Chowdhury, PhD - Mayo Clinic; Chung Wi, M.D. - Mayo Clinic; Maria Vassilaki, M.D., Ph.D. - Mayo Clinic; Xiaoke Liu, M.D., Ph.D. - Mayo Clinic; Terence Sio, M.D., M.S. - Mayo Clinic; Owen Garrick, M.D. - Mayo Clinic; Young Juhn, M.D., M.P.H. - Mayo Clinic; James Cerhan, M.D. Ph.D. - Mayo Clinic; Cui Tao, PhD - Mayo Clinic; Nansu Zong, Ph.D. - Mayo Clinic;
Poster Number: P58
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Artificial Intelligence
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Patient matching is the process of linking patients to appropriate clinical trials by accurately identifying and matching their medical records with trial eligibility criteria. We propose LLM-Match, a novel framework for patient matching leveraging fine-tuned open-source large language models. Our approach consists of four key components. First, a retrieval-augmented generation (RAG) module extracts relevant patient context from a vast pool of electronic health records (EHRs). Second, a prompt generation module constructs input prompts by integrating trial eligibility criteria (both inclusion and exclusion criteria), patient context, and system instructions. Third, a fine-tuning module with a classification head optimizes the model parameters using structured prompts and ground-truth labels. Fourth, an evaluation module assesses the fine-tuned model's performance on the testing datasets. We evaluated LLM-Match on four open datasets—n2c2, SIGIR, TREC 2021, and TREC 2022—using open-source models, comparing it against TrialGPT, Zero-Shot, and GPT-4-based closed models. LLM-Match outperformed all baselines.
Speaker:
Xiaodi Li, Ph.D.
Mayo Clinic
Authors:
Xiaodi Li, Ph.D. - Mayo Clinic; Shaika Chowdhury, PhD - Mayo Clinic; Chung Wi, M.D. - Mayo Clinic; Maria Vassilaki, M.D., Ph.D. - Mayo Clinic; Xiaoke Liu, M.D., Ph.D. - Mayo Clinic; Terence Sio, M.D., M.S. - Mayo Clinic; Owen Garrick, M.D. - Mayo Clinic; Young Juhn, M.D., M.P.H. - Mayo Clinic; James Cerhan, M.D. Ph.D. - Mayo Clinic; Cui Tao, PhD - Mayo Clinic; Nansu Zong, Ph.D. - Mayo Clinic;
Xiaodi
Li,
Ph.D. - Mayo Clinic
Enhanced Atrial Fibrillation Detection in ICU: Leveraging Novel ECG-Derived Features with Machine Learning
Poster Number: P45
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Evaluation, Quantitative Methods, Artificial Intelligence, Diagnostic Systems, Nursing Informatics, Informatics Implementation, Data transformation/ETL
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Atrial fibrillation (AF) is a prevalent cardiac condition associated with an increased risk of stroke and heart failure. Continuous monitoring of AF in intensive care units (ICUs) is crucial, yet bedside monitors often generate false AF alarms, leading to alarm fatigue. This study addresses this challenge by developing machine learning (ML) models analyzing electrocardiogram (ECG) features to distinguish AF from AF-alike conditions that commonly trigger false alarms. Clinicians annotated 30-second ECG waveforms from 723 AF alarms, identifying 161 (22.27%) as false. We compared engineered waveform features with foundation model-extracted ECG features, integrating patient demographics for AF detection. Among both linear and non-linear ML models evaluated, logistic regression demonstrated the best performance, achieving an AUROC of 87% and reducing the false alarm rate to 14.29%, while maintaining 90% sensitivity. This study highlights the potential of ML methods on ECG features and demographics to enhance AF monitoring in ICUs.
Speaker:
Andrew Lu, MSc, RN
Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University
Authors:
Andrew Lu, MSc, RN - Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University; Selene Cho, BS candidate - Department of Computer Science, Emory University, Georgia, USA; Zeyuan Meng, BS - Department of Computer Science, Emory University, Georgia, USA; Runze Yan, PhD - Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Georgia, USA; Saurabh Kataria, PhD - Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Georgia, USA; Xiao Hu, PhD - Emory University; Ran Xiao, PhD - Emory University;
Poster Number: P45
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Machine Learning, Evaluation, Quantitative Methods, Artificial Intelligence, Diagnostic Systems, Nursing Informatics, Informatics Implementation, Data transformation/ETL
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Atrial fibrillation (AF) is a prevalent cardiac condition associated with an increased risk of stroke and heart failure. Continuous monitoring of AF in intensive care units (ICUs) is crucial, yet bedside monitors often generate false AF alarms, leading to alarm fatigue. This study addresses this challenge by developing machine learning (ML) models analyzing electrocardiogram (ECG) features to distinguish AF from AF-alike conditions that commonly trigger false alarms. Clinicians annotated 30-second ECG waveforms from 723 AF alarms, identifying 161 (22.27%) as false. We compared engineered waveform features with foundation model-extracted ECG features, integrating patient demographics for AF detection. Among both linear and non-linear ML models evaluated, logistic regression demonstrated the best performance, achieving an AUROC of 87% and reducing the false alarm rate to 14.29%, while maintaining 90% sensitivity. This study highlights the potential of ML methods on ECG features and demographics to enhance AF monitoring in ICUs.
Speaker:
Andrew Lu, MSc, RN
Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University
Authors:
Andrew Lu, MSc, RN - Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University; Selene Cho, BS candidate - Department of Computer Science, Emory University, Georgia, USA; Zeyuan Meng, BS - Department of Computer Science, Emory University, Georgia, USA; Runze Yan, PhD - Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Georgia, USA; Saurabh Kataria, PhD - Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Georgia, USA; Xiao Hu, PhD - Emory University; Ran Xiao, PhD - Emory University;
Andrew
Lu,
MSc, RN - Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University
A novel approach to combine structured and narrative data annotation to improve EHR navigation: a demonstration study using OMOP.
Poster Number: P64
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Knowledge Representation and Information Modeling, Informatics Implementation, Data Standards
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Although electronic health records (EHRs) are capable of linking structured and unstructured data at the
encounter or patient level, a fully structured EHR that can significantly improve data synthesis is absent. We have
previously designed a semantically oriented EHR navigation dashboard but encountered challenges to conduct a
usability evaluation in real-world settings due to laborious manual annotations of structured and narrative data. We therefore developed a user-focused tool to facilitate these annotations using data stored in an Observational Medical Outcomes Partnership (OMOP) database. Time to annotate the concepts of an inpatient encounter went from 14 hours with a manual approach using a custom form to 3 hours and 42 minutes using our novel editor. We describe our editor and demonstrate its use with a clinical use case using real patient data to populate our navigation dashboard as a preparation for a future usability evaluation in real clinical settings.
Speaker:
Nicholas Timkovich, MSHI
UAB Health System
Authors:
Tiago Colicchio, PhD., MBA - University of Alabama at Birmingham; James Cimino, MD, FACMI, FACP, FAMIA, FIAHSI - Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham; John Osborne, PhD - University of Alabama at Birmingham; Kaiwen He, MSc - University of Alabama at Birmingham;
Poster Number: P64
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Knowledge Representation and Information Modeling, Informatics Implementation, Data Standards
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Although electronic health records (EHRs) are capable of linking structured and unstructured data at the
encounter or patient level, a fully structured EHR that can significantly improve data synthesis is absent. We have
previously designed a semantically oriented EHR navigation dashboard but encountered challenges to conduct a
usability evaluation in real-world settings due to laborious manual annotations of structured and narrative data. We therefore developed a user-focused tool to facilitate these annotations using data stored in an Observational Medical Outcomes Partnership (OMOP) database. Time to annotate the concepts of an inpatient encounter went from 14 hours with a manual approach using a custom form to 3 hours and 42 minutes using our novel editor. We describe our editor and demonstrate its use with a clinical use case using real patient data to populate our navigation dashboard as a preparation for a future usability evaluation in real clinical settings.
Speaker:
Nicholas Timkovich, MSHI
UAB Health System
Authors:
Tiago Colicchio, PhD., MBA - University of Alabama at Birmingham; James Cimino, MD, FACMI, FACP, FAMIA, FIAHSI - Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham; John Osborne, PhD - University of Alabama at Birmingham; Kaiwen He, MSc - University of Alabama at Birmingham;
Nicholas
Timkovich,
MSHI - UAB Health System
Enhancing Clinical Prediction Models through LLM-Agent
Poster Number: P59
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Artificial Intelligence
Primary Track: Applications
This study presents an LLM-agent framework to enhance clinical prediction models for in-hospital mortality in heart failure and COPD patients. By integrating GPT-4 into traditional ML pipelines, the system improves hyperparameter tuning and post-hoc prediction refinement. Using MIMIC-IV data, AUC performance improved across four ML models, most notably with XGBoost. Results demonstrate that LLM-agents can boost model accuracy, interpretability, and scalability, offering promising support for clinical risk stratification and decision-making.
Speaker:
Mohammed Al-Garadi, PhD
VUMC
Author:
Shui Mauser, Bachelor's Degree - Vanderbilt University Medical Center;
Poster Number: P59
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Artificial Intelligence
Primary Track: Applications
This study presents an LLM-agent framework to enhance clinical prediction models for in-hospital mortality in heart failure and COPD patients. By integrating GPT-4 into traditional ML pipelines, the system improves hyperparameter tuning and post-hoc prediction refinement. Using MIMIC-IV data, AUC performance improved across four ML models, most notably with XGBoost. Results demonstrate that LLM-agents can boost model accuracy, interpretability, and scalability, offering promising support for clinical risk stratification and decision-making.
Speaker:
Mohammed Al-Garadi, PhD
VUMC
Author:
Shui Mauser, Bachelor's Degree - Vanderbilt University Medical Center;
Mohammed
Al-Garadi,
PhD - VUMC
Developing an LLM-based conversational agent for Primary-Care Pre-visit Planning (PCP-Bot)
Poster Number: P60
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Documentation Burden, Workflow, Clinical Decision Support, Healthcare Quality, Patient Engagement and Preferences, User-centered Design Methods
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Pre-visit planning is critical for optimizing clinical workflows, yet traditional over-structured questionnaires burden patients and providers. We developed PCP-Bot, an LLM-based conversational assistant using GPT-4o to collect and summarize patient histories into concise, structured reports. PCP-Bot demonstrated high coherence, readability, and ease of use with minimal hallucinations through simulated clinical scenarios and physician evaluations. Results indicate strong physician acceptance, suggesting the potential to enhance efficiency and patient care in primary care settings overall.
Speaker:
Amogh Ananda Rao, MBBS, MS
University of Pennsylvania
Authors:
Pei-Lun Chen, Master of Science in Bioengineering - University of Pennsylvania; Sydney Pugh, PhD - University of Pennsylvania; Kevin Johnson, MD, MS - University of Pennsylvania;
Poster Number: P60
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Large Language Models (LLMs), Natural Language Processing, Documentation Burden, Workflow, Clinical Decision Support, Healthcare Quality, Patient Engagement and Preferences, User-centered Design Methods
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Pre-visit planning is critical for optimizing clinical workflows, yet traditional over-structured questionnaires burden patients and providers. We developed PCP-Bot, an LLM-based conversational assistant using GPT-4o to collect and summarize patient histories into concise, structured reports. PCP-Bot demonstrated high coherence, readability, and ease of use with minimal hallucinations through simulated clinical scenarios and physician evaluations. Results indicate strong physician acceptance, suggesting the potential to enhance efficiency and patient care in primary care settings overall.
Speaker:
Amogh Ananda Rao, MBBS, MS
University of Pennsylvania
Authors:
Pei-Lun Chen, Master of Science in Bioengineering - University of Pennsylvania; Sydney Pugh, PhD - University of Pennsylvania; Kevin Johnson, MD, MS - University of Pennsylvania;
Amogh
Ananda Rao,
MBBS, MS - University of Pennsylvania
Revising BPA triggers and inclusion criteria helps reduce nurses’ fatigue
Poster Number: P116
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Nursing Informatics, Human-computer Interaction
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Best Practice Advisories (BPAs) are rarely designed with nurses in mind. Yet nursing BPAs are 40% of all BPAs at NYC Health + Hospitals. Here, the BPA work group routinely revises alerts to reduce staff fatigue. Using data analysis and interviews, our study provides insight on the impact of the BPA work group, as well as design recommendations to further alleviate nurses’ alert fatigue.
Speaker:
Federica Bologna, MS
Cornell University
Authors:
Federica Bologna, MS - Cornell University; Anand Reddy, MD - NYC Health + Hospitals; David Silvestri, MD, MBA, MHS - NYC Health + Hospitals; Michael Bouton, MD - Harlem Hospital;
Poster Number: P116
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Nursing Informatics, Human-computer Interaction
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Best Practice Advisories (BPAs) are rarely designed with nurses in mind. Yet nursing BPAs are 40% of all BPAs at NYC Health + Hospitals. Here, the BPA work group routinely revises alerts to reduce staff fatigue. Using data analysis and interviews, our study provides insight on the impact of the BPA work group, as well as design recommendations to further alleviate nurses’ alert fatigue.
Speaker:
Federica Bologna, MS
Cornell University
Authors:
Federica Bologna, MS - Cornell University; Anand Reddy, MD - NYC Health + Hospitals; David Silvestri, MD, MBA, MHS - NYC Health + Hospitals; Michael Bouton, MD - Harlem Hospital;
Federica
Bologna,
MS - Cornell University
Towards a Structured Classification of Immune-Related Adverse Events to Inform a Clinical Decision Support System for Early Recognition in Cancer Patients on Immune Checkpoint Inhibitors
Poster Number: P117
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Mobile Health, Telemedicine, Healthcare Quality, User-centered Design Methods, Controlled Terminologies, Ontologies, and Vocabularies, Chronic Care Management, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Immune checkpoint inhibitors (ICIs) have transformed cancer treatment yet come with significant risks of immune-related adverse events (irAEs). Use of tools to predict irAE development are limited in clinical care. We sought to develop a clinical decision support (CDS) system to facilitate early recognition of irAEs. The CDS system will entail a patient-facing report system wherein cancer patients on ICIs can regularly report symptoms, with data pushed to the provider electronic health record's dashboard.
Speaker:
Onyekachi Ike-Okpe, MD
University Of Florida
Authors:
Thomas George, MD - Department of Medicine, University of Florida; Jiang Bian, PhD - Indiana University School of Medicine; Nicole Hammer, B.A. - University of Florida - Department of Health Outcomes and Biomedical Informatics; Protiva Rahman, PhD - University of Florida; Devika Das, MD - Department of Medicine, University of Florida; Yi Guo, PhD - University of Florida; Jennifer LeLaurin, PhD - University of Floria; Megan Gregory, Ph.D. - University of Florida;
Poster Number: P117
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Mobile Health, Telemedicine, Healthcare Quality, User-centered Design Methods, Controlled Terminologies, Ontologies, and Vocabularies, Chronic Care Management, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Immune checkpoint inhibitors (ICIs) have transformed cancer treatment yet come with significant risks of immune-related adverse events (irAEs). Use of tools to predict irAE development are limited in clinical care. We sought to develop a clinical decision support (CDS) system to facilitate early recognition of irAEs. The CDS system will entail a patient-facing report system wherein cancer patients on ICIs can regularly report symptoms, with data pushed to the provider electronic health record's dashboard.
Speaker:
Onyekachi Ike-Okpe, MD
University Of Florida
Authors:
Thomas George, MD - Department of Medicine, University of Florida; Jiang Bian, PhD - Indiana University School of Medicine; Nicole Hammer, B.A. - University of Florida - Department of Health Outcomes and Biomedical Informatics; Protiva Rahman, PhD - University of Florida; Devika Das, MD - Department of Medicine, University of Florida; Yi Guo, PhD - University of Florida; Jennifer LeLaurin, PhD - University of Floria; Megan Gregory, Ph.D. - University of Florida;
Onyekachi
Ike-Okpe,
MD - University Of Florida
A Machine Learning Approach to Predict Endometriosis Using EHR
Poster Number: P118
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Machine Learning, Data Mining
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Endometriosis is a chronic gynecological condition affecting 5-10% of women of reproductive age. Patients with
endometriosis often face delays in diagnosis due to the non-specific nature of symptoms. This study proposes a
predictive machine learning model to detect endometriosis patients using electronic health record data from
gynecology clinic visits at the University of Cincinnati Medical Center. Preliminary results indicated that among
seven machine learning algorithms, naïve bayes performed the best with an AUC of 0.79.
Speaker:
Parand Shams, Ph.D. Student
Cincinnati Children's Hospital Medical Center
Authors:
Mayur Sarangdhar, PhD - Cincinnati Children's Hospital Medical Center; Judith Dexheimer, PhD - Cincinnati Children's Hospital;
Poster Number: P118
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Machine Learning, Data Mining
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Endometriosis is a chronic gynecological condition affecting 5-10% of women of reproductive age. Patients with
endometriosis often face delays in diagnosis due to the non-specific nature of symptoms. This study proposes a
predictive machine learning model to detect endometriosis patients using electronic health record data from
gynecology clinic visits at the University of Cincinnati Medical Center. Preliminary results indicated that among
seven machine learning algorithms, naïve bayes performed the best with an AUC of 0.79.
Speaker:
Parand Shams, Ph.D. Student
Cincinnati Children's Hospital Medical Center
Authors:
Mayur Sarangdhar, PhD - Cincinnati Children's Hospital Medical Center; Judith Dexheimer, PhD - Cincinnati Children's Hospital;
Parand
Shams,
Ph.D. Student - Cincinnati Children's Hospital Medical Center
A Clinically Intuitive Approach to Evaluate Performance of Early Warning Scores
Poster Number: P119
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Machine Learning, Critical Care, Evaluation, Fairness and elimination of bias
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Early recognition of clinical deterioration improves patient outcomes. We retrospectively assessed two manual EWS—MEWS and NEWS—and three machine learning models: logistic regression, eXtreme Gradient Boosting (XGB), and long short-term memory, using a calendar-day evaluation framework. The calendar-day approach provided positive net benefits and higher utility compared to standard methods for all models, with XGB performing best. This framework offers a more clinically relevant evaluation of EWS performance.
Speaker:
Peng Wu, MS
University of Wisconsin-Madison
Authors:
Peng Wu, MS - University of Wisconsin-Madison; Kyle Carey, MPH - University of Chicago; Sierra Strutz, PhD Student in Biomedical Data Science - University of Wisconsin - Madison; Majid Afshar, MD, MSCR - University of Wisconsin - Madison; Dana Edelson, MD, MS - University of Chicago; Matthew Churpek, MD, MPH, PhD - University of Wisconsin-Madison; William Parker, MD, PhD - University of Chicago; Anoop Mayampurath, PhD - University of Wisconsin - Madison;
Poster Number: P119
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Machine Learning, Critical Care, Evaluation, Fairness and elimination of bias
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Early recognition of clinical deterioration improves patient outcomes. We retrospectively assessed two manual EWS—MEWS and NEWS—and three machine learning models: logistic regression, eXtreme Gradient Boosting (XGB), and long short-term memory, using a calendar-day evaluation framework. The calendar-day approach provided positive net benefits and higher utility compared to standard methods for all models, with XGB performing best. This framework offers a more clinically relevant evaluation of EWS performance.
Speaker:
Peng Wu, MS
University of Wisconsin-Madison
Authors:
Peng Wu, MS - University of Wisconsin-Madison; Kyle Carey, MPH - University of Chicago; Sierra Strutz, PhD Student in Biomedical Data Science - University of Wisconsin - Madison; Majid Afshar, MD, MSCR - University of Wisconsin - Madison; Dana Edelson, MD, MS - University of Chicago; Matthew Churpek, MD, MPH, PhD - University of Wisconsin-Madison; William Parker, MD, PhD - University of Chicago; Anoop Mayampurath, PhD - University of Wisconsin - Madison;
Peng
Wu,
MS - University of Wisconsin-Madison
Developing an Interpretable Breast Cancer Prediction Model Incorporating Demographic, Drug, and Disease Information
Poster Number: P120
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Human-computer Interaction, Machine Learning, Cancer Prevention
Primary Track: Applications
Breast cancer affects millions of women every year and is a leading cause of cancer related mortality. As such, early detection is critical for improving patient outcomes. In this work, we develop an explainable machine learning based predictive model for breast cancer risk. We report the construction of our cancer cohort utilizing demographic, drug, and disease information. We also report the model's classification accuracy, individual level classification uncertainty, and risk factors identified by the model.
Speaker:
Matthew Murrow, PhD
Vanderbilt University Medical Center
Authors:
Matthew Murrow, PhD - Vanderbilt University Medical Center; Zhijun Yin, Ph.D. - Vanderbilt University Medical Center; Congning Ni, Ph.D. student - Vanderbilt University; Xingyi Guo, PhD - Vanderbilt; Sharla Rahman, Master's Student - Vanderbilt;
Poster Number: P120
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Clinical Decision Support, Human-computer Interaction, Machine Learning, Cancer Prevention
Primary Track: Applications
Breast cancer affects millions of women every year and is a leading cause of cancer related mortality. As such, early detection is critical for improving patient outcomes. In this work, we develop an explainable machine learning based predictive model for breast cancer risk. We report the construction of our cancer cohort utilizing demographic, drug, and disease information. We also report the model's classification accuracy, individual level classification uncertainty, and risk factors identified by the model.
Speaker:
Matthew Murrow, PhD
Vanderbilt University Medical Center
Authors:
Matthew Murrow, PhD - Vanderbilt University Medical Center; Zhijun Yin, Ph.D. - Vanderbilt University Medical Center; Congning Ni, Ph.D. student - Vanderbilt University; Xingyi Guo, PhD - Vanderbilt; Sharla Rahman, Master's Student - Vanderbilt;
Matthew
Murrow,
PhD - Vanderbilt University Medical Center
Evaluating Data Quality in Initial and Repeat Blood Pressure Measurements
Poster Number: P121
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Workflow, Information Visualization, Healthcare Quality, Real-World Evidence Generation
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
While evaluating strategies for improving control of hypertension for our patients, we investigated the accuracy of blood pressure measurements within our system. In almost 500,000 measurements taken during primary care visits over 12 months, we noted that only 22% of elevated measurements were repeated and that the distribution of measurements showed terminal-digit bias, including a bias to record values just under the threshold for adjusting treatment, which became more prominent in repeat measurements.
Speaker:
Seneca Harberger, MD
Geisinger
Authors:
Alexander R. Chang, MD - Geisinger; Narayana Murali, MD - Geisinger; David Vawdrey, PhD - Geisinger;
Poster Number: P121
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Workflow, Information Visualization, Healthcare Quality, Real-World Evidence Generation
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
While evaluating strategies for improving control of hypertension for our patients, we investigated the accuracy of blood pressure measurements within our system. In almost 500,000 measurements taken during primary care visits over 12 months, we noted that only 22% of elevated measurements were repeated and that the distribution of measurements showed terminal-digit bias, including a bias to record values just under the threshold for adjusting treatment, which became more prominent in repeat measurements.
Speaker:
Seneca Harberger, MD
Geisinger
Authors:
Alexander R. Chang, MD - Geisinger; Narayana Murali, MD - Geisinger; David Vawdrey, PhD - Geisinger;
Seneca
Harberger,
MD - Geisinger
Assessing the Impact of a Digital Health Intervention on Self-Efficacy in Heart Failure Patients
Poster Number: P122
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Personal Health Informatics, Mobile Health, Human-computer Interaction, Patient / Person Generated Health Data (Patient Reported Outcomes), Delivering Health Information and Knowledge to the Public, Quantitative Methods
Working Group: Consumer Health Informatics Working Group
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Self-efficacy, or one’s confidence in their ability to carry out tasks, has been shown to have a powerful impact on the self-care behaviors of individuals with chronic conditions, including heart failure. This work explores whether using digital health tools for self-care impacts the self-efficacy of participants in a 6-month intervention. We find significant improvements in self-efficacy, and our results identify sociodemographic and psychosocial factors associated with changes in self-efficacy over the course the intervention.
Speaker:
Rachel Tunis, PhD Student
University of Texas at Austin
Authors:
Rachel Tunis, PhD Student - University of Texas at Austin; Namuun Clifford, MSN, RN, FNP-C - The University of Texas at Austin; Kavita Radhakrishnan, PhD - University of Texas - Austin; Nani Kim, BSN - University of Texas at Austin;
Poster Number: P122
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Personal Health Informatics, Mobile Health, Human-computer Interaction, Patient / Person Generated Health Data (Patient Reported Outcomes), Delivering Health Information and Knowledge to the Public, Quantitative Methods
Working Group: Consumer Health Informatics Working Group
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics
Self-efficacy, or one’s confidence in their ability to carry out tasks, has been shown to have a powerful impact on the self-care behaviors of individuals with chronic conditions, including heart failure. This work explores whether using digital health tools for self-care impacts the self-efficacy of participants in a 6-month intervention. We find significant improvements in self-efficacy, and our results identify sociodemographic and psychosocial factors associated with changes in self-efficacy over the course the intervention.
Speaker:
Rachel Tunis, PhD Student
University of Texas at Austin
Authors:
Rachel Tunis, PhD Student - University of Texas at Austin; Namuun Clifford, MSN, RN, FNP-C - The University of Texas at Austin; Kavita Radhakrishnan, PhD - University of Texas - Austin; Nani Kim, BSN - University of Texas at Austin;
Rachel
Tunis,
PhD Student - University of Texas at Austin
Physical Activity Phenotypes as Predictors of Pulmonary Rehabilitation Adherence
Poster Number: P123
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Patient Engagement and Preferences, Telemedicine
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
At-home pulmonary rehabilitation (PR) reduces barriers of traditional programs, but patient adherence and dropout remain challenges. In a 12-week at-home PR program for 375 COPD patients, adherence and dropout factors were examined, focusing on physical activity phenotypes. K-means clustering identified three phenotypes, but no significant associations with adherence or dropout were observed. Instead, higher dyspnea severity (mMRC) predicted lower adherence and increased dropout risk, highlighting the need to consider dyspnea severity for improving PR engagement.
Speaker:
Louis Faust, PhD
Mayo Clinic
Authors:
Louis Faust, PhD - Mayo Clinic; Stephanie Zawada, Ph.D. - Mayo Clinic; Johanna Hoult, MA - Mayo Clinic; Roberto Benzo, MD - Mayo Clinic; Emma Fortune Ngufor, Ph.D. - Mayo Clinic;
Poster Number: P123
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Patient Engagement and Preferences, Telemedicine
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
At-home pulmonary rehabilitation (PR) reduces barriers of traditional programs, but patient adherence and dropout remain challenges. In a 12-week at-home PR program for 375 COPD patients, adherence and dropout factors were examined, focusing on physical activity phenotypes. K-means clustering identified three phenotypes, but no significant associations with adherence or dropout were observed. Instead, higher dyspnea severity (mMRC) predicted lower adherence and increased dropout risk, highlighting the need to consider dyspnea severity for improving PR engagement.
Speaker:
Louis Faust, PhD
Mayo Clinic
Authors:
Louis Faust, PhD - Mayo Clinic; Stephanie Zawada, Ph.D. - Mayo Clinic; Johanna Hoult, MA - Mayo Clinic; Roberto Benzo, MD - Mayo Clinic; Emma Fortune Ngufor, Ph.D. - Mayo Clinic;
Louis
Faust,
PhD - Mayo Clinic
Dyadic Digital Health Interventions for Persons with Dementia and Their Family Caregivers: A Systematic Scoping Review
Poster Number: P124
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Mobile Health, User-centered Design Methods, Usability, Patient Engagement and Preferences, Social Media and Connected Health
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics
Innovative user-facing digital health solutions have the potential to meet the unique health needs of the growing number of adults with cognitive impairment, Alzheimer’s disease, or other dementias, along with their family or informal caregivers, but must be accessible, acceptable, and scalable. This scoping review will identify and describe dyadic digital health interventions for adults with cognitive impairment or dementia and their family caregivers. Results will inform future digital health development and implementation.
Speaker:
Cristina de Rosa, PhD, RN
University of Pittsburgh
Authors:
Cristina de Rosa, PhD, RN - University of Pittsburgh; Yuchen Zhang, BSN, RN - University of Pittsburgh; Woonkyung Kim, Doctoral Student, MSN, RN - University of Pittsburgh; Theresa Koleck, PhD, BSN, RN - University of Pittsburgh; Jennifer Lingler, PhD, MA, CRNP - University of Pittsburgh;
Poster Number: P124
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Chronic Care Management, Mobile Health, User-centered Design Methods, Usability, Patient Engagement and Preferences, Social Media and Connected Health
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics
Innovative user-facing digital health solutions have the potential to meet the unique health needs of the growing number of adults with cognitive impairment, Alzheimer’s disease, or other dementias, along with their family or informal caregivers, but must be accessible, acceptable, and scalable. This scoping review will identify and describe dyadic digital health interventions for adults with cognitive impairment or dementia and their family caregivers. Results will inform future digital health development and implementation.
Speaker:
Cristina de Rosa, PhD, RN
University of Pittsburgh
Authors:
Cristina de Rosa, PhD, RN - University of Pittsburgh; Yuchen Zhang, BSN, RN - University of Pittsburgh; Woonkyung Kim, Doctoral Student, MSN, RN - University of Pittsburgh; Theresa Koleck, PhD, BSN, RN - University of Pittsburgh; Jennifer Lingler, PhD, MA, CRNP - University of Pittsburgh;
Cristina
de Rosa,
PhD, RN - University of Pittsburgh
Automated Quantitative Assessment of Oral Cavity Dysplasia in Mouse Models of Oral Cavity Squamous Cell Carcinoma
Poster Number: P125
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Cancer Prevention, Imaging Informatics, Machine Learning, Precision Medicine
Primary Track: Applications
Dysplasia is a key but poorly understood transition in oral cavity
cancer development. Accurate, quantitative assessment is critical for
animal studies investigating dysplasia-driven tumorigenesis. However,
current evaluations are limited by subjective variability among
pathologists. We hypothesize that inter-rater variability in
pathologist-labeled dysplasia will be high and that AI-based image
analysis can enhance the consistency, robustness, and reproducibility
of dysplasia assessment in mouse models of oral cancer.
Speaker:
Sheel Tanna, B.S.
University of Chicago
Authors:
Joey Chan, M.S. - National Library of Medicine; Sheel Tanna, B.S. - University of Chicago; James Dolezal, M.D. - Geisinger Cancer Institute; Alexander Pearson, MD, PhD - University of Chicago Medical Center; Evgeny Izumchenko, PhD - University of Chicago;
Poster Number: P125
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Cancer Prevention, Imaging Informatics, Machine Learning, Precision Medicine
Primary Track: Applications
Dysplasia is a key but poorly understood transition in oral cavity
cancer development. Accurate, quantitative assessment is critical for
animal studies investigating dysplasia-driven tumorigenesis. However,
current evaluations are limited by subjective variability among
pathologists. We hypothesize that inter-rater variability in
pathologist-labeled dysplasia will be high and that AI-based image
analysis can enhance the consistency, robustness, and reproducibility
of dysplasia assessment in mouse models of oral cancer.
Speaker:
Sheel Tanna, B.S.
University of Chicago
Authors:
Joey Chan, M.S. - National Library of Medicine; Sheel Tanna, B.S. - University of Chicago; James Dolezal, M.D. - Geisinger Cancer Institute; Alexander Pearson, MD, PhD - University of Chicago Medical Center; Evgeny Izumchenko, PhD - University of Chicago;
Sheel
Tanna,
B.S. - University of Chicago
ChatGPT-4o proves effective in accurately diagnosing dermoscopic images of benign and malignant skin conditions
Poster Number: P126
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Cancer Prevention, Clinical Decision Support, Imaging Informatics, Informatics Implementation, Large Language Models (LLMs), Precision Medicine, Public Health, Telemedicine
Primary Track: Applications
Artificial intelligence (AI), such as ChatGPT-4o, hold promise in dermatologic diagnostics. This study assessed the accuracy of ChatGPT-4o in diagnosing ten common skin lesions using 150 dermoscopic images. Overall, first-attempt accuracy was 78% (117/150), with melanoma diagnosis achieving 100% sensitivity. Accuracy improved significantly to 96% within the top five differentials. Findings suggest ChatGPT-4o is a valuable diagnostic and educational tool, warranting further validation.
Speaker:
Mascha Korsch, PhD
University of Miami
Author:
Mascha Korsch, PhD - University of Miami;
Poster Number: P126
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Cancer Prevention, Clinical Decision Support, Imaging Informatics, Informatics Implementation, Large Language Models (LLMs), Precision Medicine, Public Health, Telemedicine
Primary Track: Applications
Artificial intelligence (AI), such as ChatGPT-4o, hold promise in dermatologic diagnostics. This study assessed the accuracy of ChatGPT-4o in diagnosing ten common skin lesions using 150 dermoscopic images. Overall, first-attempt accuracy was 78% (117/150), with melanoma diagnosis achieving 100% sensitivity. Accuracy improved significantly to 96% within the top five differentials. Findings suggest ChatGPT-4o is a valuable diagnostic and educational tool, warranting further validation.
Speaker:
Mascha Korsch, PhD
University of Miami
Author:
Mascha Korsch, PhD - University of Miami;
Mascha
Korsch,
PhD - University of Miami
Evaluating the Effectiveness of the Family First Prevention Services Act Using NLP and Staggered Difference-in-Differences Model
Poster Number: P127
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Bioinformatics, Public Health, Natural Language Processing, Evaluation
Primary Track: Policy
Programmatic Theme: Public Health Informatics
This study evaluates the Family First Prevention Services Act's (FFPSA) effectiveness in reducing child maltreatment rates across U.S. states using NLP to analyze FFPSA plans and a staggered Difference-in-Differences model. Results indicate FFPSA implementation significantly reduced maltreatment, particularly through prevention strategies and Qualified Residential Treatment Programs. However, kinship care promotion was associated with increased maltreatment. This study highlights the potential of FFPSA in improving outcomes but also identifies areas for further exploration to optimize its implementation.
Speaker:
Zhidi Luo, MS
Northwestern University
Authors:
Zhidi Luo, MS - Northwestern University; Richard Epstein, PhD - Northwestern University; Nethra Sambamoorthi, PhD - Northwestern University; Lutfiyya Muhammad, PhD - Northwestern University; Michael Cull, PhD - Center for the Helping Professions; Neil Jordan, PhD - Northwestern University;
Poster Number: P127
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Bioinformatics, Public Health, Natural Language Processing, Evaluation
Primary Track: Policy
Programmatic Theme: Public Health Informatics
This study evaluates the Family First Prevention Services Act's (FFPSA) effectiveness in reducing child maltreatment rates across U.S. states using NLP to analyze FFPSA plans and a staggered Difference-in-Differences model. Results indicate FFPSA implementation significantly reduced maltreatment, particularly through prevention strategies and Qualified Residential Treatment Programs. However, kinship care promotion was associated with increased maltreatment. This study highlights the potential of FFPSA in improving outcomes but also identifies areas for further exploration to optimize its implementation.
Speaker:
Zhidi Luo, MS
Northwestern University
Authors:
Zhidi Luo, MS - Northwestern University; Richard Epstein, PhD - Northwestern University; Nethra Sambamoorthi, PhD - Northwestern University; Lutfiyya Muhammad, PhD - Northwestern University; Michael Cull, PhD - Center for the Helping Professions; Neil Jordan, PhD - Northwestern University;
Zhidi
Luo,
MS - Northwestern University
Decoding Visual Narrative Processing: A Multimodal sEEG-Visual Signal Framework for Mapping Neural Dynamics
Poster Number: P128
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Bioinformatics, Computational Biology, Data Mining, Disability, Accessibility, and Human Function
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
sEEG amplitude fluctuations corresponded to narrative elements, we observed that it peaked during emotional climaxes and noted consistent directional interactions between the occipital and temporal cortices. These findings underscore how visual features shape brain connectivity, highlighting cortical synchronization in response to salient plot events. This work provides insight into the neural basis of narrative engagement and visual processing. Using a pipeline to analyze trial-based sEEG data from 10 epilepsy subjects watching annotated Hollywood movies.
Speaker:
Yu Sun, Master of Science
Yale
Authors:
Yu Sun, Master of Science - Yale; Fan Ma, Phd - yale; Hua Xu, Ph.D - Yale University;
Poster Number: P128
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Bioinformatics, Computational Biology, Data Mining, Disability, Accessibility, and Human Function
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
sEEG amplitude fluctuations corresponded to narrative elements, we observed that it peaked during emotional climaxes and noted consistent directional interactions between the occipital and temporal cortices. These findings underscore how visual features shape brain connectivity, highlighting cortical synchronization in response to salient plot events. This work provides insight into the neural basis of narrative engagement and visual processing. Using a pipeline to analyze trial-based sEEG data from 10 epilepsy subjects watching annotated Hollywood movies.
Speaker:
Yu Sun, Master of Science
Yale
Authors:
Yu Sun, Master of Science - Yale; Fan Ma, Phd - yale; Hua Xu, Ph.D - Yale University;
Yu
Sun,
Master of Science - Yale
Methodological reporting of studies evaluating the performance of conversational agents on unstructured data analysis in healthcare: Preliminary findings of a rapid review
Poster Number: P129
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Quantitative Methods, Evaluation
Primary Track: Applications
This rapid review aimed to analyze and evaluate the methodological content of research articles written on the use of conversational agents in healthcare settings. Overall, we found highly varied reporting of key methodological components ranging from 26.3% (how users interacted with outputs) to 83.2% (version number of agents used tied with description of prompts used). Future work will focus on developing a reporting guideline for future studies evaluating conversational agents.
Speaker:
Oliver Nguyen, MSHI
University of Wisconsin at Madison
Authors:
Arsalan Ahmad, MS - University of Wisconsin at Madison; Mahdi Fayazi, MS - University of Wisconsin at Madison; Douglas Wiegmann, PhD - University of Wisconsin at Madison;
Poster Number: P129
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Quantitative Methods, Evaluation
Primary Track: Applications
This rapid review aimed to analyze and evaluate the methodological content of research articles written on the use of conversational agents in healthcare settings. Overall, we found highly varied reporting of key methodological components ranging from 26.3% (how users interacted with outputs) to 83.2% (version number of agents used tied with description of prompts used). Future work will focus on developing a reporting guideline for future studies evaluating conversational agents.
Speaker:
Oliver Nguyen, MSHI
University of Wisconsin at Madison
Authors:
Arsalan Ahmad, MS - University of Wisconsin at Madison; Mahdi Fayazi, MS - University of Wisconsin at Madison; Douglas Wiegmann, PhD - University of Wisconsin at Madison;
Oliver
Nguyen,
MSHI - University of Wisconsin at Madison
Mapping AI’s Impact in Medical Informatics: A Text Mining Approach
Poster Number: P130
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Machine Learning, Natural Language Processing
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
This poster presents preliminary findings from an ongoing study exploring the impact of artificial intelligence in medical informatics through text mining. Based on an analysis of 1,018 research articles, keyword network analysis and bigram topic modeling were used to identify key themes such as EHRs, decision support, diagnostics, and AI-driven communication. The findings highlight AI’s expanding role and emerging trends across medical informatics, including machine learning, deep learning, and large language models.
Speaker:
Yong Hyun Park, MD, PhD
The Catholic University of Korea
Author:
Soohyung Joo;
Poster Number: P130
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Machine Learning, Natural Language Processing
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
This poster presents preliminary findings from an ongoing study exploring the impact of artificial intelligence in medical informatics through text mining. Based on an analysis of 1,018 research articles, keyword network analysis and bigram topic modeling were used to identify key themes such as EHRs, decision support, diagnostics, and AI-driven communication. The findings highlight AI’s expanding role and emerging trends across medical informatics, including machine learning, deep learning, and large language models.
Speaker:
Yong Hyun Park, MD, PhD
The Catholic University of Korea
Author:
Soohyung Joo;
Yong Hyun
Park,
MD, PhD - The Catholic University of Korea
Evaluating AI Voice Agents for Patient Education and Follow-up in Ophthalmology
Poster Number: P131
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This pilot study evaluates AI voice agents for patient education and follow-up in Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD), and Vabysmo treatment. Results showed high accuracy (95-98%), no hallucinations across 55 simulated interactions, and positive subjective feedback (Likert scores: 4.5-4.8). AI voice agents demonstrate promise for enhancing patient communication, warranting further clinical evaluation.
Speaker:
Jaison Jain, BS
Stony Brook University School of Medicine
Authors:
Jaison Jain, BS - Stony Brook University School of Medicine; Suraj Jain, BS - NYITCOM; James Kaan, MD - Penn State Health; Camilo Martinez, MD - Stony Brook University Hospital; Apostolos Tassiopoulos, MD - Stony Brook University Hospital;
Poster Number: P131
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This pilot study evaluates AI voice agents for patient education and follow-up in Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD), and Vabysmo treatment. Results showed high accuracy (95-98%), no hallucinations across 55 simulated interactions, and positive subjective feedback (Likert scores: 4.5-4.8). AI voice agents demonstrate promise for enhancing patient communication, warranting further clinical evaluation.
Speaker:
Jaison Jain, BS
Stony Brook University School of Medicine
Authors:
Jaison Jain, BS - Stony Brook University School of Medicine; Suraj Jain, BS - NYITCOM; James Kaan, MD - Penn State Health; Camilo Martinez, MD - Stony Brook University Hospital; Apostolos Tassiopoulos, MD - Stony Brook University Hospital;
Jaison
Jain,
BS - Stony Brook University School of Medicine
AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learning
Poster Number: P132
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Deep Learning
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Clinical calculators are essential for diagnosis, prognosis, and treatment planning but often remain underutilized due to usability and dissemination challenges. We propose AgentMD, an LLM-based autonomous agent that curates 2,164 clinical calculators. AgentMD achieved over 85% accuracy in quality checks and a 90%+ pass rate for unit tests. On the RiskQA benchmark, AgentMD significantly outperforms GPT-4 with chain-of-thought prompting (87.7% vs. 40.9%). These results highlight its potential to enhance clinical decision support and workflow efficiency.
Speaker:
Qiao Jin, M.D.
National Institutes of Health
Authors:
Qiao Jin, M.D. - National Institutes of Health; Nicholas Wan, BSc - National Institutes of Health; Zhizheng Wang, Ph.D - National Institutes of Health; Donald Wright, MD, MHS - Yale School of Medicine, Department of Emergency Medicine; Thomas Huang, BSc - Yale University; Nikhil Khandekar, BSc - Yale University; W. John Wilbur, MD - Computer Craft Corporation; Yifan Yang, B.S. - NCBI, NLM/NIH; Qingqing Zhu, PHD - National Institutes of Health; Xuguang Ai, MS in Data Science - Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University; Zhe He, PhD, FAMIA - Florida State University; Andrew Taylor, MD - Yale University; Qingyu Chen, PhD - Yale University; Zhiyong Lu, PhD - National Library of Medicine, NIH;
Poster Number: P132
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Deep Learning
Primary Track: Foundations
Programmatic Theme: Clinical Informatics
Clinical calculators are essential for diagnosis, prognosis, and treatment planning but often remain underutilized due to usability and dissemination challenges. We propose AgentMD, an LLM-based autonomous agent that curates 2,164 clinical calculators. AgentMD achieved over 85% accuracy in quality checks and a 90%+ pass rate for unit tests. On the RiskQA benchmark, AgentMD significantly outperforms GPT-4 with chain-of-thought prompting (87.7% vs. 40.9%). These results highlight its potential to enhance clinical decision support and workflow efficiency.
Speaker:
Qiao Jin, M.D.
National Institutes of Health
Authors:
Qiao Jin, M.D. - National Institutes of Health; Nicholas Wan, BSc - National Institutes of Health; Zhizheng Wang, Ph.D - National Institutes of Health; Donald Wright, MD, MHS - Yale School of Medicine, Department of Emergency Medicine; Thomas Huang, BSc - Yale University; Nikhil Khandekar, BSc - Yale University; W. John Wilbur, MD - Computer Craft Corporation; Yifan Yang, B.S. - NCBI, NLM/NIH; Qingqing Zhu, PHD - National Institutes of Health; Xuguang Ai, MS in Data Science - Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University; Zhe He, PhD, FAMIA - Florida State University; Andrew Taylor, MD - Yale University; Qingyu Chen, PhD - Yale University; Zhiyong Lu, PhD - National Library of Medicine, NIH;
Qiao
Jin,
M.D. - National Institutes of Health
Human vs. AI: Performance of AI in Literature Review Screening
Poster Number: P133
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Population Health
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
This study evaluates the performance of ChatGPT-4o in title and abstract screening for a literature review focused on studies involving researchers as subjects. From a 10% sample of 19,140 citations, AI screening showed lower sensitivity (76.0%) but higher specificity (92.4%) compared to humans (89.5% sensitivity, 77.1% specificity). While general-purpose AI lacks human-level sensitivity, it may augment review processes by improving specificity and identifying overlooked references, potentially reducing screening burden.
Speaker:
Amelia Shunk, MMCi
NYU Grossman School of Medicine
Authors:
Elizabeth Stevens, PhD, MPH - NYU Grossman School of Medicine; Natalie Henning; Amelia Shunk, MMCi - NYU Grossman School of Medicine; Gregory Laynor, PhD, MLS - NYU Grossman School of Medicine;
Poster Number: P133
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Population Health
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
This study evaluates the performance of ChatGPT-4o in title and abstract screening for a literature review focused on studies involving researchers as subjects. From a 10% sample of 19,140 citations, AI screening showed lower sensitivity (76.0%) but higher specificity (92.4%) compared to humans (89.5% sensitivity, 77.1% specificity). While general-purpose AI lacks human-level sensitivity, it may augment review processes by improving specificity and identifying overlooked references, potentially reducing screening burden.
Speaker:
Amelia Shunk, MMCi
NYU Grossman School of Medicine
Authors:
Elizabeth Stevens, PhD, MPH - NYU Grossman School of Medicine; Natalie Henning; Amelia Shunk, MMCi - NYU Grossman School of Medicine; Gregory Laynor, PhD, MLS - NYU Grossman School of Medicine;
Amelia
Shunk,
MMCi - NYU Grossman School of Medicine
Generative AI-Powered Dataset Retrieval Tool
Poster Number: P134
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Information Retrieval, Machine Learning, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Retrieving datasets from clinical data warehouses is a time-consuming and requires SQL expertise. We developed the Generative AI-Powered Dataset Retrieval Tool (GAPDART), which leverages Azure OpenAI to transform natural language input into executable SQL queries, enabling users to access the necessary data for predictive modeling without manual query build. We used GAPDART to retrieve data for an inpatient hypoglycemia prediction model. We recorded the average time to generate the AI-created SQL, token usage per variable, and data retrieval time from the clinical data warehouse. GAPDART successfully generated valid SQL queries and retrieved data for the six prediction variables and one outcome variable. On average, each query took 14.95 seconds to generate, utilized approximately 2,689 tokens, and required up to 8 seconds to retrieve data from the clinical data warehouse for each variable. GAPDART demonstrates the application of generative AI to simplify querying clinical data warehouses.
Speaker:
Aileen Wright, MD
Vanderbilt
Authors:
Aileen Wright, MD - Vanderbilt; Adam Wright, PhD - Vanderbilt University Medical Center; Peter Embi, MD - VUMC;
Poster Number: P134
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Information Retrieval, Machine Learning, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Retrieving datasets from clinical data warehouses is a time-consuming and requires SQL expertise. We developed the Generative AI-Powered Dataset Retrieval Tool (GAPDART), which leverages Azure OpenAI to transform natural language input into executable SQL queries, enabling users to access the necessary data for predictive modeling without manual query build. We used GAPDART to retrieve data for an inpatient hypoglycemia prediction model. We recorded the average time to generate the AI-created SQL, token usage per variable, and data retrieval time from the clinical data warehouse. GAPDART successfully generated valid SQL queries and retrieved data for the six prediction variables and one outcome variable. On average, each query took 14.95 seconds to generate, utilized approximately 2,689 tokens, and required up to 8 seconds to retrieve data from the clinical data warehouse for each variable. GAPDART demonstrates the application of generative AI to simplify querying clinical data warehouses.
Speaker:
Aileen Wright, MD
Vanderbilt
Authors:
Aileen Wright, MD - Vanderbilt; Adam Wright, PhD - Vanderbilt University Medical Center; Peter Embi, MD - VUMC;
Aileen
Wright,
MD - Vanderbilt
Leveraging Large Language Models to Enhance Machine Learning Interpretability and Predictive Performance: A Case Study on Emergency Department Returns for Mental Health Patients
Poster Number: P135
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Information Extraction, Large Language Models (LLMs), Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Emergency department (ED) returns for mental health patients are frequent and often preventable. This study explores the integration of large language models (LLMs) with traditional machine learning to improve prediction and interpretability. LLM-derived features enhanced performance and generated accurate, clinically meaningful explanations. The framework offers a promising step toward actionable, explainable AI in emergency psychiatry.
Speaker:
Abdulaziz Ahmed, PhD
University of Alabama at Birmingham
Authors:
Abdulaziz Ahmed, PhD - University of Alabama at Birmingham; Mohammad Saleem, MSc - University of Alabama at Birmingham; Mohammed Alzeen, M.D. - University of Alabama at Birmingham; Badari Birur, M.D. - University of Alabama at Birmingham; Rachel Fargason, M.D. - University of Alabama at Birmingham; Bradley Burk, PharmD - University of Alabama at Birmingham; Hannah Harkins, MSc - University of Alabama at Birmingham; Ahmed Alhassan, M.D. - University of Alabama at Birmingham; Mohammed Al-Garadi, PhD - VUMC;
Poster Number: P135
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Information Extraction, Large Language Models (LLMs), Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Emergency department (ED) returns for mental health patients are frequent and often preventable. This study explores the integration of large language models (LLMs) with traditional machine learning to improve prediction and interpretability. LLM-derived features enhanced performance and generated accurate, clinically meaningful explanations. The framework offers a promising step toward actionable, explainable AI in emergency psychiatry.
Speaker:
Abdulaziz Ahmed, PhD
University of Alabama at Birmingham
Authors:
Abdulaziz Ahmed, PhD - University of Alabama at Birmingham; Mohammad Saleem, MSc - University of Alabama at Birmingham; Mohammed Alzeen, M.D. - University of Alabama at Birmingham; Badari Birur, M.D. - University of Alabama at Birmingham; Rachel Fargason, M.D. - University of Alabama at Birmingham; Bradley Burk, PharmD - University of Alabama at Birmingham; Hannah Harkins, MSc - University of Alabama at Birmingham; Ahmed Alhassan, M.D. - University of Alabama at Birmingham; Mohammed Al-Garadi, PhD - VUMC;
Abdulaziz
Ahmed,
PhD - University of Alabama at Birmingham
Efficient Prediction of COPD Outcomes from Clinical Notes Using Filtered Input and Language Model Embeddings
Poster Number: P136
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Information Extraction, Large Language Models (LLMs)
Primary Track: Applications
We present a scalable framework for predicting COPD outcomes using unstructured clinical notes. By combining a lightweight note-filtering classifier with transformer-based embeddings (Clinical-Longformer), we reduced input volume by over 42% while maintaining comparable AUC performance across six outcomes. Our filtered pipeline lowers computational costs with minimal loss in predictive power, supporting real-time risk stratification in resource-constrained settings. This approach enables efficient, large-scale deployment of language model–based prediction in clinical practice.
Speaker:
Mohammed Al-Garadi, PhD
VUMC
Authors:
Sharon Davis, PhD - Vanderbilt University Medical Center; Michael Matheny, MD, MS, MPH, FACMI, FAMIA - Vanderbilt University Medical Center; Dax Westerman, MS - Vanderbilt University Medical Center Department of Biomedical Informatics; Bradley Richmond, PhD - VUMC; Adrienne Conger, M.D - VUMC; Thomas Lasko, MD, PhD - Vanderbilt University Medical Center; Iben Ricket, PhD - Dartmouth Center for Implementation Science Geisel School of Medicine Lebanon; Laura Paulin, MD MHS; Jeremiah Brown, PhD, MS - The Dartmouth Institute; Ruth Reeves - Tennessee Valley Health Care System, US Veterans' Affairs;
Poster Number: P136
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Information Extraction, Large Language Models (LLMs)
Primary Track: Applications
We present a scalable framework for predicting COPD outcomes using unstructured clinical notes. By combining a lightweight note-filtering classifier with transformer-based embeddings (Clinical-Longformer), we reduced input volume by over 42% while maintaining comparable AUC performance across six outcomes. Our filtered pipeline lowers computational costs with minimal loss in predictive power, supporting real-time risk stratification in resource-constrained settings. This approach enables efficient, large-scale deployment of language model–based prediction in clinical practice.
Speaker:
Mohammed Al-Garadi, PhD
VUMC
Authors:
Sharon Davis, PhD - Vanderbilt University Medical Center; Michael Matheny, MD, MS, MPH, FACMI, FAMIA - Vanderbilt University Medical Center; Dax Westerman, MS - Vanderbilt University Medical Center Department of Biomedical Informatics; Bradley Richmond, PhD - VUMC; Adrienne Conger, M.D - VUMC; Thomas Lasko, MD, PhD - Vanderbilt University Medical Center; Iben Ricket, PhD - Dartmouth Center for Implementation Science Geisel School of Medicine Lebanon; Laura Paulin, MD MHS; Jeremiah Brown, PhD, MS - The Dartmouth Institute; Ruth Reeves - Tennessee Valley Health Care System, US Veterans' Affairs;
Mohammed
Al-Garadi,
PhD - VUMC
Team Leadership Structure and Health AI Implementation Outcomes
Poster Number: P137
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Informatics Implementation, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
We investigated how team composition and leadership structure influence AI implementation outcomes in healthcare, adjusting for other AI- and clinically relevant confounders. Using results from 114 clinical studies and assuming that senior authorship reflects team leadership, we hypothesized that clinician (vs. technical)-led AI implementations are more likely to demonstrate significant impact. Logistic regression revealed that leadership structure significantly predicted AI impact, with clinical leadership associated with a higher likelihood of impact (OR = 4.55, p = 0.009).
Speaker:
Peilin Li, MS
Cornell University
Authors:
Qilu Li, MS - Weill Cornell Medical College; Peilin Li, Master of Science - Cornell University; Haijing Hao, PhD - Bentley University; Yiye Zhang, PhD - Weill Cornell Medicine;
Poster Number: P137
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Informatics Implementation, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
We investigated how team composition and leadership structure influence AI implementation outcomes in healthcare, adjusting for other AI- and clinically relevant confounders. Using results from 114 clinical studies and assuming that senior authorship reflects team leadership, we hypothesized that clinician (vs. technical)-led AI implementations are more likely to demonstrate significant impact. Logistic regression revealed that leadership structure significantly predicted AI impact, with clinical leadership associated with a higher likelihood of impact (OR = 4.55, p = 0.009).
Speaker:
Peilin Li, MS
Cornell University
Authors:
Qilu Li, MS - Weill Cornell Medical College; Peilin Li, Master of Science - Cornell University; Haijing Hao, PhD - Bentley University; Yiye Zhang, PhD - Weill Cornell Medicine;
Peilin
Li,
MS - Cornell University
A Resource-Conscious Approach Using Small LLMs for Structured Infection Indicator Extraction from Home Healthcare Clinical Notes
Poster Number: P138
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Infectious Diseases and Epidemiology, Natural Language Processing, Nursing Informatics, Deep Learning, Large Language Models (LLMs), Clinical Decision Support, Information Extraction
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Infections account for 80% of the top ten reasons for unplanned hospitalizations in home healthcare. Delivering structured, precise, and context-rich infection indicators from clinical notes is essential for prompt clinical interventions. However, limited resources and rapid patient dynamics present ongoing challenges in HHC settings. This study demonstrates that fine-tuned, resource-conscious small LLMs (<10B parameters) can effectively extract these indicators under realistic constraints, bridging the gap between high-resource LLMs and decentralized resources available in HHC settings.
Speaker:
Zidu Xu, Mphil,MMed
Columbia University
Authors:
Zidu Xu, Mphil,MMed - Columbia University; Jiyoun Song, PhD - University of Pennsylvania School of Nursing; Shuang Zhou, PhD - University of Minnesota Twin Cities; Danielle Scharp, MSN, BSN - Danielle Scharp; Mollie Hobensack, PhD, RN - Icahn School of Medicine at Mount Sinai; Jingjing Shang, PhD; Max Topaz, PhD, RN, MA, FAAN, FIAHSI, FACMI - Columbia University School of Nursing;
Poster Number: P138
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Infectious Diseases and Epidemiology, Natural Language Processing, Nursing Informatics, Deep Learning, Large Language Models (LLMs), Clinical Decision Support, Information Extraction
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Infections account for 80% of the top ten reasons for unplanned hospitalizations in home healthcare. Delivering structured, precise, and context-rich infection indicators from clinical notes is essential for prompt clinical interventions. However, limited resources and rapid patient dynamics present ongoing challenges in HHC settings. This study demonstrates that fine-tuned, resource-conscious small LLMs (<10B parameters) can effectively extract these indicators under realistic constraints, bridging the gap between high-resource LLMs and decentralized resources available in HHC settings.
Speaker:
Zidu Xu, Mphil,MMed
Columbia University
Authors:
Zidu Xu, Mphil,MMed - Columbia University; Jiyoun Song, PhD - University of Pennsylvania School of Nursing; Shuang Zhou, PhD - University of Minnesota Twin Cities; Danielle Scharp, MSN, BSN - Danielle Scharp; Mollie Hobensack, PhD, RN - Icahn School of Medicine at Mount Sinai; Jingjing Shang, PhD; Max Topaz, PhD, RN, MA, FAAN, FIAHSI, FACMI - Columbia University School of Nursing;
Zidu
Xu,
Mphil,MMed - Columbia University
Leveraging Vision-Language Models for Cephalometric Landmark Detection
Poster Number: P139
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Imaging Informatics, Personal Health Informatics, Clinical Decision Support, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Cephalometric analysis plays a critical role in orthodontic diagnosis and treatment planning.This study introduces an innovative approach utilizing pretrained Vision-Language Models (VLMs), specifically CLIP-based LLaVA, to automate cephalometric landmark detection in orthodontics. Using advanced zero-shot and few-shot prompting methods on the Aariz Cephalometric Dataset (1,000 images, 29 landmarks), our approach significantly improved landmark accuracy and workflow efficiency. Challenges remain in precise spatial localization, suggesting further domain-specific fine-tuning and multicentric validation for enhanced clinical utility.
Speaker:
Harsha Kamineni, Health Informatics- Masters
IUPUI
Author:
Harsha Kamineni, Health Informatics- Masters - IUPUI;
Poster Number: P139
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Imaging Informatics, Personal Health Informatics, Clinical Decision Support, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Cephalometric analysis plays a critical role in orthodontic diagnosis and treatment planning.This study introduces an innovative approach utilizing pretrained Vision-Language Models (VLMs), specifically CLIP-based LLaVA, to automate cephalometric landmark detection in orthodontics. Using advanced zero-shot and few-shot prompting methods on the Aariz Cephalometric Dataset (1,000 images, 29 landmarks), our approach significantly improved landmark accuracy and workflow efficiency. Challenges remain in precise spatial localization, suggesting further domain-specific fine-tuning and multicentric validation for enhanced clinical utility.
Speaker:
Harsha Kamineni, Health Informatics- Masters
IUPUI
Author:
Harsha Kamineni, Health Informatics- Masters - IUPUI;
Harsha
Kamineni,
Health Informatics- Masters - IUPUI
Enhancing Equity and Efficiency: AI-Powered Eligibility Assessment for Adult Complex Care
Poster Number: P140
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Health Equity, Population Health, Large Language Models (LLMs), Evaluation, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics
The Office of Population Health at our academic medical center provides Adult Complex Care services for high-utilizing patients, but manual eligibility determination is time-consuming. Using a HIPAA-compliant large language model (LLM) platform, we deployed a structured prompt to streamline chart review. In evaluating 27 charts, the model achieved 100% sensitivity and 77% specificity, with over-inclusion due to missing medical/social complexity criteria. Future refinements will enhance specificity, demonstrating LLMs’ potential to improve efficiency, consistency, and equity in eligibility assessment.
Speaker:
Sara Faghihi Kashani, MD, MPH
UCSF
Authors:
Sara Faghihi Kashani, MD, MPH - UCSF; Abimbola Fadairo-Azinge, MD,MPH, MMCI - UCSF; Joshua Munday, MSN, MPH, RN, FNP - UCSF; Timothy Judson, MD MPH - UCSF; Xinran Liu, MD, MS, FAMIA - UCSF;
Poster Number: P140
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Health Equity, Population Health, Large Language Models (LLMs), Evaluation, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics
The Office of Population Health at our academic medical center provides Adult Complex Care services for high-utilizing patients, but manual eligibility determination is time-consuming. Using a HIPAA-compliant large language model (LLM) platform, we deployed a structured prompt to streamline chart review. In evaluating 27 charts, the model achieved 100% sensitivity and 77% specificity, with over-inclusion due to missing medical/social complexity criteria. Future refinements will enhance specificity, demonstrating LLMs’ potential to improve efficiency, consistency, and equity in eligibility assessment.
Speaker:
Sara Faghihi Kashani, MD, MPH
UCSF
Authors:
Sara Faghihi Kashani, MD, MPH - UCSF; Abimbola Fadairo-Azinge, MD,MPH, MMCI - UCSF; Joshua Munday, MSN, MPH, RN, FNP - UCSF; Timothy Judson, MD MPH - UCSF; Xinran Liu, MD, MS, FAMIA - UCSF;
Sara
Faghihi Kashani,
MD, MPH - UCSF
Evaluating Approaches to Integrating Social Determinants in Synthetic Data Generation
Poster Number: P141
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Geospatial (GIS) Data/Analysis, Evaluation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Synthetic medical data offers distinct opportunities to facilitate secure data sharing and medical research, particularly when combined with Social Determinants of Health (SDoH), which are essential for promoting equitable healthcare. However, methodologies for effectively integrating SDoH with synthetic clinical data remain underexplored. This study evaluates post-joined and pre-joined approaches for integrating SDoH data with synthetic clinical data generated via a LLM-based generative AI model. The post-joined approach synthesizes clinical data separately and subsequently integrates it with real SDoH data using synthetic geographic identifiers, while the pre-joined approach simultaneously synthesizes clinical and SDoH variables. Evaluations using a real dataset of 8,813 cancer patients demonstrated that synthetic datasets maintain strong clinical-SDoH correlations, though biases emerge near zero-correlation points. Geographic analyses showed similar county-level value distributions but differences in patient geographic distributions between synthetic and real datasets. Future research should enhance generative models to address these biases and refine synthetic data generation.
Speaker:
Yili Zhang, PhD
Georgetown University
Authors:
Yili Zhang, PhD - Georgetown University; Jia Li Dong, MS - Georgetown University; Kanchi Krishnamurthy, MS - Georgetown University; Adil Alaoui - ICBI; Peter McGarvey, PhD - Georgetown University Medical Center;
Poster Number: P141
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Geospatial (GIS) Data/Analysis, Evaluation
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Synthetic medical data offers distinct opportunities to facilitate secure data sharing and medical research, particularly when combined with Social Determinants of Health (SDoH), which are essential for promoting equitable healthcare. However, methodologies for effectively integrating SDoH with synthetic clinical data remain underexplored. This study evaluates post-joined and pre-joined approaches for integrating SDoH data with synthetic clinical data generated via a LLM-based generative AI model. The post-joined approach synthesizes clinical data separately and subsequently integrates it with real SDoH data using synthetic geographic identifiers, while the pre-joined approach simultaneously synthesizes clinical and SDoH variables. Evaluations using a real dataset of 8,813 cancer patients demonstrated that synthetic datasets maintain strong clinical-SDoH correlations, though biases emerge near zero-correlation points. Geographic analyses showed similar county-level value distributions but differences in patient geographic distributions between synthetic and real datasets. Future research should enhance generative models to address these biases and refine synthetic data generation.
Speaker:
Yili Zhang, PhD
Georgetown University
Authors:
Yili Zhang, PhD - Georgetown University; Jia Li Dong, MS - Georgetown University; Kanchi Krishnamurthy, MS - Georgetown University; Adil Alaoui - ICBI; Peter McGarvey, PhD - Georgetown University Medical Center;
Yili
Zhang,
PhD - Georgetown University
On the Bias, Fairness and Bias Mitigation for a Wearable-based Freezing of Gait Detection in Parkinson’s Disease
Poster Number: P142
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Fairness and elimination of bias, Machine Learning, Health Equity, Clinical Decision Support, Deep Learning
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Consistent performance across diverse demographics is essential for wearable-based freezing of gait (FOG) detection in Parkinson's disease (PD). Our study revealed biases in existing wearable-based FOG models across demographic attributes in multiple datasets. Our proposed transfer learning from multi-site datasets and generic activity representations reduced bias (demographic parity ratio +0.027 & +0.039) and increased performance ( F1-score of +0.026 & +0.018). We hope this work encourages the development of unbiased artificial intelligence for PD populations.
Speaker:
Timothy Odonga, Masters
Emory University
Authors:
Timothy Odonga, Masters - Emory University; Hyeokhyen Kwon, Ph.D. - Emory University; Lucas McKay, Ph.D - Emory University; Christine Esper, MD - Emory University; Stewart Factor, DO - Emory University;
Poster Number: P142
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Fairness and elimination of bias, Machine Learning, Health Equity, Clinical Decision Support, Deep Learning
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
Consistent performance across diverse demographics is essential for wearable-based freezing of gait (FOG) detection in Parkinson's disease (PD). Our study revealed biases in existing wearable-based FOG models across demographic attributes in multiple datasets. Our proposed transfer learning from multi-site datasets and generic activity representations reduced bias (demographic parity ratio +0.027 & +0.039) and increased performance ( F1-score of +0.026 & +0.018). We hope this work encourages the development of unbiased artificial intelligence for PD populations.
Speaker:
Timothy Odonga, Masters
Emory University
Authors:
Timothy Odonga, Masters - Emory University; Hyeokhyen Kwon, Ph.D. - Emory University; Lucas McKay, Ph.D - Emory University; Christine Esper, MD - Emory University; Stewart Factor, DO - Emory University;
Timothy
Odonga,
Masters - Emory University
Fairness in AI-based Bruise Detection: Averaging vs. Separating Predictions
Poster Number: P143
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Fairness and elimination of bias, Imaging Informatics, Diversity, Equity, Inclusion, and Accessibility, Deep Learning
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Artificial intelligence-based bruise detection models may underperform on darker skin tones, raising fairness concerns. We compare two prediction handling approaches, averaging detections per image versus analyzing detections individually, to examine their impact on fairness using Demographic Parity and Equal Opportunity. Using a YOLOv5 model tested on 1,766 images, averaging yielded stable but less sensitive fairness estimates, while separation revealed greater disparities. Results suggest that prediction handling choices can influence fairness outcomes in bruise detection models.
Speaker:
Dharmi Desai, PhD
George Mason University
Authors:
Janusz Wojtusiak, PhD - George Mason University; Amin Nayebi Nodoushan, PhD - George Mason University; David Lattanzi, PhD, PE, FSEI - George Mason University; Katherine Scafide, PhD, RN, FAAN - George Mason University; Mehrdad Ghyabi, PhD - George Mason University; Dharmi Desai, MS - George Mason University;
Poster Number: P143
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Fairness and elimination of bias, Imaging Informatics, Diversity, Equity, Inclusion, and Accessibility, Deep Learning
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics
Artificial intelligence-based bruise detection models may underperform on darker skin tones, raising fairness concerns. We compare two prediction handling approaches, averaging detections per image versus analyzing detections individually, to examine their impact on fairness using Demographic Parity and Equal Opportunity. Using a YOLOv5 model tested on 1,766 images, averaging yielded stable but less sensitive fairness estimates, while separation revealed greater disparities. Results suggest that prediction handling choices can influence fairness outcomes in bruise detection models.
Speaker:
Dharmi Desai, PhD
George Mason University
Authors:
Janusz Wojtusiak, PhD - George Mason University; Amin Nayebi Nodoushan, PhD - George Mason University; David Lattanzi, PhD, PE, FSEI - George Mason University; Katherine Scafide, PhD, RN, FAAN - George Mason University; Mehrdad Ghyabi, PhD - George Mason University; Dharmi Desai, MS - George Mason University;
Dharmi
Desai,
PhD - George Mason University
Towards Safer AI in Healthcare: Using Storytelling to Educate About Unintended Harms of AI-Powered Tools
Poster Number: P144
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Fairness and elimination of bias, Large Language Models (LLMs), Natural Language Processing
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
We introduce a Human-AI Storytelling Framework that uses multi-agent large language models to simulate clinical narratives and expose potential harms from AI-powered healthcare tools. Our method identifies risks early by generating diverse, realistic stories that highlight failure modes and disparities. Evaluation using GPT-4o shows our method outperforms a template-based baseline in storytelling quality (79%) and risk realism (67.5%), helping stakeholders better anticipate and mitigate unintended consequences in healthcare AI systems before the system is developed.
Speaker:
Anthony Rios, PhD
University of Texas at San Antonio
Author:
Xingmeng Zhao, PHD - University o Texas San Antonio;
Poster Number: P144
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Fairness and elimination of bias, Large Language Models (LLMs), Natural Language Processing
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
We introduce a Human-AI Storytelling Framework that uses multi-agent large language models to simulate clinical narratives and expose potential harms from AI-powered healthcare tools. Our method identifies risks early by generating diverse, realistic stories that highlight failure modes and disparities. Evaluation using GPT-4o shows our method outperforms a template-based baseline in storytelling quality (79%) and risk realism (67.5%), helping stakeholders better anticipate and mitigate unintended consequences in healthcare AI systems before the system is developed.
Speaker:
Anthony Rios, PhD
University of Texas at San Antonio
Author:
Xingmeng Zhao, PHD - University o Texas San Antonio;
Anthony
Rios,
PhD - University of Texas at San Antonio
The Use of Computer Vision for Keystroke Detection for an Adaptive Assessment of Individual with Disability Capacity in Using a Smartphone
Poster Number: P145
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Deep Learning, Evaluation
Primary Track: Foundations
Programmatic Theme: Consumer Health Informatics
Existing keystroke assessment methods require environmental modifications that create barriers for users with disabilities. We aim to bridge this gap by developing a computer vision tool that captures keystrokes during natural smartphone interaction. This approach allows us to observe user behavior by capturing keystrokes without the environmental modifications that may influence user behavior. Our tool demonstrates promising results for keystroke detection with an F1 score of 99%.
Speaker:
Firdaus Indradhirmaya, PhD Student
University of Pittsburgh
Authors:
Firdaus Indradhirmaya, PhD Student - University of Pittsburgh; Andi Saptono, PhD - University of Pittsburgh School of Health and Rehabilitation Services;
Poster Number: P145
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Deep Learning, Evaluation
Primary Track: Foundations
Programmatic Theme: Consumer Health Informatics
Existing keystroke assessment methods require environmental modifications that create barriers for users with disabilities. We aim to bridge this gap by developing a computer vision tool that captures keystrokes during natural smartphone interaction. This approach allows us to observe user behavior by capturing keystrokes without the environmental modifications that may influence user behavior. Our tool demonstrates promising results for keystroke detection with an F1 score of 99%.
Speaker:
Firdaus Indradhirmaya, PhD Student
University of Pittsburgh
Authors:
Firdaus Indradhirmaya, PhD Student - University of Pittsburgh; Andi Saptono, PhD - University of Pittsburgh School of Health and Rehabilitation Services;
Firdaus
Indradhirmaya,
PhD Student - University of Pittsburgh
Implementing an AI Clinical Reasoning Model as a Second Site: Enhancements and Lessons Learned
Poster Number: P146
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Data Sharing, Large Language Models (LLMs), Education and Training
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This podium details the implementation of an AI-based Clinical Reasoning (CR) assessment model built at New York University (NYU) at the University of Cincinnati (UC) as a second site. Using previous Named Entity Recognition (NER) and Large Language Model (LLM) approaches, we fine-tuned and adapted previous models to achieve performance at our site. We share key lessons learned about the impact of documentation and technological differences, annotation pipelines, and fine-tuning strategies.
Speaker:
Scott Vennemeyer, BS
University of Cincinnati, College of Medicine
Authors:
Danny Wu, PhD - University of North Carolina at Chapel Hill; Hanniel Shih, MS - University of Cincinnati; Benedict Guzman, MS - NYU Langone Health; Jesse Burke-Rafel, MD - NYU Langone Health; Ilan Reinstein, MS - NYU Langone Health; Abbie Goodman, MD - University of Cincinnati; Danielle Weber, MD - University of Cincinnati;
Poster Number: P146
Presentation Time: 05:00 PM - 06:30 PM
Abstract Keywords: Artificial Intelligence, Data Sharing, Large Language Models (LLMs), Education and Training
Primary Track: Applications
Programmatic Theme: Clinical Informatics
This podium details the implementation of an AI-based Clinical Reasoning (CR) assessment model built at New York University (NYU) at the University of Cincinnati (UC) as a second site. Using previous Named Entity Recognition (NER) and Large Language Model (LLM) approaches, we fine-tuned and adapted previous models to achieve performance at our site. We share key lessons learned about the impact of documentation and technological differences, annotation pipelines, and fine-tuning strategies.
Speaker:
Scott Vennemeyer, BS
University of Cincinnati, College of Medicine
Authors:
Danny Wu, PhD - University of North Carolina at Chapel Hill; Hanniel Shih, MS - University of Cincinnati; Benedict Guzman, MS - NYU Langone Health; Jesse Burke-Rafel, MD - NYU Langone Health; Ilan Reinstein, MS - NYU Langone Health; Abbie Goodman, MD - University of Cincinnati; Danielle Weber, MD - University of Cincinnati;
Scott
Vennemeyer,
BS - University of Cincinnati, College of Medicine
Use of Advanced Design Methods for End-of-Life Care
Category
Poster - Student
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11/18/2025 06:30 PM (Eastern Time (US & Canada))