- Home
- 2025 Clinical Informatics Conference Gallery
- Designing Clinician Personas to Enhance Technology Adoption
Custom CSS
double-click to edit, do not edit in source
5/20/2025 |
4:00 PM – 5:30 PM |
California Ballroom C
Poster Session 1
Presentation Type: Poster
Building a Foundation of Quality Data for AI for Clinical Decision-Making: The Post-Acute Care InterOperability (PACIO) Project
Poster Number: P01
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Interoperability, Co-production/Co-Design, Cross-organization Partnerships including Public-private Partnerships, Health Information Exchange (HIE), Health IT Standards (USCDI, FHIR®, SMART, etc.)
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Artificial intelligence (AI) has tremendous potential to improve clinical care and decision-making. However, siloed systems and lack of standardized data across EHRs constrains the ability of AI to generate accurate and useful outputs. This is particularly relevant to in post-acute care (PAC) that often lack interoperability between other PACs and acute care settings, lead swaths of critical data missing from PAC patients records, potentially impacting AI used in their care. To address this issue, the Post Acute Care InterOperability Project (PACIO), to establish a framework for developing Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®)* implementation guides (IGs) to facilitate health information exchange between PAC and other entities. PACIO has applied user-centered design and evidence-based approaches to develop IGs, which were then tested in Connectathons with PAC stakeholders. The Connectathons have demonstrated the IGs' technical capabilities, provided opportunities to identify issues that implementers may face, and built community among PAC stakeholders. Next steps include developing new use cases and improving existing IG through Connectathons. By improving interoperability, PACIO has the potential to support an EHR ecosystem that is more complete, incorporating data from acute and PAC settings. This can better inform the development and application of AI to support clinical care.
Speaker:
Uba Backonja, PhD, MS, RN
The MITRE Corporation
Authors:
Uba Backonja, PhD, MS, RN - The MITRE Corporation; Jessica Skopac, PhD/JD/MA; Dave Hill, BS - MITRE; Howard Capon, MPH NRP; Brian Meshell, MS - MITRE; Lorraine Wickiser; Elizabeth Palena Hal, MIS MBA RN - Centers for Medicare & Medicaid Services; Alyssa Ford, MOT, DHSc - Indiana Wesleyan University;
Poster Number: P01
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Interoperability, Co-production/Co-Design, Cross-organization Partnerships including Public-private Partnerships, Health Information Exchange (HIE), Health IT Standards (USCDI, FHIR®, SMART, etc.)
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Artificial intelligence (AI) has tremendous potential to improve clinical care and decision-making. However, siloed systems and lack of standardized data across EHRs constrains the ability of AI to generate accurate and useful outputs. This is particularly relevant to in post-acute care (PAC) that often lack interoperability between other PACs and acute care settings, lead swaths of critical data missing from PAC patients records, potentially impacting AI used in their care. To address this issue, the Post Acute Care InterOperability Project (PACIO), to establish a framework for developing Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®)* implementation guides (IGs) to facilitate health information exchange between PAC and other entities. PACIO has applied user-centered design and evidence-based approaches to develop IGs, which were then tested in Connectathons with PAC stakeholders. The Connectathons have demonstrated the IGs' technical capabilities, provided opportunities to identify issues that implementers may face, and built community among PAC stakeholders. Next steps include developing new use cases and improving existing IG through Connectathons. By improving interoperability, PACIO has the potential to support an EHR ecosystem that is more complete, incorporating data from acute and PAC settings. This can better inform the development and application of AI to support clinical care.
Speaker:
Uba Backonja, PhD, MS, RN
The MITRE Corporation
Authors:
Uba Backonja, PhD, MS, RN - The MITRE Corporation; Jessica Skopac, PhD/JD/MA; Dave Hill, BS - MITRE; Howard Capon, MPH NRP; Brian Meshell, MS - MITRE; Lorraine Wickiser; Elizabeth Palena Hal, MIS MBA RN - Centers for Medicare & Medicaid Services; Alyssa Ford, MOT, DHSc - Indiana Wesleyan University;
Uba
Backonja,
PhD, MS, RN - The MITRE Corporation
Summarizing Image Dataset to Detect Malignancies using Supervised Deep Learning
Poster Number: P02
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Care Delivery Models, Adaptive Clinical Decision Support, Artificial Intelligence/Machine Learning, Data Science, Data Visualization
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
In this study, we explore a supervised deep learning approach with transfer learning to analyze 3D imaging scans for malignancy diagnosis. Each scan comprises hundreds of slices, though only a few may depict malignant lesions. Our model aimed to summarize datasets by focusing on these critical slices. However, initial results showed limited performance that suggests further exploration of advanced techniques, such as Reinforcement Learning, to enhance the ability to effectively distinguish malignancies within complex datasets.
Speaker:
Rayyan Khan, PhD
CancerCare Manitoba
Poster Number: P02
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Care Delivery Models, Adaptive Clinical Decision Support, Artificial Intelligence/Machine Learning, Data Science, Data Visualization
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
In this study, we explore a supervised deep learning approach with transfer learning to analyze 3D imaging scans for malignancy diagnosis. Each scan comprises hundreds of slices, though only a few may depict malignant lesions. Our model aimed to summarize datasets by focusing on these critical slices. However, initial results showed limited performance that suggests further exploration of advanced techniques, such as Reinforcement Learning, to enhance the ability to effectively distinguish malignancies within complex datasets.
Speaker:
Rayyan Khan, PhD
CancerCare Manitoba
Rayyan
Khan,
PhD - CancerCare Manitoba
Leveraging AI Knowledge-sharing Tools to Enhance Human-System Performance in EHR Environments
Poster Number: P03
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Usability and Measuring User Experience, Documentation Burden, Building Value for Informatics via Education and Training, Clinician Burnout, Workflow Efficiency, Patient Safety, Disruptive and Innovative Technologies
Primary Track: AI and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
As healthcare technology advances, the challenge of addressing usability and workflow inefficiencies within EHR systems becomes increasingly urgent. This poster presents innovative AI-driven knowledge-sharing tool, designed to strengthen usability competencies among VA informaticists. Our AI/ML framework enables dynamic knowledge sharing and real-time feedback, facilitating continuous improvement in user training and support. We will detail how this tool is used to identify and address workflow inefficiencies in the Veterans Health Information Systems and Technology Architecture (VistA) EHR and the new Federal EHR powered by Oracle Health technology. Additionally, we will demonstrate how AI-driven analytics can deliver actionable insights tailored to meet the needs of informaticists. By fostering interoperability and streamlining usability evaluations, our tool supports clinician satisfaction and improves patient care outcomes. This poster invites attendees to explore how AI-driven insights can transform usability training and enhance EHR user experience across the VHA network.
Speaker:
Ashley Ercolino, Ph.D.
Design Interactive
Authors:
Ashley Ercolino, Ph.D. - Design Interactive; Nicole Dorey, PhD - Design Interactive; Logan Gisick; Logan Gisick, PhD - Design Interactive; D’An Knowles Ball, PhD - Design Interactive; Arindam Gupta, M.S. - Design Interactive; Ross Speir, Human Factors Psychology, ABD - Veterans Affairs; Jesse Flint, MS - Design Interactive, Inc.; William Rivera, PhD - Design Interactive; Kay M. Stanney, PhD - Design Interactive;
Poster Number: P03
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Usability and Measuring User Experience, Documentation Burden, Building Value for Informatics via Education and Training, Clinician Burnout, Workflow Efficiency, Patient Safety, Disruptive and Innovative Technologies
Primary Track: AI and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
As healthcare technology advances, the challenge of addressing usability and workflow inefficiencies within EHR systems becomes increasingly urgent. This poster presents innovative AI-driven knowledge-sharing tool, designed to strengthen usability competencies among VA informaticists. Our AI/ML framework enables dynamic knowledge sharing and real-time feedback, facilitating continuous improvement in user training and support. We will detail how this tool is used to identify and address workflow inefficiencies in the Veterans Health Information Systems and Technology Architecture (VistA) EHR and the new Federal EHR powered by Oracle Health technology. Additionally, we will demonstrate how AI-driven analytics can deliver actionable insights tailored to meet the needs of informaticists. By fostering interoperability and streamlining usability evaluations, our tool supports clinician satisfaction and improves patient care outcomes. This poster invites attendees to explore how AI-driven insights can transform usability training and enhance EHR user experience across the VHA network.
Speaker:
Ashley Ercolino, Ph.D.
Design Interactive
Authors:
Ashley Ercolino, Ph.D. - Design Interactive; Nicole Dorey, PhD - Design Interactive; Logan Gisick; Logan Gisick, PhD - Design Interactive; D’An Knowles Ball, PhD - Design Interactive; Arindam Gupta, M.S. - Design Interactive; Ross Speir, Human Factors Psychology, ABD - Veterans Affairs; Jesse Flint, MS - Design Interactive, Inc.; William Rivera, PhD - Design Interactive; Kay M. Stanney, PhD - Design Interactive;
Ashley
Ercolino,
Ph.D. - Design Interactive
SNAP Judgments: Using LLMs and Pharmacy Dispense Data to Uncover Disparities in Pediatric AOM Antibiotic Prescribing and Utilization
Poster Number: P04
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Medication Adherence, Social Determinants of Health
Primary Track: AI and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
This study used an LLM-based system and novel SQL methodology to classify treatment plans and capture dispensing data for pediatric AOM, addressing the understudied area of SNAP utilization. Findings revealed disparities in SNAPs, with non-English speakers and lower SES patients less likely to receive SNAPs, highlighting equity issues.
Speaker:
Anh Vo, MD, MPH
UCSF Benioff Children's Hospitals
Authors:
Jessica Pourian, MD - UCSF; Ben Michaels, PharmD - UCSF; Anh Voh, MD MPH - UCSF; A J Holmgren, PhD - University of California, San Francisco; Augusto Garcia-Agundez, PhD - UCSF; Valerie Flaherman - UCSF;
Poster Number: P04
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Medication Adherence, Social Determinants of Health
Primary Track: AI and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
This study used an LLM-based system and novel SQL methodology to classify treatment plans and capture dispensing data for pediatric AOM, addressing the understudied area of SNAP utilization. Findings revealed disparities in SNAPs, with non-English speakers and lower SES patients less likely to receive SNAPs, highlighting equity issues.
Speaker:
Anh Vo, MD, MPH
UCSF Benioff Children's Hospitals
Authors:
Jessica Pourian, MD - UCSF; Ben Michaels, PharmD - UCSF; Anh Voh, MD MPH - UCSF; A J Holmgren, PhD - University of California, San Francisco; Augusto Garcia-Agundez, PhD - UCSF; Valerie Flaherman - UCSF;
Anh
Vo,
MD, MPH - UCSF Benioff Children's Hospitals
Evaluation of Discrimination Risk in Clinical Decision Support Tools: A Health Care System’s Approach
Poster Number: P05
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Algorithmic bias and impacts on Health Equity, Adaptive Clinical Decision Support, Risk Measurement, Patient Safety, Affordable Care Act (ACA), Ethical, Legal, and Social Issues
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
We present a framework for healthcare institutions to assess and mitigate discrimination risks in clinical decision support (CDS) tools, addressing Section 1557 compliance of the Affordable Care Act. The approach identifies CDS subtypes, stratifies risks, and proposes mitigation strategies. Ten CDS subtypes were identified, with three classified as high-risk. Recommendations include improving awareness, establishing governance, implementing monitoring protocols, and developing specific strategies for high-risk CDS subtypes.
Speaker:
Bethel Mieso, MD
Stanford
Authors:
Kameron Black, DO, MPH - Stanford University; Aydin Zahedivash, MD, MBA - Stanford University; Nicholas Marshall, MD - Stanford; Keith Morse, MD - Stanford University School of Medicine;
Poster Number: P05
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Algorithmic bias and impacts on Health Equity, Adaptive Clinical Decision Support, Risk Measurement, Patient Safety, Affordable Care Act (ACA), Ethical, Legal, and Social Issues
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
We present a framework for healthcare institutions to assess and mitigate discrimination risks in clinical decision support (CDS) tools, addressing Section 1557 compliance of the Affordable Care Act. The approach identifies CDS subtypes, stratifies risks, and proposes mitigation strategies. Ten CDS subtypes were identified, with three classified as high-risk. Recommendations include improving awareness, establishing governance, implementing monitoring protocols, and developing specific strategies for high-risk CDS subtypes.
Speaker:
Bethel Mieso, MD
Stanford
Authors:
Kameron Black, DO, MPH - Stanford University; Aydin Zahedivash, MD, MBA - Stanford University; Nicholas Marshall, MD - Stanford; Keith Morse, MD - Stanford University School of Medicine;
Bethel
Mieso,
MD - Stanford
Development and Validation of a Feature-Based Machine Learning for ECG Artifact Detection and Classification
Poster Number: P07
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Clinical informatics organizational models, Data Science
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
This study focuses on enhancing ECG diagnostic accuracy by addressing the challenge of signal artifacts, which can obscure critical information. A machine learning model was developed to classify ECG artifacts using a feature-based approach. Features were extracted from Time, Frequency, Time-frequency, and Decomposition domains, and Recursive Feature Elimination (RFE) was employed to narrow down to 45 key features relevant to artifact detection, such as motion artifacts, baseline wander, muscle tremors, and power line interference. The Light Gradient Boosting Machine (LightGBM) was chosen as the classifier, and model performance was tested internally on the KURIAS ECG database, containing 36,000 records, and validated externally on two databases: CinC2011 (12,000 records) and MIT-BIH NST (800 records). Results demonstrated high accuracy, with the model achieving an F1 score of 89.88% and an MCC of 88.35% in internal testing, and F1 scores of 94.35% and MCCs around 92.70% in external testing. These findings indicate that the model can effectively classify artifacts across various conditions, enhancing ECG reliability and diagnostic accuracy. Future efforts will explore real-time analysis and broader clinical applications, aiming to integrate this solution into routine clinical workflows.
Speaker:
Jose Moon, MS
Korea University
Authors:
Hyung Joon Joo, MD, PhD; JONGHO KIM, Ph.D;
Poster Number: P07
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Clinical informatics organizational models, Data Science
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
This study focuses on enhancing ECG diagnostic accuracy by addressing the challenge of signal artifacts, which can obscure critical information. A machine learning model was developed to classify ECG artifacts using a feature-based approach. Features were extracted from Time, Frequency, Time-frequency, and Decomposition domains, and Recursive Feature Elimination (RFE) was employed to narrow down to 45 key features relevant to artifact detection, such as motion artifacts, baseline wander, muscle tremors, and power line interference. The Light Gradient Boosting Machine (LightGBM) was chosen as the classifier, and model performance was tested internally on the KURIAS ECG database, containing 36,000 records, and validated externally on two databases: CinC2011 (12,000 records) and MIT-BIH NST (800 records). Results demonstrated high accuracy, with the model achieving an F1 score of 89.88% and an MCC of 88.35% in internal testing, and F1 scores of 94.35% and MCCs around 92.70% in external testing. These findings indicate that the model can effectively classify artifacts across various conditions, enhancing ECG reliability and diagnostic accuracy. Future efforts will explore real-time analysis and broader clinical applications, aiming to integrate this solution into routine clinical workflows.
Speaker:
Jose Moon, MS
Korea University
Authors:
Hyung Joon Joo, MD, PhD; JONGHO KIM, Ph.D;
Jose
Moon,
MS - Korea University
AI Encounter Summarization: Early Insights from Clinician-Users
Poster Number: P08
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, EHR Implementation and Optimization, Workflow Efficiency
Primary Track: AI and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
This session explores early clinician feedback on an Epic AI encounter-summarization tool. Based on survey data, clinicians desire a summarization approach that prioritizes high-value information without compromising patient safety. Key concerns included ensuring an efficient workflow and accuracy in AI outputs. These findings underscore the importance of iterative tool development to balance conciseness and completeness to enhance clinical efficiency and care quality. Attendees will gain insights into optimizing AI summarization tools for diverse clinical settings.
Speaker:
Jared Silberlust, MD MPH
NYU Langone Health
Poster Number: P08
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, EHR Implementation and Optimization, Workflow Efficiency
Primary Track: AI and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
This session explores early clinician feedback on an Epic AI encounter-summarization tool. Based on survey data, clinicians desire a summarization approach that prioritizes high-value information without compromising patient safety. Key concerns included ensuring an efficient workflow and accuracy in AI outputs. These findings underscore the importance of iterative tool development to balance conciseness and completeness to enhance clinical efficiency and care quality. Attendees will gain insights into optimizing AI summarization tools for diverse clinical settings.
Speaker:
Jared Silberlust, MD MPH
NYU Langone Health
Jared
Silberlust,
MD MPH - NYU Langone Health
Assessing the Reliability of Large Language Models in Assisting with Qualitative Analysis for Healthcare Informatics Research
Poster Number: P09
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Disruptive and Innovative Technologies, Precision Health and Genomics
Primary Track: AI and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
This study explored using large language models (LLMs) to expedite thematic analysis in healthcare informatics research. Four LLMs (Gemini-1.5-pro, GPT-4o, Claude-3.5-sonnet, and llama-3.1-8b-instruct) analyzed clinician interviews evaluating pharmacogenomics module using a standardized prompt. Gemini and Claude showed 80% consistency with human-generated themes, also revealing novel insights. While significantly faster, LLMs lacked the contextual interpretation depth of human coders. LLMs show promise but currently serve best as complementary tools for healthcare informatics analysis.
Speaker:
Je-Won Hong, PharmD
University of Florida
Author:
Khoa Nguyen, Pharm.D - University of Florida;
Poster Number: P09
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Disruptive and Innovative Technologies, Precision Health and Genomics
Primary Track: AI and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
This study explored using large language models (LLMs) to expedite thematic analysis in healthcare informatics research. Four LLMs (Gemini-1.5-pro, GPT-4o, Claude-3.5-sonnet, and llama-3.1-8b-instruct) analyzed clinician interviews evaluating pharmacogenomics module using a standardized prompt. Gemini and Claude showed 80% consistency with human-generated themes, also revealing novel insights. While significantly faster, LLMs lacked the contextual interpretation depth of human coders. LLMs show promise but currently serve best as complementary tools for healthcare informatics analysis.
Speaker:
Je-Won Hong, PharmD
University of Florida
Author:
Khoa Nguyen, Pharm.D - University of Florida;
Je-Won
Hong,
PharmD - University of Florida
Analyzing Clinician Responses to Electronic Health Records Alerts with Large Language Models
Poster Number: P10
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Adaptive Clinical Decision Support, Artificial Intelligence/Machine Learning, EHR Implementation and Optimization
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Electronic health record alerts offer summarized computerized knowledge to aid clinical decision-making. However, inappropriate alerts contribute to alert fatigue. Large language models offer the ability to relatively accurately categorize and determine the appropriateness of free-text clinician rationale for bypassing alerts that warn of duplicate chest X-ray orders. This analysis can be used to improve alert triggers to reduce inappropriate alerts, or to inform and influence clinical practice to minimize the bypass of appropriate alerts.
Speaker:
Michael Phillipi, BA
UCI
Authors:
Michael Phillipi, BA - UCI; Shawn Sun, MD - University of California, Irvine; Jeanette Meraz, BS - University of California, Irvine; Gavin Shu, BS - University of California, Irvine; Hannah Cho, BS - University of California, Irvine; Jadyn Lontoc, BS - University of California, Irvine; Justin Glavis-Bloom, MD - University of California, Irvine; Roozbeh Houshyar, MD - University of California, Irvine;
Poster Number: P10
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Adaptive Clinical Decision Support, Artificial Intelligence/Machine Learning, EHR Implementation and Optimization
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Electronic health record alerts offer summarized computerized knowledge to aid clinical decision-making. However, inappropriate alerts contribute to alert fatigue. Large language models offer the ability to relatively accurately categorize and determine the appropriateness of free-text clinician rationale for bypassing alerts that warn of duplicate chest X-ray orders. This analysis can be used to improve alert triggers to reduce inappropriate alerts, or to inform and influence clinical practice to minimize the bypass of appropriate alerts.
Speaker:
Michael Phillipi, BA
UCI
Authors:
Michael Phillipi, BA - UCI; Shawn Sun, MD - University of California, Irvine; Jeanette Meraz, BS - University of California, Irvine; Gavin Shu, BS - University of California, Irvine; Hannah Cho, BS - University of California, Irvine; Jadyn Lontoc, BS - University of California, Irvine; Justin Glavis-Bloom, MD - University of California, Irvine; Roozbeh Houshyar, MD - University of California, Irvine;
Michael
Phillipi,
BA - UCI
Development of a Deep Learning Model for Intussusception Using Point-of-Care Ultrasound
Poster Number: P11
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Disruptive and Innovative Technologies, Adaptive Clinical Decision Support
Primary Track: AI and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
Introduction: Intussusception is a pediatric emergency frequently with delays. We developed deep learning models for detection on POCUS images.
Methods: Images trained using various machine learning and classification approaches.
Results: 1,582 intussusception and 1,965 normal images were converted. Fine-tuning models performed better than machine, ensemble and transfer learning. Threshold-based classification resulted in greatest predictive performance.
Conclusions: Feasibility of developing deep learning model for intussusception using a smaller dataset of POCUS images compared to previous studies.
Speaker:
Brian Lefchak, MD, MPH
Hennepin Healthcare
Authors:
Anand Thyagachadnran, PhD - Department of Computer Science & Engineering, Indian Institute of Technology Madras; Brian Lefchak, MD, MPH - Hennepin Healthcare; Hema Murthy, PhD - Department of Computer Science & Engineering, Indian Institute of Technology Madras; Kelly Bergmann, DO, MS - Department of Pediatric Emergency Medicine, Children’s Minnesota; Manu Madhok, MD, MPH - Department of Pediatric Emergency Medicine, Children’s Minnesota;
Poster Number: P11
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Disruptive and Innovative Technologies, Adaptive Clinical Decision Support
Primary Track: AI and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
Introduction: Intussusception is a pediatric emergency frequently with delays. We developed deep learning models for detection on POCUS images.
Methods: Images trained using various machine learning and classification approaches.
Results: 1,582 intussusception and 1,965 normal images were converted. Fine-tuning models performed better than machine, ensemble and transfer learning. Threshold-based classification resulted in greatest predictive performance.
Conclusions: Feasibility of developing deep learning model for intussusception using a smaller dataset of POCUS images compared to previous studies.
Speaker:
Brian Lefchak, MD, MPH
Hennepin Healthcare
Authors:
Anand Thyagachadnran, PhD - Department of Computer Science & Engineering, Indian Institute of Technology Madras; Brian Lefchak, MD, MPH - Hennepin Healthcare; Hema Murthy, PhD - Department of Computer Science & Engineering, Indian Institute of Technology Madras; Kelly Bergmann, DO, MS - Department of Pediatric Emergency Medicine, Children’s Minnesota; Manu Madhok, MD, MPH - Department of Pediatric Emergency Medicine, Children’s Minnesota;
Brian
Lefchak,
MD, MPH - Hennepin Healthcare
Predicting In-Hospital Mortality following Blunt Splenic Injury
Poster Number: P12
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Data Science, Big Data
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Research with a North American Trauma Database to examine risk factors for in-hospital mortality in blunt splenic injury patients demonstrated that splenic injury severity and its management strategy were not the prime determinants of mortality. As compared to XGBoost and 5-layer neural networks, Generalized Additive Models (GAMs) were interpretable with comparable performance.
Speaker:
Randeep Jawa, MD
Stony Brook Medicine
Authors:
Randeep Jawa, MD - Stony Brook Medicine; James Vosswinkel, MD - Stony Brook University; Janos Hajagos, Ph.D. - Stony Brook University;
Poster Number: P12
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Data Science, Big Data
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Research with a North American Trauma Database to examine risk factors for in-hospital mortality in blunt splenic injury patients demonstrated that splenic injury severity and its management strategy were not the prime determinants of mortality. As compared to XGBoost and 5-layer neural networks, Generalized Additive Models (GAMs) were interpretable with comparable performance.
Speaker:
Randeep Jawa, MD
Stony Brook Medicine
Authors:
Randeep Jawa, MD - Stony Brook Medicine; James Vosswinkel, MD - Stony Brook University; Janos Hajagos, Ph.D. - Stony Brook University;
Randeep
Jawa,
MD - Stony Brook Medicine
Explainable LLM Classification (ELC) for Discharge Disposition Prediction
Poster Number: P13
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Adaptive Clinical Decision Support, Data Science, Coordination Across the Continuum of Care, Risk Measurement, Big Data
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Discharge disposition planning to Skilled Nursing Facilities (SNFs) for hospitalized patients is an important process. A novel method called Explainable LLM Classification (ELC) was developed, where GPT4-generated summaries of unstructured history and physical notes are used as both input data to discriminative LLMs and as an interpretable output for end-users. ELC enhances the predictive performance of discriminative LLMs above truncated text alone, and the best-performing fine-tuned LLM, NYUTron, vastly outperformed human predictions
Speaker:
Ryan Crowley, BS, Mphil
NYU Grossman School of Medicine
Authors:
Ryan Crowley, BS, Mphil - NYU Grossman School of Medicine; Chloe Pariente, MBAN - NYU Langone Health; Kevin Eaton, MD - NYU Langone Health; Yindalon Aphinyanaphongs, MD - NYU Langone Health; William Small, MD, MBA - NYU Langone Health;
Poster Number: P13
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Adaptive Clinical Decision Support, Data Science, Coordination Across the Continuum of Care, Risk Measurement, Big Data
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Discharge disposition planning to Skilled Nursing Facilities (SNFs) for hospitalized patients is an important process. A novel method called Explainable LLM Classification (ELC) was developed, where GPT4-generated summaries of unstructured history and physical notes are used as both input data to discriminative LLMs and as an interpretable output for end-users. ELC enhances the predictive performance of discriminative LLMs above truncated text alone, and the best-performing fine-tuned LLM, NYUTron, vastly outperformed human predictions
Speaker:
Ryan Crowley, BS, Mphil
NYU Grossman School of Medicine
Authors:
Ryan Crowley, BS, Mphil - NYU Grossman School of Medicine; Chloe Pariente, MBAN - NYU Langone Health; Kevin Eaton, MD - NYU Langone Health; Yindalon Aphinyanaphongs, MD - NYU Langone Health; William Small, MD, MBA - NYU Langone Health;
Ryan
Crowley,
BS, Mphil - NYU Grossman School of Medicine
A Deep Learning Approach to Classifying Catatonia Diagnosis from Clinical Text
Poster Number: P14
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Adaptive Clinical Decision Support, Risk Measurement
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Catatonia is a treatable condition that is underrecognized in the hospital setting. No HIT tools are available to help clinicians in considering targeted screening for the diagnosis. We have built a machine learning model that accurately classifies the diagnosis of catatonia from a subset of MIMIC-IV clinical text with high precision. Further work is needed in data collection, local training, and explainability analysis but initial results are encouraging for possible downstream CDS applications.
Speaker:
Jonathan Chastain, DO
University of Arkansas for Medical Sciences
Authors:
Jonathan Chastain, DO - University of Arkansas for Medical Sciences; Jonathan Bona, PhD - University of Arkansas for Medical Sciences (UAMS);
Poster Number: P14
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Adaptive Clinical Decision Support, Risk Measurement
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Catatonia is a treatable condition that is underrecognized in the hospital setting. No HIT tools are available to help clinicians in considering targeted screening for the diagnosis. We have built a machine learning model that accurately classifies the diagnosis of catatonia from a subset of MIMIC-IV clinical text with high precision. Further work is needed in data collection, local training, and explainability analysis but initial results are encouraging for possible downstream CDS applications.
Speaker:
Jonathan Chastain, DO
University of Arkansas for Medical Sciences
Authors:
Jonathan Chastain, DO - University of Arkansas for Medical Sciences; Jonathan Bona, PhD - University of Arkansas for Medical Sciences (UAMS);
Jonathan
Chastain,
DO - University of Arkansas for Medical Sciences
Dose-volume histograms guided deep dose predictions
Poster Number: P15
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Workflow Efficiency, Clinical Process Automation, Adaptive Clinical Decision Support, Digital Therapeutics
Working Group: Clinical Decision Support Working Group
Primary Track: AI and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
In radiotherapy treatment planning, we propose a deep learning framework that integrates target Dose-Volume Histograms (DVHs) to improve dose prediction accuracy and enable interactive adjustments within the treatment planning system.
Our model uses DVHs with Direct Affine Feature Transforms (DAFT) to adapt dose distributions based on dosimetrist-defined DVHs.
The DVH-guided model outperformed traditional approaches, facilitating a workflow where dosimetrists design target DVHs, streamlining plan customization for optimized, patient-specific treatments.
Speaker:
Paul Dubois, Master
TheraPanacea
Poster Number: P15
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Workflow Efficiency, Clinical Process Automation, Adaptive Clinical Decision Support, Digital Therapeutics
Working Group: Clinical Decision Support Working Group
Primary Track: AI and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
In radiotherapy treatment planning, we propose a deep learning framework that integrates target Dose-Volume Histograms (DVHs) to improve dose prediction accuracy and enable interactive adjustments within the treatment planning system.
Our model uses DVHs with Direct Affine Feature Transforms (DAFT) to adapt dose distributions based on dosimetrist-defined DVHs.
The DVH-guided model outperformed traditional approaches, facilitating a workflow where dosimetrists design target DVHs, streamlining plan customization for optimized, patient-specific treatments.
Speaker:
Paul Dubois, Master
TheraPanacea
Paul
Dubois,
Master - TheraPanacea
Post-Processing Algorithmic Bias Mitigation at NYC Health + Hospitals
Poster Number: P16
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Algorithmic bias and impacts on Health Equity, Educating on Self Service Analytics, Diversity, Equity and Inclusion, Population Health, HIT Safety/EHR Unintended Consequences, Adaptive Clinical Decision Support, Artificial Intelligence/Machine Learning
Primary Track: AI and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
Algorithmic bias in healthcare occurs when model performance varies by protected class, leading to biased treatment. We identified bias in an Epic asthma model at a large urban safety net system. Threshold adjustment and reject option classification post-processing methods both reduced bias meaningfully, with custom threshold adjustment in RStudio out-performing open-source python methods. Our findings show the promise of post-processing to lower the barrier-to-entry for mitigating bias in binary predictive models for other healthcare systems.
Speaker:
Silas Lee, MS
NYC Health and Hospitals
Authors:
Shaina Mackin, MPH - NYC Health + Hospitals; Silas Lee, MS - NYC Health and Hospitals; Abe Dickenson, BS - Epic; Vincent Major, PhD - NYU Grossman School of Medicine; Rumi Chunara, PhD - NYU Center for Health Data Science; Remle Newton-Dame, MPH - NYC Health + Hospitals;
Poster Number: P16
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Algorithmic bias and impacts on Health Equity, Educating on Self Service Analytics, Diversity, Equity and Inclusion, Population Health, HIT Safety/EHR Unintended Consequences, Adaptive Clinical Decision Support, Artificial Intelligence/Machine Learning
Primary Track: AI and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
Algorithmic bias in healthcare occurs when model performance varies by protected class, leading to biased treatment. We identified bias in an Epic asthma model at a large urban safety net system. Threshold adjustment and reject option classification post-processing methods both reduced bias meaningfully, with custom threshold adjustment in RStudio out-performing open-source python methods. Our findings show the promise of post-processing to lower the barrier-to-entry for mitigating bias in binary predictive models for other healthcare systems.
Speaker:
Silas Lee, MS
NYC Health and Hospitals
Authors:
Shaina Mackin, MPH - NYC Health + Hospitals; Silas Lee, MS - NYC Health and Hospitals; Abe Dickenson, BS - Epic; Vincent Major, PhD - NYU Grossman School of Medicine; Rumi Chunara, PhD - NYU Center for Health Data Science; Remle Newton-Dame, MPH - NYC Health + Hospitals;
Silas
Lee,
MS - NYC Health and Hospitals
The Association of Age and Sex on Revision Microdiscectomy and Subsequent Fusion Following Lumbar Microdiscectomy
Poster Number: P17
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Data Visualization, Big Data, Adaptive Clinical Decision Support, Data Science, Risk Measurement
Primary Track: AI and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
A retrospective analysis of insurance claims and electronic health records identified risk factors for revision lumbar microdiscectomy and subsequent fusion. The findings revealed a significantly higher risk of lumbar fusion in women aged 40-59 with at least one comorbidities. This suggests a potential for improved patient outcomes through targeted interventions and preventative strategies for this high-risk group.
Speaker:
Charlotte Drury-Gworek, MSN
Highmark Health
Authors:
Charlotte Drury-Gworek, MSN - Highmark Health; Shannon Richards, MSN - Highmark Health; Tyson S. Barrett, PhD - Highmark Health; Dallas E. Kramer, MD - Allegheny Health; Keith LeJeune, PhD - Highmark Health/Allegheny Health Network; Lara Massie, MD - Highmark Health;
Poster Number: P17
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Data Visualization, Big Data, Adaptive Clinical Decision Support, Data Science, Risk Measurement
Primary Track: AI and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
A retrospective analysis of insurance claims and electronic health records identified risk factors for revision lumbar microdiscectomy and subsequent fusion. The findings revealed a significantly higher risk of lumbar fusion in women aged 40-59 with at least one comorbidities. This suggests a potential for improved patient outcomes through targeted interventions and preventative strategies for this high-risk group.
Speaker:
Charlotte Drury-Gworek, MSN
Highmark Health
Authors:
Charlotte Drury-Gworek, MSN - Highmark Health; Shannon Richards, MSN - Highmark Health; Tyson S. Barrett, PhD - Highmark Health; Dallas E. Kramer, MD - Allegheny Health; Keith LeJeune, PhD - Highmark Health/Allegheny Health Network; Lara Massie, MD - Highmark Health;
Charlotte
Drury-Gworek,
MSN - Highmark Health
Validating a Proprietary No-Show Predictive Model in a Large Pediatric Primary Care Network
Poster Number: P18
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Algorithmic bias and impacts on Health Equity, Data Visualization
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
This study evaluates the performance of a proprietary naive Bayes No-Show prediction algorithm in a large pediatric primary care network. Despite widespread use of such proprietary models, validation remains limited. Findings indicate a poor calibration, especially within the 31-70% prediction range, and an area under the curve (AUC) of 0.68, suggesting limited efficacy for predicting No-Shows in the local context of this pediatric primary care network.
Speaker:
Eleanor Verhagen, MS
Boston Children's Hospital
Authors:
Emily Trudell Correa, MPH, MS - Boston Children's Hospital; Naveed Rabbani, MD - Boston Children's Hospital; Louis Vernacchio, MD, MSc - Boston Children's Hospital; Jonathan Hatoun, MD, MPH, MS - Boston Children's Hospital;
Poster Number: P18
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Algorithmic bias and impacts on Health Equity, Data Visualization
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
This study evaluates the performance of a proprietary naive Bayes No-Show prediction algorithm in a large pediatric primary care network. Despite widespread use of such proprietary models, validation remains limited. Findings indicate a poor calibration, especially within the 31-70% prediction range, and an area under the curve (AUC) of 0.68, suggesting limited efficacy for predicting No-Shows in the local context of this pediatric primary care network.
Speaker:
Eleanor Verhagen, MS
Boston Children's Hospital
Authors:
Emily Trudell Correa, MPH, MS - Boston Children's Hospital; Naveed Rabbani, MD - Boston Children's Hospital; Louis Vernacchio, MD, MSc - Boston Children's Hospital; Jonathan Hatoun, MD, MPH, MS - Boston Children's Hospital;
Eleanor
Verhagen,
MS - Boston Children's Hospital
Using a Word Embedding Approach to Detect Negative Provider Sentiment in Clinical Notes
Poster Number: P19
Click to View Presentation
Click to View Handout
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Bridging Analytics, Bedside Care, Clinical Documentation, and Education, Diversity, Equity and Inclusion
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Negative provider sentiment toward patients can negatively impact patient care, with some patients receiving lower quality care due to factors such as substance use disorder. Negative sentiment in clinical notes can both indicate negative sentiment toward patients from a given provider, and also propagate this negative sentiment, affecting other providers' decision making. We have developed an NLP model to quantify language containing negative sentiment in clinical notes, an important step in understanding the relationship of the language found in notes to clinical outcomes and mitigating its impact.
Speaker:
Patrick Wedgeworth, MD, MISM
University of Washington
Authors:
Priscilla Lui, PhD - University of Washington; Aishwarya Raj, Student - University of Washington;
Poster Number: P19
Click to View Presentation
Click to View Handout
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Bridging Analytics, Bedside Care, Clinical Documentation, and Education, Diversity, Equity and Inclusion
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Negative provider sentiment toward patients can negatively impact patient care, with some patients receiving lower quality care due to factors such as substance use disorder. Negative sentiment in clinical notes can both indicate negative sentiment toward patients from a given provider, and also propagate this negative sentiment, affecting other providers' decision making. We have developed an NLP model to quantify language containing negative sentiment in clinical notes, an important step in understanding the relationship of the language found in notes to clinical outcomes and mitigating its impact.
Speaker:
Patrick Wedgeworth, MD, MISM
University of Washington
Authors:
Priscilla Lui, PhD - University of Washington; Aishwarya Raj, Student - University of Washington;
Patrick
Wedgeworth,
MD, MISM - University of Washington
Preliminary Findings From A Comparative Study of GPT and Neuro-symbolic Models in Salt Content Information Delivery
Poster Number: P20
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Algorithmic bias and impacts on Health Equity, Driving Digital Equity, Social Determinants of Health
Primary Track: AI and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Introduction: African American patients (AA) with heart failure (HF) have poorer prognoses, exacerbated by limited access to customized self-care resources (Nayak, Hicks, and Morris 2020). Our previous work focused on developing dialogue agents (DAs) for personalized, accurate self-care guidance (Tayal et al. 2024). The second iteration of our DA, HFChat, uses a neuro-symbolic approach and USDA data to provide salt content information in foods, adjusting for cooking method, portion size, home-cooked vs. restaurant meals, and added ingredients. We compare this HFChat iteration with a custom GPT-based DA built on OpenAI’s infrastructure utilizing the same data inputs to assess the feasibility of an LLM-based solution.
Methods: Nineteen AA participants with HF were recruited. Each participant asked both ChatGPT and HFChat five food-related questions. Both systems requested clarifying information to personalize answers. Demographic, health literacy, and digital literacy data, along with user experience surveys, were also collected.
Results: The average age of the cohort was ~60 years, with 7/19 participants being women. HFChat was preferred by 10/19 participants; preferences varied by digital literacy (see graph).
Conclusion: Preliminary findings show that participants with higher digital literacy preferred HFChat, while those with lower digital literacy favored ChatGPT. Individuals with lower digital literacy may lack the skills to critically assess health information.(Yuen et al. 2024). This is especially relevant given ChatGPT’s potential for hallucinations (Siontis et al. 2024). Additionally, DAs built on external infrastructure like OpenAI cannot be fully controlled making consistency and reliability of information difficult to guarantee.
Speaker:
Devika Salunke, BPharm, MS, MSc
University of Illinois
Authors:
Devika Salunke, BPharm, MS, MSc - University of Illinois; Anuja Tayal, BTech - University of Illinois Chicago; Barbara Di Eugenio, PhD - University of Illinois Chicago; Paula Allen-Meares, PhD - University of Illinois Chicago; Carolyn Dickens, PhD; Olga Garcia-Bedoya, MD - University of Illinois Chicago; Eulalia Abril, PhD - University of Illinois Chicago; Andrew Boyd, MD - University of Illinois at Chicago;
Poster Number: P20
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Algorithmic bias and impacts on Health Equity, Driving Digital Equity, Social Determinants of Health
Primary Track: AI and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Introduction: African American patients (AA) with heart failure (HF) have poorer prognoses, exacerbated by limited access to customized self-care resources (Nayak, Hicks, and Morris 2020). Our previous work focused on developing dialogue agents (DAs) for personalized, accurate self-care guidance (Tayal et al. 2024). The second iteration of our DA, HFChat, uses a neuro-symbolic approach and USDA data to provide salt content information in foods, adjusting for cooking method, portion size, home-cooked vs. restaurant meals, and added ingredients. We compare this HFChat iteration with a custom GPT-based DA built on OpenAI’s infrastructure utilizing the same data inputs to assess the feasibility of an LLM-based solution.
Methods: Nineteen AA participants with HF were recruited. Each participant asked both ChatGPT and HFChat five food-related questions. Both systems requested clarifying information to personalize answers. Demographic, health literacy, and digital literacy data, along with user experience surveys, were also collected.
Results: The average age of the cohort was ~60 years, with 7/19 participants being women. HFChat was preferred by 10/19 participants; preferences varied by digital literacy (see graph).
Conclusion: Preliminary findings show that participants with higher digital literacy preferred HFChat, while those with lower digital literacy favored ChatGPT. Individuals with lower digital literacy may lack the skills to critically assess health information.(Yuen et al. 2024). This is especially relevant given ChatGPT’s potential for hallucinations (Siontis et al. 2024). Additionally, DAs built on external infrastructure like OpenAI cannot be fully controlled making consistency and reliability of information difficult to guarantee.
Speaker:
Devika Salunke, BPharm, MS, MSc
University of Illinois
Authors:
Devika Salunke, BPharm, MS, MSc - University of Illinois; Anuja Tayal, BTech - University of Illinois Chicago; Barbara Di Eugenio, PhD - University of Illinois Chicago; Paula Allen-Meares, PhD - University of Illinois Chicago; Carolyn Dickens, PhD; Olga Garcia-Bedoya, MD - University of Illinois Chicago; Eulalia Abril, PhD - University of Illinois Chicago; Andrew Boyd, MD - University of Illinois at Chicago;
Devika
Salunke,
BPharm, MS, MSc - University of Illinois
Leveraging Generative AI in Clinical Decision Support: A Case Report on Using OpenEvidence for Diagnosing Treatment-Induced Neuropathy in Diabetes
Poster Number: P21
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Workflow Efficiency, SAFER guidelines, Patient Safety
Primary Track: AI and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
This case report demonstrates the application of OpenEvidence, a generative AI tool, in diagnosing Treatment-Induced Neuropathy of Diabetes (TIND). A 74-year-old male presented with progressive neuropathy following rapid correction of serum glucose. Initial extensive investigation did not identify a cause. Using OpenEvidence, clinician accessed synthesized evidence and prioritized TIND as a differential diagnosis enabling evidence-based management. This case illustrates the role of AI in improving clinical decision-making and patient outcomes in complex primary care cases.
Speaker:
Toyosi Akinbami, MD
Oregon Health and Science University
Poster Number: P21
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Workflow Efficiency, SAFER guidelines, Patient Safety
Primary Track: AI and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
This case report demonstrates the application of OpenEvidence, a generative AI tool, in diagnosing Treatment-Induced Neuropathy of Diabetes (TIND). A 74-year-old male presented with progressive neuropathy following rapid correction of serum glucose. Initial extensive investigation did not identify a cause. Using OpenEvidence, clinician accessed synthesized evidence and prioritized TIND as a differential diagnosis enabling evidence-based management. This case illustrates the role of AI in improving clinical decision-making and patient outcomes in complex primary care cases.
Speaker:
Toyosi Akinbami, MD
Oregon Health and Science University
Toyosi
Akinbami,
MD - Oregon Health and Science University
Leveraging Large Language Models to Predict Mental Status in Patients with Dementia Using Electronic Health Records
Poster Number: P22
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Big Data, Adaptive Clinical Decision Support
Primary Track: AI and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
Elderly individuals living with dementia are particularly vulnerable to mental health challenges. This study employs Large Language Models (LLMs) to analyze Electronic Health Records (EHRs) and predict patients' mental status. We evaluated the performance of four LLMs combined with three prompting strategies in assessing the mental status of individuals with dementia.
Speaker:
Jie Yang, PhD, FAMIA
Brigham and Women's Hospital/Harvard Medical School
Authors:
Jiageng Wu, MS - Brigham and Women’s Hospital and Harvard Medical School; Richard Wyss, PhD - Brigham and Women’s Hospital and Harvard Medical School; Kueiyu Joshua Lin, ScD - Brigham and Women’s Hospital and Harvard Medical School; Jie Yang, PhD - Brigham and Women’s Hospital and Harvard Medical School;
Poster Number: P22
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Big Data, Adaptive Clinical Decision Support
Primary Track: AI and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
Elderly individuals living with dementia are particularly vulnerable to mental health challenges. This study employs Large Language Models (LLMs) to analyze Electronic Health Records (EHRs) and predict patients' mental status. We evaluated the performance of four LLMs combined with three prompting strategies in assessing the mental status of individuals with dementia.
Speaker:
Jie Yang, PhD, FAMIA
Brigham and Women's Hospital/Harvard Medical School
Authors:
Jiageng Wu, MS - Brigham and Women’s Hospital and Harvard Medical School; Richard Wyss, PhD - Brigham and Women’s Hospital and Harvard Medical School; Kueiyu Joshua Lin, ScD - Brigham and Women’s Hospital and Harvard Medical School; Jie Yang, PhD - Brigham and Women’s Hospital and Harvard Medical School;
Jie
Yang,
PhD, FAMIA - Brigham and Women's Hospital/Harvard Medical School
Bridging AI and Clinical Guidelines: An integrated clinical and AI stratification framework for Diabetes Management
Poster Number: P23
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Care Delivery Models, Population Health
Primary Track: AI and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
A significant gap exists between the predictive capabilities of AI-driven diagnostics and the practical application of clinical guidelines in chronic disease management. Current systems often operate in isolation, failing to integrate AI insights with guideline-based risk stratification to provide actionable, patient-centered care. The Integrated Clinical-AI Stratification Framework by bridges this divide by combining structured and unstructured EHR data with clinical guidelines. Patients are dynamically assigned to clinically sound risk groups, validated by healthcare professionals, with tailored interventions aligned with international standards. From early screening to complication prevention, the integrated clinical and AI stratification enables proactive, precision-targeted care. Ongoing validation efforts with national health data in Saudi Arabia demonstrated its potential to transform diabetes management.
Speaker:
Mohammad Ghosheh, M.D
iO Health
Poster Number: P23
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Care Delivery Models, Population Health
Primary Track: AI and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
A significant gap exists between the predictive capabilities of AI-driven diagnostics and the practical application of clinical guidelines in chronic disease management. Current systems often operate in isolation, failing to integrate AI insights with guideline-based risk stratification to provide actionable, patient-centered care. The Integrated Clinical-AI Stratification Framework by bridges this divide by combining structured and unstructured EHR data with clinical guidelines. Patients are dynamically assigned to clinically sound risk groups, validated by healthcare professionals, with tailored interventions aligned with international standards. From early screening to complication prevention, the integrated clinical and AI stratification enables proactive, precision-targeted care. Ongoing validation efforts with national health data in Saudi Arabia demonstrated its potential to transform diabetes management.
Speaker:
Mohammad Ghosheh, M.D
iO Health
Mohammad
Ghosheh,
M.D - iO Health
Designing Clinician Personas to Enhance Technology Adoption
Poster Number: P24
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Ambient documentation, Clinician Burnout, Documentation Burden
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Clinician documentation burden continues to challenge healthcare systems, contributing to burnout and inefficiencies. Despite the availability of various documentation tools—such as note templates, speech-to-text dictation, scribes, and ambient scribes—their adoption often falters due to a lack of customization for diverse clinician needs. This study introduces a persona-based approach to optimize technology implementation for reducing documentation burden. Personas, fictional characters representing key clinician profiles, were developed through user interviews, and input from frontline clinicians. The resulting four personas—Cautious Carl, Traditional Tina, Efficient Edward, and Innovative Indira—illustrate the spectrum of clinician preferences based on technology comfort, documentation habits, and specialties. EHR coaches (N=21) validated the utility of these personas, with 100% agreeing that personas facilitate the development of tailored tools and training. Coaches also provided recommendations for tools matched to each persona, such as human scribes for Cautious Carl and ambient AI for Innovative Indira. These findings underscore the importance of user-centered design in healthcare technology, offering a scalable, needs-based strategy to enhance clinician satisfaction and efficiency. Future work includes validating this approach in broader healthcare settings to further refine its applicability and impact.
Speaker:
Charumathi Raghu Subramanian, MD
UCSF
Author:
Matthew Sakumoto, MD - Sutter Health;
Poster Number: P24
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Ambient documentation, Clinician Burnout, Documentation Burden
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Clinician documentation burden continues to challenge healthcare systems, contributing to burnout and inefficiencies. Despite the availability of various documentation tools—such as note templates, speech-to-text dictation, scribes, and ambient scribes—their adoption often falters due to a lack of customization for diverse clinician needs. This study introduces a persona-based approach to optimize technology implementation for reducing documentation burden. Personas, fictional characters representing key clinician profiles, were developed through user interviews, and input from frontline clinicians. The resulting four personas—Cautious Carl, Traditional Tina, Efficient Edward, and Innovative Indira—illustrate the spectrum of clinician preferences based on technology comfort, documentation habits, and specialties. EHR coaches (N=21) validated the utility of these personas, with 100% agreeing that personas facilitate the development of tailored tools and training. Coaches also provided recommendations for tools matched to each persona, such as human scribes for Cautious Carl and ambient AI for Innovative Indira. These findings underscore the importance of user-centered design in healthcare technology, offering a scalable, needs-based strategy to enhance clinician satisfaction and efficiency. Future work includes validating this approach in broader healthcare settings to further refine its applicability and impact.
Speaker:
Charumathi Raghu Subramanian, MD
UCSF
Author:
Matthew Sakumoto, MD - Sutter Health;
Charumathi
Raghu Subramanian,
MD - UCSF
Ambient AI Scribes: Surveying and Quantifying Education and Care Quality Concerns
Poster Number: P25
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Bridging Analytics, Bedside Care, Clinical Documentation, and Education, Ambient documentation, Artificial Intelligence/Machine Learning
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
Our study examines concerns about AI ambient documentation tools among medical students, residents, fellows, and attending doctors. By conducting a cross-sectional survey at UCI Health, the study discovered significant gender differences related to AI’s impact on medical training milestones, privacy, and documentation accuracy. Our work indicates the need for further investigation to explore AI tools and to integrate these insights into both AI-ambient documentation tools and AI training curriculum for medical trainees.
Speaker:
Yawen Guo, MISM
University of California - Irvine
Authors:
Di Hu, Master of Science in Information Systems - University of California - Irvine; Yawen Guo, MISM - University of California - Irvine; Shirin Salehi, BS - University of California, Irvine, School of Medicine; Kai Zheng, PhD - University of California, Irvine; Emilie Chow, MD - University of California, Irvine;
Poster Number: P25
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Bridging Analytics, Bedside Care, Clinical Documentation, and Education, Ambient documentation, Artificial Intelligence/Machine Learning
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
Our study examines concerns about AI ambient documentation tools among medical students, residents, fellows, and attending doctors. By conducting a cross-sectional survey at UCI Health, the study discovered significant gender differences related to AI’s impact on medical training milestones, privacy, and documentation accuracy. Our work indicates the need for further investigation to explore AI tools and to integrate these insights into both AI-ambient documentation tools and AI training curriculum for medical trainees.
Speaker:
Yawen Guo, MISM
University of California - Irvine
Authors:
Di Hu, Master of Science in Information Systems - University of California - Irvine; Yawen Guo, MISM - University of California - Irvine; Shirin Salehi, BS - University of California, Irvine, School of Medicine; Kai Zheng, PhD - University of California, Irvine; Emilie Chow, MD - University of California, Irvine;
Yawen
Guo,
MISM - University of California - Irvine
Improving Provider Documentation Using Pediatric AutoDx
Poster Number: P26
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Workflow Efficiency, EHR Implementation and Optimization, Change Management
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Provider documentation is essential to hospital operations and clinical communication. Coding queries account for a non-insignificant amount of provider time and effort, increasing provider frustration and dissatisfaction with practice. We aimed to decrease applicable coding queries by 50% and improve ease of practice for 25% of pediatric inpatient providers in Comer Children’s Hospital by June 30, 2024. We adapted the existing AutoDx tool used at our institution for adults to meet the unique needs of the pediatric population. This involved creating pediatric-specific rules, optimizing lookback windows, and accounting for documentation variations. The system was then deployed to inpatient pediatric primary services. The impact of the intervention was assessed by surveying providers before and after implementation, measuring coding queries before and after implementation, and conducting PDSA cycles to improve usage rates of the tool. Pediatric AutoDx implementation resulted in a 58% decrease in coding queries for the relevant conditions. The most significant gains were in rules evaluating laboratory results, such as hemoglobin and electrolyte values. Nearly half of providers agreed that AutoDx improved ease of practice. AutoDx usage steadily increased and then leveled off during the intervention period, suggesting consistent adoption and efficacy of multiple PDSA cycles to improve usage.
Speaker:
Kevin Smith, MD
University of Chicago Medicine
Authors:
Riley Boland, MD - University of Chicago; Matthew Cerasale, MD, MPH - University of Chicago; Cheng-Kai Kao, MD - University of Chicago;
Poster Number: P26
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Workflow Efficiency, EHR Implementation and Optimization, Change Management
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Provider documentation is essential to hospital operations and clinical communication. Coding queries account for a non-insignificant amount of provider time and effort, increasing provider frustration and dissatisfaction with practice. We aimed to decrease applicable coding queries by 50% and improve ease of practice for 25% of pediatric inpatient providers in Comer Children’s Hospital by June 30, 2024. We adapted the existing AutoDx tool used at our institution for adults to meet the unique needs of the pediatric population. This involved creating pediatric-specific rules, optimizing lookback windows, and accounting for documentation variations. The system was then deployed to inpatient pediatric primary services. The impact of the intervention was assessed by surveying providers before and after implementation, measuring coding queries before and after implementation, and conducting PDSA cycles to improve usage rates of the tool. Pediatric AutoDx implementation resulted in a 58% decrease in coding queries for the relevant conditions. The most significant gains were in rules evaluating laboratory results, such as hemoglobin and electrolyte values. Nearly half of providers agreed that AutoDx improved ease of practice. AutoDx usage steadily increased and then leveled off during the intervention period, suggesting consistent adoption and efficacy of multiple PDSA cycles to improve usage.
Speaker:
Kevin Smith, MD
University of Chicago Medicine
Authors:
Riley Boland, MD - University of Chicago; Matthew Cerasale, MD, MPH - University of Chicago; Cheng-Kai Kao, MD - University of Chicago;
Kevin
Smith,
MD - University of Chicago Medicine
Communicating Genetic Test Results to Clinicians and Patients
Poster Number: P27
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: EHR Implementation and Optimization, Precision Health and Genomics, Bridging Analytics, Bedside Care, Clinical Documentation, and Education
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Communicating genetic test results to clinicians and patients via EHR presents challenges. Conveying the complexity of “negative” and “positive” test results for conditions with varied inheritance patterns and risk profiles requires different approaches in the absence of a standardized reporting system. We convened a multidisciplinary panel to construct genomic indicators for three Tier 1 conditions. We present our model based on a well-considered approach for clinicians and patients to better understand the significance of results.
Speaker:
Sophie Cameron, MBChB, MS
Mayo Clinic
Authors:
Pedro Caraballo, MD - Mayo Clinic; Joseph Sutton, MS (CIS) - Mayo Clinic; Jennifer Kemppainen, MS, CGC - Mayo Clinic; Sophie Cameron, MBChB, MS - Mayo Clinic;
Poster Number: P27
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: EHR Implementation and Optimization, Precision Health and Genomics, Bridging Analytics, Bedside Care, Clinical Documentation, and Education
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Communicating genetic test results to clinicians and patients via EHR presents challenges. Conveying the complexity of “negative” and “positive” test results for conditions with varied inheritance patterns and risk profiles requires different approaches in the absence of a standardized reporting system. We convened a multidisciplinary panel to construct genomic indicators for three Tier 1 conditions. We present our model based on a well-considered approach for clinicians and patients to better understand the significance of results.
Speaker:
Sophie Cameron, MBChB, MS
Mayo Clinic
Authors:
Pedro Caraballo, MD - Mayo Clinic; Joseph Sutton, MS (CIS) - Mayo Clinic; Jennifer Kemppainen, MS, CGC - Mayo Clinic; Sophie Cameron, MBChB, MS - Mayo Clinic;
Sophie
Cameron,
MBChB, MS - Mayo Clinic
Creation of a Novel, Multicenter Neonatal Clinical Decision Support Rounding Tool
Poster Number: P28
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: EHR Implementation and Optimization, Patient Safety, Adaptive Clinical Decision Support, Usability and Measuring User Experience, Workflow Efficiency, Quality Measures and eCQMs / Quality Improvement
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
There is a well-established body of research associating checklists with improved safety and clinical outcomes. In response to provider feedback, our team was able to develop an integrated EHR clinical decision support rounding checklist tool that supports quality improvement and patient safety across four Penn Medicine neonatal intensive care units. Our work represents the first multicenter application of this framework to a more generalizable neonatal care population.
Speaker:
Alex Ruan, MD
Children's Hospital of Philadelphia
Authors:
Osvaldo Mercado, MD - Children's Hospital of Philadelphia; Deborah Welsh, BSN, RNC-OB - University of Pennsylvania Health System; Leah Carr, MD - Children's Hospital of Philadelphia;
Poster Number: P28
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: EHR Implementation and Optimization, Patient Safety, Adaptive Clinical Decision Support, Usability and Measuring User Experience, Workflow Efficiency, Quality Measures and eCQMs / Quality Improvement
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
There is a well-established body of research associating checklists with improved safety and clinical outcomes. In response to provider feedback, our team was able to develop an integrated EHR clinical decision support rounding checklist tool that supports quality improvement and patient safety across four Penn Medicine neonatal intensive care units. Our work represents the first multicenter application of this framework to a more generalizable neonatal care population.
Speaker:
Alex Ruan, MD
Children's Hospital of Philadelphia
Authors:
Osvaldo Mercado, MD - Children's Hospital of Philadelphia; Deborah Welsh, BSN, RNC-OB - University of Pennsylvania Health System; Leah Carr, MD - Children's Hospital of Philadelphia;
Alex
Ruan,
MD - Children's Hospital of Philadelphia
A Patient-Driven versus Staff-Driven Registration Process for Noona in the Oncology Setting
Poster Number: P29
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Documentation Burden, Clinician Burnout, Consumer and Patient Engagement, Innovation in Digital Care
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Noona is a patient facing app that is used by our organization to connect patients to their electronic health record and communicate with their care team. Registration is done by front-line staff but with competing priorities in the clinic setting, registration rates have remained low regardless of the organizational campaign efforts. Creating an alternative solution for patients to register became a required necessity.
Speaker:
Barbara Kitzan, Nurse
CancerCare Manitoba
Authors:
Kathleen Decker, PhD - CancerCare Manitoba; Marshall Pitz, MD. MPH - CancerCare Manitoba; Pascal Lambert, MSc - CancerCare Manitoba;
Poster Number: P29
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Documentation Burden, Clinician Burnout, Consumer and Patient Engagement, Innovation in Digital Care
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Noona is a patient facing app that is used by our organization to connect patients to their electronic health record and communicate with their care team. Registration is done by front-line staff but with competing priorities in the clinic setting, registration rates have remained low regardless of the organizational campaign efforts. Creating an alternative solution for patients to register became a required necessity.
Speaker:
Barbara Kitzan, Nurse
CancerCare Manitoba
Authors:
Kathleen Decker, PhD - CancerCare Manitoba; Marshall Pitz, MD. MPH - CancerCare Manitoba; Pascal Lambert, MSc - CancerCare Manitoba;
Barbara
Kitzan,
Nurse - CancerCare Manitoba
Multi-Disciplinary Workflow for Diabetic Retinopathy Screenings and Referrals: A Simplified Approach
Poster Number: P30
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Usability and Measuring User Experience, Workflow Efficiency, EHR Implementation and Optimization
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Informatics tools can be used to enhance diabetic retinopathy (DR) screenings in primary care settings, as we implemented a simplified screening interpretation form and clinical decision support in the electronic health record to improve provider efficiency and patient follow-up with ophthalmology. We provide preliminary data of our screening results, relaying insights into implementation and challenges in workflow integration, ultimately setting a foundation for improved screening accessibility and care coordination in primary care.
Speaker:
Kiran Malhotra, M.D.
NYU Langone Health
Authors:
Samyuktha Guttha, M.D. - NYU Langone Health; Kristin Hanselmann, B.S. - Epic; Lauren Golden, M.D. - NYU Langone Health; Elisabeth Cohen, M.D. - NYU Langone Health; Kathryn Colby, MD, PhD - NYU Langone Health; Adam Szerencsy, DO - NYU Langone Health;
Poster Number: P30
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Usability and Measuring User Experience, Workflow Efficiency, EHR Implementation and Optimization
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Informatics tools can be used to enhance diabetic retinopathy (DR) screenings in primary care settings, as we implemented a simplified screening interpretation form and clinical decision support in the electronic health record to improve provider efficiency and patient follow-up with ophthalmology. We provide preliminary data of our screening results, relaying insights into implementation and challenges in workflow integration, ultimately setting a foundation for improved screening accessibility and care coordination in primary care.
Speaker:
Kiran Malhotra, M.D.
NYU Langone Health
Authors:
Samyuktha Guttha, M.D. - NYU Langone Health; Kristin Hanselmann, B.S. - Epic; Lauren Golden, M.D. - NYU Langone Health; Elisabeth Cohen, M.D. - NYU Langone Health; Kathryn Colby, MD, PhD - NYU Langone Health; Adam Szerencsy, DO - NYU Langone Health;
Kiran
Malhotra,
M.D. - NYU Langone Health
Streamlining Drug Administration: The Role of Real-Time Data Integration
Poster Number: P31
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Workflow Efficiency, Adaptive Clinical Decision Support, Data Visualization
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
The integration of real-time data retrieval for laboratory results and vital signs into the In-Patient Medication Order Entry (IPMOE) system is designed to enhance medication safety and workflow efficiency in Hong Kong's public hospitals. By streamlining access to critical patient information, nurses can make informed medication decisions without navigating multiple systems. This initiative is expected to reduce application-switching time by 500 hours daily, resulting in significant annual savings and improved patient care through timely assessments during drug administration.
Speaker:
Sin Ting Leung, Bachelor of Nursing
Hospital Authority, Hong Kong
Poster Number: P31
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Workflow Efficiency, Adaptive Clinical Decision Support, Data Visualization
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
The integration of real-time data retrieval for laboratory results and vital signs into the In-Patient Medication Order Entry (IPMOE) system is designed to enhance medication safety and workflow efficiency in Hong Kong's public hospitals. By streamlining access to critical patient information, nurses can make informed medication decisions without navigating multiple systems. This initiative is expected to reduce application-switching time by 500 hours daily, resulting in significant annual savings and improved patient care through timely assessments during drug administration.
Speaker:
Sin Ting Leung, Bachelor of Nursing
Hospital Authority, Hong Kong
Sin Ting
Leung,
Bachelor of Nursing - Hospital Authority, Hong Kong
How Effective are Sepsis-Related Best Practice Advisories?: A Sabercaremetrics Approach
Poster Number: P32
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Adaptive Clinical Decision Support, Bridging Analytics, Bedside Care, Clinical Documentation, and Education, Communication Strategies
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
This study evaluates the impact of sepsis best practice advisories (BPAs) on the actions of providers caring for patients on hospital wards and step-down units. Our results support the use of general EHR-based clinical activity data following BPA firing over the use of in-BPA action data to evaluate the effectiveness of BPAs.
Speaker:
Colleen Flanagan, MD
UCSF
Poster Number: P32
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Adaptive Clinical Decision Support, Bridging Analytics, Bedside Care, Clinical Documentation, and Education, Communication Strategies
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
This study evaluates the impact of sepsis best practice advisories (BPAs) on the actions of providers caring for patients on hospital wards and step-down units. Our results support the use of general EHR-based clinical activity data following BPA firing over the use of in-BPA action data to evaluate the effectiveness of BPAs.
Speaker:
Colleen Flanagan, MD
UCSF
Colleen
Flanagan,
MD - UCSF
Enhancing Pediatric Headache Management in Primary Care Through a Dynamic EHR-Integrated Clinical Decision Support Tool
Poster Number: P33
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Adaptive Clinical Decision Support, EHR Implementation and Optimization, Workflow Efficiency, Usability and Measuring User Experience, Documentation Burden, Quality Measures and eCQMs / Quality Improvement
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
In CHOP’s Primary Care Network, an EHR-integrated clinical decision support tool was developed to guide providers in managing pediatric headaches and minimizing unnecessary referrals. This tool builds off of a text macro to be more interactive, dynamic, and easily integrated into provider workflow. Following pilot testing, this project will discuss the CDS development and outcomes including referral patterns, patient outcomes and provider satisfaction.
Speaker:
Loukya Kanakamedala, DO
CHOP
Authors:
Loukya Kanakamedala, DO - CHOP; Alex Ruan, MD - Children's Hospital of Philadelphia; Greg Lawton, MD - Children's Hospital of Philadelphia;
Poster Number: P33
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Adaptive Clinical Decision Support, EHR Implementation and Optimization, Workflow Efficiency, Usability and Measuring User Experience, Documentation Burden, Quality Measures and eCQMs / Quality Improvement
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
In CHOP’s Primary Care Network, an EHR-integrated clinical decision support tool was developed to guide providers in managing pediatric headaches and minimizing unnecessary referrals. This tool builds off of a text macro to be more interactive, dynamic, and easily integrated into provider workflow. Following pilot testing, this project will discuss the CDS development and outcomes including referral patterns, patient outcomes and provider satisfaction.
Speaker:
Loukya Kanakamedala, DO
CHOP
Authors:
Loukya Kanakamedala, DO - CHOP; Alex Ruan, MD - Children's Hospital of Philadelphia; Greg Lawton, MD - Children's Hospital of Philadelphia;
Loukya
Kanakamedala,
DO - CHOP
Leveraging large language models for complex care planning in patients with multiple chronic conditions
Poster Number: P35
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Documentation Burden, Patient Safety, Clinician Burnout
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Care planning for patients with multiple chronic conditions (MCCs) is data-intensive, time-consuming, and requires experienced care teams. Due to these complexities, discrepancies in care plans during transitions can occur and result in medical errors, patient harm, and clinician burnout. Large Language Models (LLMs) can potentially alleviate this burden by generating person-centered care summaries. We conducted a comparison study between physician participants and a LLM to explore the ability of a LLM to generate accurate clinical summaries for recently discharged patients using the MIMIC-III database.
Six physicians from OHSU created synopses of 12–16 discharge summaries each. The LLM generated synopses for the same summaries. Both physicians and the LLM were prompted to generate summaries following the DIKW framework, which prompts to identify key data, contextualize information, integrate evidence-based guidelines, and incorporate social and behavioral needs. Five physicians then blindly rated all synopses based on four criteria: Accuracy, Synthesis, Succinctness, and Usefulness using a 5-point Likert scale.
Results showed physicians outperformed the LLM in areas of accuracy of knowledge synopses, synthesis of data, succinctness of information, knowledge, and wisdom, and the usefulness of data and information. Conversely, the LLM demonstrated higher performance in the accuracy, synthesis, and usefulness of wisdom, and succinctness of data.
Our results suggest the LLM has strengths in generating accurate, harmonized, and useful wisdom synopses. This suggests that LLMs could aid in producing person-centered care plans, helping to reduce the burden of complex care planning on clinicians.
Speaker:
Emma Young, MS
Oregon Health & Science University
Authors:
Jean Sabile, MD - Oregon Health & Science University; LeAnn Michaels, BS - Oregon Health & Science University; Nicole Weiskopf, PhD - Oregon Health & Science University; Steven Bedrick - Oregon Health & Science University; David Dorr, MD, MS, FACMI, FAMIA, FIAHSI - Oregon Health & Science University;
Poster Number: P35
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Documentation Burden, Patient Safety, Clinician Burnout
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Care planning for patients with multiple chronic conditions (MCCs) is data-intensive, time-consuming, and requires experienced care teams. Due to these complexities, discrepancies in care plans during transitions can occur and result in medical errors, patient harm, and clinician burnout. Large Language Models (LLMs) can potentially alleviate this burden by generating person-centered care summaries. We conducted a comparison study between physician participants and a LLM to explore the ability of a LLM to generate accurate clinical summaries for recently discharged patients using the MIMIC-III database.
Six physicians from OHSU created synopses of 12–16 discharge summaries each. The LLM generated synopses for the same summaries. Both physicians and the LLM were prompted to generate summaries following the DIKW framework, which prompts to identify key data, contextualize information, integrate evidence-based guidelines, and incorporate social and behavioral needs. Five physicians then blindly rated all synopses based on four criteria: Accuracy, Synthesis, Succinctness, and Usefulness using a 5-point Likert scale.
Results showed physicians outperformed the LLM in areas of accuracy of knowledge synopses, synthesis of data, succinctness of information, knowledge, and wisdom, and the usefulness of data and information. Conversely, the LLM demonstrated higher performance in the accuracy, synthesis, and usefulness of wisdom, and succinctness of data.
Our results suggest the LLM has strengths in generating accurate, harmonized, and useful wisdom synopses. This suggests that LLMs could aid in producing person-centered care plans, helping to reduce the burden of complex care planning on clinicians.
Speaker:
Emma Young, MS
Oregon Health & Science University
Authors:
Jean Sabile, MD - Oregon Health & Science University; LeAnn Michaels, BS - Oregon Health & Science University; Nicole Weiskopf, PhD - Oregon Health & Science University; Steven Bedrick - Oregon Health & Science University; David Dorr, MD, MS, FACMI, FAMIA, FIAHSI - Oregon Health & Science University;
Emma
Young,
MS - Oregon Health & Science University
Decision Support Tools Decrease Heart Failure Exacerbation Admissions
Poster Number: P36
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Bridging Analytics, Bedside Care, Clinical Documentation, and Education, Clinical Process Automation, Quality Measures and eCQMs / Quality Improvement
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
We deployed and trained emergency physicians to use calculators for the Emergency Heart Failure Mortality Risk Grade tool and the Diuretic Optimization Strategies Evaluation using a third-party package from Terumo ePrism™ in our electronic health record. It included partial automatic import of clinical data into the form for ease of use. Monthly baseline admission rates decreased from 91.5% (95%CI: 85.0-97.9%)(n=305) to 84.3% (p<0.001)(n=32).
Speaker:
Jeffrey Nielson, MD
Kettering Health and Ohio University
Authors:
Alex Mueller, MD - Kettering Health; Margaret Soulen, MD - Kettering Health; Jeffrey Nielson, MD, MS - Kettering Health; Josephine Randazzo, DO - Kettering Health; Roy Johnson, MD - Kettering Health;
Poster Number: P36
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Bridging Analytics, Bedside Care, Clinical Documentation, and Education, Clinical Process Automation, Quality Measures and eCQMs / Quality Improvement
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
We deployed and trained emergency physicians to use calculators for the Emergency Heart Failure Mortality Risk Grade tool and the Diuretic Optimization Strategies Evaluation using a third-party package from Terumo ePrism™ in our electronic health record. It included partial automatic import of clinical data into the form for ease of use. Monthly baseline admission rates decreased from 91.5% (95%CI: 85.0-97.9%)(n=305) to 84.3% (p<0.001)(n=32).
Speaker:
Jeffrey Nielson, MD
Kettering Health and Ohio University
Authors:
Alex Mueller, MD - Kettering Health; Margaret Soulen, MD - Kettering Health; Jeffrey Nielson, MD, MS - Kettering Health; Josephine Randazzo, DO - Kettering Health; Roy Johnson, MD - Kettering Health;
Jeffrey
Nielson,
MD - Kettering Health and Ohio University
A Provider EHR Efficiency Training to Reduce Burnout and Turnover
Poster Number: P37
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: EHR Implementation and Optimization, Clinician Burnout, Building Value for Informatics via Education and Training, Workflow Efficiency, Documentation Burden
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Developed in 2019, the Super Thrive program is a 3-day off-site EHR efficiency training for ambulatory providers. Our data indicates that Super Thrive is successful in meeting our providers’ EHR educational and efficiency needs. Additionally, we have noted a decrease in attendee turnover rates compared to organizational rates and an anticipated reduction in provider burnout levels. The calculated cost savings due to the Super Thrive program is significant for our organization.
Speaker:
Gillian Piro, MD
Legacy Health
Authors:
Samantha Fogel, BS in Criminal Justice - Legacy Health; Kelley Aurand - Legacy Health;
Poster Number: P37
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: EHR Implementation and Optimization, Clinician Burnout, Building Value for Informatics via Education and Training, Workflow Efficiency, Documentation Burden
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Developed in 2019, the Super Thrive program is a 3-day off-site EHR efficiency training for ambulatory providers. Our data indicates that Super Thrive is successful in meeting our providers’ EHR educational and efficiency needs. Additionally, we have noted a decrease in attendee turnover rates compared to organizational rates and an anticipated reduction in provider burnout levels. The calculated cost savings due to the Super Thrive program is significant for our organization.
Speaker:
Gillian Piro, MD
Legacy Health
Authors:
Samantha Fogel, BS in Criminal Justice - Legacy Health; Kelley Aurand - Legacy Health;
Gillian
Piro,
MD - Legacy Health
The AI-Generated Documentation in Healthcare: A Pilot Study on Physician Well-being and Productivity.
Poster Number: P38
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Workflow Efficiency, Artificial Intelligence/Machine Learning, Clinician Burnout
Working Group: AMIA Clinical Informatics Fellows (ACIF) Working Group
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
This poster examines the impact of AI-generated documentation on practitioners' efficiency and satisfaction. We surveyed practitioners to assess readiness for adoption and decided on licensing needs.
After 90 days of implementation, we conducted a pilot study to evaluate satisfaction levels; we also used signals to track efficiency changes before and after adoption, focusing on patient numbers, time on the system, and pajamas' time. We aim to offer insights into AI-EHR’s potential benefits in clinical settings.
Speaker:
Mohammad Mohammad, Dr
Waco Family Medicine
Author:
Kelly Nieves, MD - Waco Family Medicine;
Poster Number: P38
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Workflow Efficiency, Artificial Intelligence/Machine Learning, Clinician Burnout
Working Group: AMIA Clinical Informatics Fellows (ACIF) Working Group
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
This poster examines the impact of AI-generated documentation on practitioners' efficiency and satisfaction. We surveyed practitioners to assess readiness for adoption and decided on licensing needs.
After 90 days of implementation, we conducted a pilot study to evaluate satisfaction levels; we also used signals to track efficiency changes before and after adoption, focusing on patient numbers, time on the system, and pajamas' time. We aim to offer insights into AI-EHR’s potential benefits in clinical settings.
Speaker:
Mohammad Mohammad, Dr
Waco Family Medicine
Author:
Kelly Nieves, MD - Waco Family Medicine;
Mohammad
Mohammad,
Dr - Waco Family Medicine
Characterization of Abnormal Involuntary Movement Scale (AIMS) Utilization for the Screening of Tardive Dyskinesia and Risk Modeling in Veterans with Severe Mental Illness
Poster Number: P39
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Precision Health and Genomics, Data Science
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Objective: This study aims to enable clinicians to leverage patients' antipsychotic prescriptions, demographic, and screening records to predict the risk of developing abnormal involuntary movements (AIMS) using electronic health records (EHR).
Description: Tardive dyskinesia (TD) is a debilitating movement disorder caused by long-term antipsychotic use, severely affecting patients' quality of life. The Abnormal Involuntary Movement Scale (AIMS) is the standard tool for TD surveillance in antipsychotic-treated patients. Despite known risk factors such as antipsychotic burden, age, and comorbidities, personalized TD risk prediction remains a challenge.
Methods: The study analyzed EHR data from the VA Cooperative Studies Program #572, covering nearly 20 years and including 7089 veterans with schizophrenia or bipolar disorder on antipsychotics. A random forest model was trained on data from 3974 patients with complete records, using chlorpromazine equivalents, demographic data, and diagnosis information. The model's performance was validated using a 10-fold cross-validation and tested on a separate dataset.
Results: The model achieved a precision-recall AUC of 0.755 and a ROC AUC of 0.726, demonstrating moderate predictive performance.
Conclusion: This study highlights the potential of EHR-based predictive models to identify high-risk TD patients, aiding in early intervention and improved patient outcomes. Future work will integrate genetic data to enhanced prediction accuracy.
Speaker:
Conner Polet, MD MBA
NYU Langone Health
Authors:
Craig Tenner, MD - VA New York Harbor Healthcare System / NYU School of Medicine; Jay Pendse, MD, PhD - VHA/NYU; Tim Bigdeli, PhD - VA New York Harbor Health System;
Poster Number: P39
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Precision Health and Genomics, Data Science
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Objective: This study aims to enable clinicians to leverage patients' antipsychotic prescriptions, demographic, and screening records to predict the risk of developing abnormal involuntary movements (AIMS) using electronic health records (EHR).
Description: Tardive dyskinesia (TD) is a debilitating movement disorder caused by long-term antipsychotic use, severely affecting patients' quality of life. The Abnormal Involuntary Movement Scale (AIMS) is the standard tool for TD surveillance in antipsychotic-treated patients. Despite known risk factors such as antipsychotic burden, age, and comorbidities, personalized TD risk prediction remains a challenge.
Methods: The study analyzed EHR data from the VA Cooperative Studies Program #572, covering nearly 20 years and including 7089 veterans with schizophrenia or bipolar disorder on antipsychotics. A random forest model was trained on data from 3974 patients with complete records, using chlorpromazine equivalents, demographic data, and diagnosis information. The model's performance was validated using a 10-fold cross-validation and tested on a separate dataset.
Results: The model achieved a precision-recall AUC of 0.755 and a ROC AUC of 0.726, demonstrating moderate predictive performance.
Conclusion: This study highlights the potential of EHR-based predictive models to identify high-risk TD patients, aiding in early intervention and improved patient outcomes. Future work will integrate genetic data to enhanced prediction accuracy.
Speaker:
Conner Polet, MD MBA
NYU Langone Health
Authors:
Craig Tenner, MD - VA New York Harbor Healthcare System / NYU School of Medicine; Jay Pendse, MD, PhD - VHA/NYU; Tim Bigdeli, PhD - VA New York Harbor Health System;
Conner
Polet,
MD MBA - NYU Langone Health
Using On-Demand Reporting to Enhance Timely Identification of Pediatric Acute Pancreatitis Patients for a Quality Improvement Initiative
Poster Number: P40
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Quality Measures and eCQMs / Quality Improvement, Bridging Analytics, Bedside Care, Clinical Documentation, and Education, Population Health
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Acute Pancreatitis (AP) is rare in children but increasing in incidence with risks for complications like diabetes and recurrent pancreatitis. Timely identification and follow-up with Pediatric Gastroenterologist is often challenging due to short hospital stays and care fragmentation. Our Quality Improvement initiative utilized EHR tools to achieve on-demand and timely identification of AP patients before discharge. This increased GI team involvement from 68% to 84%, improving patient follow-up and monitoring.
Speaker:
John Grisham, MD
Nationwide Children's Hospital
Authors:
John Grisham, MD - Nationwide Children's Hospital; Jennifer Lee, MD - Nationwide Children's Hospital;
Poster Number: P40
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Quality Measures and eCQMs / Quality Improvement, Bridging Analytics, Bedside Care, Clinical Documentation, and Education, Population Health
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Acute Pancreatitis (AP) is rare in children but increasing in incidence with risks for complications like diabetes and recurrent pancreatitis. Timely identification and follow-up with Pediatric Gastroenterologist is often challenging due to short hospital stays and care fragmentation. Our Quality Improvement initiative utilized EHR tools to achieve on-demand and timely identification of AP patients before discharge. This increased GI team involvement from 68% to 84%, improving patient follow-up and monitoring.
Speaker:
John Grisham, MD
Nationwide Children's Hospital
Authors:
John Grisham, MD - Nationwide Children's Hospital; Jennifer Lee, MD - Nationwide Children's Hospital;
John
Grisham,
MD - Nationwide Children's Hospital
From Collection to Acknowledgement: Closed-loop Specimen Tracking Solution to Promote Healthcare Operational Excellence and Sustainability
Poster Number: P41
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Patient Safety, Workflow Efficiency, Interoperability, Documentation Burden
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Organizational Challenges
Secure pre-laboratory specimen delivery is crucial to ensure patient safety and patient outcome. To prevent irretrievable specimen loss, a closed-loop specimen tracking IT solution was developed and piloted in the Hong Kong Hospital Authority. The product streamlined the workflow from specimen collection to laboratory acknowledgement. It also showed high effectiveness on improving working efficiency, enhancing patient safety and maintaining environmental sustainability.
Speaker:
Wing Tung Ho, Bachelor of Nursing
Hong Kong Hospital Authority
Poster Number: P41
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Patient Safety, Workflow Efficiency, Interoperability, Documentation Burden
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Organizational Challenges
Secure pre-laboratory specimen delivery is crucial to ensure patient safety and patient outcome. To prevent irretrievable specimen loss, a closed-loop specimen tracking IT solution was developed and piloted in the Hong Kong Hospital Authority. The product streamlined the workflow from specimen collection to laboratory acknowledgement. It also showed high effectiveness on improving working efficiency, enhancing patient safety and maintaining environmental sustainability.
Speaker:
Wing Tung Ho, Bachelor of Nursing
Hong Kong Hospital Authority
Wing Tung
Ho,
Bachelor of Nursing - Hong Kong Hospital Authority
Coordn8: A Transformative Tool to Optimizing External Medical Record Processing
Poster Number: P42
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Clinical Process Automation, Documentation Burden, Artificial Intelligence/Machine Learning
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
Acquiring and processing medical records is a challenge in healthcare, often delaying patient care. Staff face manual, error-prone tasks, such as obtaining patient consent and processing faxed records. The Center for Health Care Transformation at Penn Medicine partnered with ambulatory practices to address inefficiencies. The result was coordn8, a platform using automation, OCR, and NLP to streamline consent and record processing, improving efficiency and staff satisfaction.
Speaker:
Jency Daniel, DNP, MSN, RN
Penn Medicine
Authors:
Bhavana Kunisetty, BA - Penn Medicine Center for Health Care Transformation and Innovation; Jency Daniel, MSN, DNP - Penn Medicine Center for Health Care Transformation and Innovation; Yevgeniy Gitelman, MD - Penn Medicine Center for Health Care Transformation and Innovation; Gideon Whitehead, MS - Penn Medicine Center for Health Care Transformation and Innovation; Laura Kavanaugh, MHCI - Penn Medicine Center for Health Care Transformation and Innovation; Steve Honeywell, MBA - Penn Medicine Center for Health Care Transformation and Innovation; Lauren Hahn, MBA - Penn Medicine Center for Health Care Transformation and Innovation; Kathleen Lee, MD - Penn Medicine Center for Health Care Transformation and Innovation; Emeka Anyanwu, MD, MScBMI - Penn Medicine Center for Health Care Transformation and Innovation;
Poster Number: P42
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Clinical Process Automation, Documentation Burden, Artificial Intelligence/Machine Learning
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Emerging Technology and Technical Infrastructure
Acquiring and processing medical records is a challenge in healthcare, often delaying patient care. Staff face manual, error-prone tasks, such as obtaining patient consent and processing faxed records. The Center for Health Care Transformation at Penn Medicine partnered with ambulatory practices to address inefficiencies. The result was coordn8, a platform using automation, OCR, and NLP to streamline consent and record processing, improving efficiency and staff satisfaction.
Speaker:
Jency Daniel, DNP, MSN, RN
Penn Medicine
Authors:
Bhavana Kunisetty, BA - Penn Medicine Center for Health Care Transformation and Innovation; Jency Daniel, MSN, DNP - Penn Medicine Center for Health Care Transformation and Innovation; Yevgeniy Gitelman, MD - Penn Medicine Center for Health Care Transformation and Innovation; Gideon Whitehead, MS - Penn Medicine Center for Health Care Transformation and Innovation; Laura Kavanaugh, MHCI - Penn Medicine Center for Health Care Transformation and Innovation; Steve Honeywell, MBA - Penn Medicine Center for Health Care Transformation and Innovation; Lauren Hahn, MBA - Penn Medicine Center for Health Care Transformation and Innovation; Kathleen Lee, MD - Penn Medicine Center for Health Care Transformation and Innovation; Emeka Anyanwu, MD, MScBMI - Penn Medicine Center for Health Care Transformation and Innovation;
Jency
Daniel,
DNP, MSN, RN - Penn Medicine
Data Visualization of Oral Anticoagulation Trends in Atrial Fibrillation Patients
Poster Number: P43
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Data Visualization, Big Data, Medication Adherence
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Clinical practice guidelines changed from prescribing Warfarin to direct oral anticoagulation (DOAC) for atrial fibrillation patients. To evaluate whether prescribing has followed guidelines, we identified Warfarin and DOAC use from prescription claims for members with atrial fibrillation from 2010-2022. Data showed that any OAC use increased, with Warfarin decreasing and DOAC use increasing. DOAC use outpaced Warfarin after 2017. There was variation in when DOACs were preferentially prescribed, and overall OAC use in patient subgroups.
Speaker:
Shannon Richards, MSN
Highmark Health
Authors:
Shannon Richards, MSN - Highmark Health; Charlotte Drury-Gworek, MSN - Highmark Health; Jillian Rung, PhD - Highmark Health; Brent Williams, PhD - Allegheny Health Network;
Poster Number: P43
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Data Visualization, Big Data, Medication Adherence
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Clinical practice guidelines changed from prescribing Warfarin to direct oral anticoagulation (DOAC) for atrial fibrillation patients. To evaluate whether prescribing has followed guidelines, we identified Warfarin and DOAC use from prescription claims for members with atrial fibrillation from 2010-2022. Data showed that any OAC use increased, with Warfarin decreasing and DOAC use increasing. DOAC use outpaced Warfarin after 2017. There was variation in when DOACs were preferentially prescribed, and overall OAC use in patient subgroups.
Speaker:
Shannon Richards, MSN
Highmark Health
Authors:
Shannon Richards, MSN - Highmark Health; Charlotte Drury-Gworek, MSN - Highmark Health; Jillian Rung, PhD - Highmark Health; Brent Williams, PhD - Allegheny Health Network;
Shannon
Richards,
MSN - Highmark Health
Association between Health Equity Scoring and Electronic Health Record Enhancement Deployment Speed
Poster Number: P45
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: EHR Implementation and Optimization, Driving Digital Equity, Workflow Efficiency, EHR Implementation and Optimization
Primary Track: Innovation in Driving Digital Equity and Social Care
Programmatic Theme: Clinical Decision Support and Analytics
Background
In a rapidly evolving clinical and operational environment, the volume of Electronic Health Record (EHR) enhancement requests often exceeds capacity, necessitating scoring for prioritization. At UCSF, Physician Informaticists evaluate enhancement requests using an overall impact score (OIS) and a Health Equity Impact Score (HEIS). It is unknown if these scores are associated with faster enhancement deployment.
Objectives
To quantify the effect of the OIS and HEIS on EHR enhancement deployment speed.
Methods
At UCSF, Physician Informaticists score EHR enhancement requests for impact on outcomes, efficiency, feasibility, and operations. Starting 2019, though not directly incorporated into the OIS, HEIS is used to subjectively categorize these requests based on health equity impact. We studied the association between these scores and the time from initiation of a request to deployment.
Results
Between October 2018 and September 2024, 318 EHR enhancement requests were scored for overall impact. After HEIS was introduced, 209 requests were additionally scored as Beneficial or Neutral. Most Beneficial requests were made by the Emergency Department and aimed to improve the care for vulnerable or socially disadvantaged populations.
The average time from request to deployment was 305.9 days. Higher OIS was associated with a modest but significantly decreased time (2.5 days per increment in score) to deployment. There was no association between HEIS and the time to EHR enhancement deployment.
Conclusions
Though higher OIS is associated with quicker EHR enhancement deployment, HEIS is not. Prioritizing requests that advance health equity may require strategies beyond this standalone score.
Speaker:
Sristi Sharma, M.D., M.P.H.
UCSF
Authors:
Anoop Muniyappa, MD, MS - UCSF; Susan Chim, DHA, MAT - University of CA, San Francisco; Christy Sedore, BS - UCSF; Michael Lang, MD, MPH - UCSF Health; Raman Khanna, MD, MAS - University of California, San Francisco;
Poster Number: P45
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: EHR Implementation and Optimization, Driving Digital Equity, Workflow Efficiency, EHR Implementation and Optimization
Primary Track: Innovation in Driving Digital Equity and Social Care
Programmatic Theme: Clinical Decision Support and Analytics
Background
In a rapidly evolving clinical and operational environment, the volume of Electronic Health Record (EHR) enhancement requests often exceeds capacity, necessitating scoring for prioritization. At UCSF, Physician Informaticists evaluate enhancement requests using an overall impact score (OIS) and a Health Equity Impact Score (HEIS). It is unknown if these scores are associated with faster enhancement deployment.
Objectives
To quantify the effect of the OIS and HEIS on EHR enhancement deployment speed.
Methods
At UCSF, Physician Informaticists score EHR enhancement requests for impact on outcomes, efficiency, feasibility, and operations. Starting 2019, though not directly incorporated into the OIS, HEIS is used to subjectively categorize these requests based on health equity impact. We studied the association between these scores and the time from initiation of a request to deployment.
Results
Between October 2018 and September 2024, 318 EHR enhancement requests were scored for overall impact. After HEIS was introduced, 209 requests were additionally scored as Beneficial or Neutral. Most Beneficial requests were made by the Emergency Department and aimed to improve the care for vulnerable or socially disadvantaged populations.
The average time from request to deployment was 305.9 days. Higher OIS was associated with a modest but significantly decreased time (2.5 days per increment in score) to deployment. There was no association between HEIS and the time to EHR enhancement deployment.
Conclusions
Though higher OIS is associated with quicker EHR enhancement deployment, HEIS is not. Prioritizing requests that advance health equity may require strategies beyond this standalone score.
Speaker:
Sristi Sharma, M.D., M.P.H.
UCSF
Authors:
Anoop Muniyappa, MD, MS - UCSF; Susan Chim, DHA, MAT - University of CA, San Francisco; Christy Sedore, BS - UCSF; Michael Lang, MD, MPH - UCSF Health; Raman Khanna, MD, MAS - University of California, San Francisco;
Sristi
Sharma,
M.D., M.P.H. - UCSF
Standardizing Pre-exposure Prophylaxis for HIV (PrEP) Management in Primary Care
Poster Number: P46
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Workflow Efficiency, Driving Digital Equity, Driving Digital Equity, Clinician Burnout, Usability and Measuring User Experience
Primary Track: Innovation in Driving Digital Equity and Social Care
Programmatic Theme: Clinical Decision Support and Analytics
This implementation study explores barriers to guideline-directed PrEP management in primary care and the implementation of EHR-based clinical decision support (CDS) tools. At a large academic health center, redesigned workflows—including alerts, an updated order panel, and a patient-facing tracker—enhanced PrEP prescribing efficiency and patient engagement. Results demonstrate increased tool adoption, highlighting the scalability of this approach to improve access for underserved populations via telemedicine.
Speaker:
Kevin Truong, MD, MBA
UCLA
Authors:
Hazel Oza, BS - UCLA Health IT; David Gomez, MBA - UCLA Health IT; Ashwin Buchipudi, BS - UCLA Health IT; Annapoorna Chirra, MD - UCLA Health IT; Lawrence Dardick, MD - UCLA;
Poster Number: P46
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Workflow Efficiency, Driving Digital Equity, Driving Digital Equity, Clinician Burnout, Usability and Measuring User Experience
Primary Track: Innovation in Driving Digital Equity and Social Care
Programmatic Theme: Clinical Decision Support and Analytics
This implementation study explores barriers to guideline-directed PrEP management in primary care and the implementation of EHR-based clinical decision support (CDS) tools. At a large academic health center, redesigned workflows—including alerts, an updated order panel, and a patient-facing tracker—enhanced PrEP prescribing efficiency and patient engagement. Results demonstrate increased tool adoption, highlighting the scalability of this approach to improve access for underserved populations via telemedicine.
Speaker:
Kevin Truong, MD, MBA
UCLA
Authors:
Hazel Oza, BS - UCLA Health IT; David Gomez, MBA - UCLA Health IT; Ashwin Buchipudi, BS - UCLA Health IT; Annapoorna Chirra, MD - UCLA Health IT; Lawrence Dardick, MD - UCLA;
Kevin
Truong,
MD, MBA - UCLA
Case Study on the Role of Clinical Informaticists in Ensuring Data Quality and Reusability for Shared Data Sets
Poster Number: P47
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Health IT Standards (USCDI, FHIR®, SMART, etc.), Quality Measures and eCQMs / Quality Improvement, Coordination Across the Continuum of Care
Primary Track: Leadership and Governance
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Medical researchers publish deidentified data sets and code for reproducibility, a requirement for NIH grants since 2023. Data sets, primarily created for specific studies, often lack reusability. We contrast three data sets where the involvement of clinical informaticists earlier in the data set creation process improved data quality and reusability.
Speaker:
Divya Kapoor, MD
Author:
Senthil Nachimuthu, MD, PhD, FAMIA - University of Utah School of Medicine;
Poster Number: P47
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Health IT Standards (USCDI, FHIR®, SMART, etc.), Quality Measures and eCQMs / Quality Improvement, Coordination Across the Continuum of Care
Primary Track: Leadership and Governance
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Medical researchers publish deidentified data sets and code for reproducibility, a requirement for NIH grants since 2023. Data sets, primarily created for specific studies, often lack reusability. We contrast three data sets where the involvement of clinical informaticists earlier in the data set creation process improved data quality and reusability.
Speaker:
Divya Kapoor, MD
Author:
Senthil Nachimuthu, MD, PhD, FAMIA - University of Utah School of Medicine;
Divya
Kapoor,
MD -
Nine Steps for a Smooth and Effective EHR Transition: An AMA STEPS Forward Toolkit for Healthcare Leaders
Poster Number: P48
Click to View Presentation
Click to View Handout
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Change Management, EHR Implementation and Optimization, Clinician Burnout
Primary Track: Leadership and Governance
Programmatic Theme: Organizational Challenges
Introduction: Transitioning to a new electronic health record (EHR) is a complex and costly endeavor that impacts nearly every aspect of a healthcare organization. The shift demands a proactive, strategic approach to mitigate disruptions in patient care, support physician morale, and enhance overall system effectiveness. Most EHR transitions are guided by EHR vendors, and limited empirical data is accessible to guide healthcare leaders preparing to undergo EHR transitions. We worked with the AMA STEPS Forward initiative to develop 9 steps that support EHR transitions.
Methods: Our framework draws from prior EHR implementation studies, practical lessons from health systems undergoing transitions, and personal experiences to compile best practices for each step.
Nine Steps for a Smooth EHR Transition:
1. Create an EHR Transition Team
2. Communicate with End-Users
3. Decide on Implementation Approach
4. Understand the Current EHR State
5. Consider Customization Needs
6. Anticipate Challenges
7. Offer Training and Support
8. Mitigate Other Workplace Stressors
9. Gather Feedback and Optimize
Conclusion: This nine-step framework provides actionable guidance for organizations undertaking EHR transitions, with a focus on minimizing disruptions, fostering clinician engagement, and ensuring patient safety. By following these steps, healthcare organizations can create a foundation for successful EHR implementation and ongoing optimization.
Speaker:
Seppo Rinne, MD, PhD
VA Bedford Healthcare System
Author:
Dominis Matt, BS - American Medical Association;
Poster Number: P48
Click to View Presentation
Click to View Handout
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Change Management, EHR Implementation and Optimization, Clinician Burnout
Primary Track: Leadership and Governance
Programmatic Theme: Organizational Challenges
Introduction: Transitioning to a new electronic health record (EHR) is a complex and costly endeavor that impacts nearly every aspect of a healthcare organization. The shift demands a proactive, strategic approach to mitigate disruptions in patient care, support physician morale, and enhance overall system effectiveness. Most EHR transitions are guided by EHR vendors, and limited empirical data is accessible to guide healthcare leaders preparing to undergo EHR transitions. We worked with the AMA STEPS Forward initiative to develop 9 steps that support EHR transitions.
Methods: Our framework draws from prior EHR implementation studies, practical lessons from health systems undergoing transitions, and personal experiences to compile best practices for each step.
Nine Steps for a Smooth EHR Transition:
1. Create an EHR Transition Team
2. Communicate with End-Users
3. Decide on Implementation Approach
4. Understand the Current EHR State
5. Consider Customization Needs
6. Anticipate Challenges
7. Offer Training and Support
8. Mitigate Other Workplace Stressors
9. Gather Feedback and Optimize
Conclusion: This nine-step framework provides actionable guidance for organizations undertaking EHR transitions, with a focus on minimizing disruptions, fostering clinician engagement, and ensuring patient safety. By following these steps, healthcare organizations can create a foundation for successful EHR implementation and ongoing optimization.
Speaker:
Seppo Rinne, MD, PhD
VA Bedford Healthcare System
Author:
Dominis Matt, BS - American Medical Association;
Seppo
Rinne,
MD, PhD - VA Bedford Healthcare System
EHR system usability trends - work schedules, and work pace in hospital settings
Poster Number: P50
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Usability and Measuring User Experience, Clinician Burnout, Documentation Burden, Disruptive and Innovative Technologies
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Healthcare needs supportive technology. The aim of this study is to assess how health professionals (n = 4160) at a Scandinavian hospital evaluated the EHR usability before and after adoption of an American EHR system, and to describe the association with work-related factors. Preliminary analyses show a significant drop in EHR usability from the old to the new system that stays low 6 and 14 months after Go-live, irrespective of work demands. Analyses on work schedules and shift work sleep disorder will be performed.
Speaker:
Signe Lohmann-Lafrenz, MD
ISM, NTNU, Trondheim, Norway
Poster Number: P50
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Usability and Measuring User Experience, Clinician Burnout, Documentation Burden, Disruptive and Innovative Technologies
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Healthcare needs supportive technology. The aim of this study is to assess how health professionals (n = 4160) at a Scandinavian hospital evaluated the EHR usability before and after adoption of an American EHR system, and to describe the association with work-related factors. Preliminary analyses show a significant drop in EHR usability from the old to the new system that stays low 6 and 14 months after Go-live, irrespective of work demands. Analyses on work schedules and shift work sleep disorder will be performed.
Speaker:
Signe Lohmann-Lafrenz, MD
ISM, NTNU, Trondheim, Norway
Signe
Lohmann-Lafrenz,
MD - ISM, NTNU, Trondheim, Norway
Automated Derivation of Complication after Pediatric Cardiothoracic Surgery from Physician Notes
Poster Number: P51
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Documentation Burden, Data Science
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Identifying surgical complications efficiently is critical for monitoring pediatric cardiovascular surgery outcomes. This study built an Artificial Intelligence/Machine Learning-based model to automate the complication extraction process and successfully captured the first targeted surgical complication “Sternum left open, Planned” from unstructured physician case notes. The model reduces resources needed to identify complications by automating data extraction.
Speaker:
Jiayu Dai, MS
University of Nebraska Omaha
Poster Number: P51
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Documentation Burden, Data Science
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Identifying surgical complications efficiently is critical for monitoring pediatric cardiovascular surgery outcomes. This study built an Artificial Intelligence/Machine Learning-based model to automate the complication extraction process and successfully captured the first targeted surgical complication “Sternum left open, Planned” from unstructured physician case notes. The model reduces resources needed to identify complications by automating data extraction.
Speaker:
Jiayu Dai, MS
University of Nebraska Omaha
Jiayu
Dai,
MS - University of Nebraska Omaha
Enhancing EHR Utilization and Experience
Poster Number: P52
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: EHR Implementation and Optimization, Workflow Efficiency, Usability and Measuring User Experience
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
This session will discuss UTHealth Houston's (UTHH) review of EHR functionality in fall 2023, identifying underutilized opportunities and missing support components. It will cover the creation of the Clinic Operations Systems Support Liaisons team to provide at-the-elbow support, improve Epic workflows, and enhance user experience. The session will also outline the team's responsibilities, governance structure, and the goal of building relationships with department and clinic leadership to standardize best practices.
Speaker:
olasunkanmi adeyinka, MD
ut health
Authors:
olasunkanmi adeyinka, MD - ut health; Lindy Anderson-Papke, MHA - UTHealth Houston;
Poster Number: P52
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: EHR Implementation and Optimization, Workflow Efficiency, Usability and Measuring User Experience
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
This session will discuss UTHealth Houston's (UTHH) review of EHR functionality in fall 2023, identifying underutilized opportunities and missing support components. It will cover the creation of the Clinic Operations Systems Support Liaisons team to provide at-the-elbow support, improve Epic workflows, and enhance user experience. The session will also outline the team's responsibilities, governance structure, and the goal of building relationships with department and clinic leadership to standardize best practices.
Speaker:
olasunkanmi adeyinka, MD
ut health
Authors:
olasunkanmi adeyinka, MD - ut health; Lindy Anderson-Papke, MHA - UTHealth Houston;
olasunkanmi
adeyinka,
MD - ut health
Evaluating Postpartum Hemorrhage: A Fault Tree Analysis Approach
Poster Number: P53
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Bridging Analytics, Bedside Care, Clinical Documentation, and Education, Change Management, Clinical Content and IT Project Governance, Clinician Burnout, Building Value for Informatics via Education and Training
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Fault Tree Analysis (FTA) is a graphical representation of system errors that contribute to adverse outcomes. This approach examines system failures without assigning blame to individuals. This systematic approach will decrease bias and provide a more objective understanding of contributing factors to adverse events. These changes in perspective will encourage behavior modifications among physicians, leading to improvements in patient safety. A Fault Tree Analysis was created to assess postpartum hemorrhage at Kettering Health.
Speaker:
Jennifer Glance, DO
Kettering Health
Author:
Jeffrey Nielson, MD, MS - Kettering Health;
Poster Number: P53
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Bridging Analytics, Bedside Care, Clinical Documentation, and Education, Change Management, Clinical Content and IT Project Governance, Clinician Burnout, Building Value for Informatics via Education and Training
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Fault Tree Analysis (FTA) is a graphical representation of system errors that contribute to adverse outcomes. This approach examines system failures without assigning blame to individuals. This systematic approach will decrease bias and provide a more objective understanding of contributing factors to adverse events. These changes in perspective will encourage behavior modifications among physicians, leading to improvements in patient safety. A Fault Tree Analysis was created to assess postpartum hemorrhage at Kettering Health.
Speaker:
Jennifer Glance, DO
Kettering Health
Author:
Jeffrey Nielson, MD, MS - Kettering Health;
Jennifer
Glance,
DO - Kettering Health
In-hospital Patient Portal use in Admitted Surgical Patients
Poster Number: P54
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: 21st Century Cures (including considerations for special populations such as adolescents), Telemedicine and Telehealth including mHealth, App’s etc, Secure Communication, Innovation in Digital Care, Social Determinants of Health
Primary Track: Innovation in Driving Digital Equity and Social Care
Programmatic Theme: Emerging Technology and Technical Infrastructure
In surgical patients, in-hospital patient portal use is common and increasing, offering opportunities for improved transparency and communication. However, portal use disparities persist in age, race, insurance status, and surgical specialty.
Speaker:
Andrew Bain, MD
University of Texas Southwestern Medical Center
Poster Number: P54
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: 21st Century Cures (including considerations for special populations such as adolescents), Telemedicine and Telehealth including mHealth, App’s etc, Secure Communication, Innovation in Digital Care, Social Determinants of Health
Primary Track: Innovation in Driving Digital Equity and Social Care
Programmatic Theme: Emerging Technology and Technical Infrastructure
In surgical patients, in-hospital patient portal use is common and increasing, offering opportunities for improved transparency and communication. However, portal use disparities persist in age, race, insurance status, and surgical specialty.
Speaker:
Andrew Bain, MD
University of Texas Southwestern Medical Center
Andrew
Bain,
MD - University of Texas Southwestern Medical Center
Clinical Validation of a Machine Learning Model Prior to Implementation: Role, Impact, and Lessons Learned
Poster Number: P55
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Adaptive Clinical Decision Support, Quality Measures and eCQMs / Quality Improvement
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Introduction: Palliative Care (PC) consults often rely on clinicians' initiative to identify and refer appropriate patients. To support this process, we developed a Machine Learning (ML) model that predicts six-month post-hospitalization mortality, prompting clinicians to consider PC consultations for high-risk patients, serving as a Clinical Decision Support (CDS) tool.
Method: Our LightGBM-based ML model was trained on data from Rush University Medical Center, incorporating 173 variables, including demographics, laboratory results, healthcare utilization, ICD-10 codes, and more, among patients aged 18 and older. The model calculates the six-month mortality risk (0–100%) post-hospitalization for inpatients. A two-phase silent validation pilot preceded implementation to ensure technical feasibility, operational impact, and alignment with clinical needs. Phase 1 verified the model’s technical deployment and assessed the feasibility of various risk thresholds. Phase 2 focused on clinical validation, during which three blinded reviewers evaluated the appropriateness of PC consults for flagged patients.
Results: The initial threshold of 0.3 generated excessive triggers with lower clinical concordance, prompting an adjustment to 0.41. During the updated threshold pilot, 137 patients were flagged over 23 days, resulting in a median of 6 triggers per day. Concordance rates reached 68.6% for inpatient consults and 98.5% for outpatient consults. Notably, 86.4% of triggers occurred within three days of admission.
Discussion/Conclusion: This study highlights the importance of pre-implementation validation for refining the model and ensuring operational readiness. This systematic process enhances patient-centered care by enabling the early identification of high-risk patients and facilitating timely palliative care consultations.
Speaker:
Kyunghoon Rhee, MD
Rush University Medical Center
Authors:
Kyunghoon Rhee, MD - Rush University Medical Center; Ajeet Singh, MD MPH - Rush University Medical Center; Vaishvik Chaudhari, MS - Rush University Medical Center; Mia McClintic, BS - Rush University Medical Center; Elaine Chen, MD - Rush University Medical Center; Juan C Rojas, MD, MS - Rush University Medical Center;
Poster Number: P55
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Adaptive Clinical Decision Support, Quality Measures and eCQMs / Quality Improvement
Primary Track: AI and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Introduction: Palliative Care (PC) consults often rely on clinicians' initiative to identify and refer appropriate patients. To support this process, we developed a Machine Learning (ML) model that predicts six-month post-hospitalization mortality, prompting clinicians to consider PC consultations for high-risk patients, serving as a Clinical Decision Support (CDS) tool.
Method: Our LightGBM-based ML model was trained on data from Rush University Medical Center, incorporating 173 variables, including demographics, laboratory results, healthcare utilization, ICD-10 codes, and more, among patients aged 18 and older. The model calculates the six-month mortality risk (0–100%) post-hospitalization for inpatients. A two-phase silent validation pilot preceded implementation to ensure technical feasibility, operational impact, and alignment with clinical needs. Phase 1 verified the model’s technical deployment and assessed the feasibility of various risk thresholds. Phase 2 focused on clinical validation, during which three blinded reviewers evaluated the appropriateness of PC consults for flagged patients.
Results: The initial threshold of 0.3 generated excessive triggers with lower clinical concordance, prompting an adjustment to 0.41. During the updated threshold pilot, 137 patients were flagged over 23 days, resulting in a median of 6 triggers per day. Concordance rates reached 68.6% for inpatient consults and 98.5% for outpatient consults. Notably, 86.4% of triggers occurred within three days of admission.
Discussion/Conclusion: This study highlights the importance of pre-implementation validation for refining the model and ensuring operational readiness. This systematic process enhances patient-centered care by enabling the early identification of high-risk patients and facilitating timely palliative care consultations.
Speaker:
Kyunghoon Rhee, MD
Rush University Medical Center
Authors:
Kyunghoon Rhee, MD - Rush University Medical Center; Ajeet Singh, MD MPH - Rush University Medical Center; Vaishvik Chaudhari, MS - Rush University Medical Center; Mia McClintic, BS - Rush University Medical Center; Elaine Chen, MD - Rush University Medical Center; Juan C Rojas, MD, MS - Rush University Medical Center;
Kyunghoon
Rhee,
MD - Rush University Medical Center
Human-Centered Approach to Audiology Workflow Optimization
Poster Number: P56
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Workflow Efficiency, Usability and Measuring User Experience, Human Factors Testing
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
This case study highlights a repeatable and scalable human factors workflow optimization process. We applied this process to audiology workflows within the Veterans Health Administration’s Federal EHR. Technology and non-technology interventions were identified to address workflow inefficiencies and pain points. Integral to the success of the project were steps taken to engage internal and external stakeholders as well as end users throughout the entire study process.
Speaker:
Janey Barnes, PhD
Ben Allegretti Consulting
Authors:
Jennifer Clark, AuD, MHA - Department of Veterans Affairs; Kyle Maddox, MS - Department of Veterans Affairs; Kendyl Stubleski, Master's of Health Informatics - Department of Veteran Affairs; Janey Barnes, PhD - Ben Allegretti Consulting;
Poster Number: P56
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Workflow Efficiency, Usability and Measuring User Experience, Human Factors Testing
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
This case study highlights a repeatable and scalable human factors workflow optimization process. We applied this process to audiology workflows within the Veterans Health Administration’s Federal EHR. Technology and non-technology interventions were identified to address workflow inefficiencies and pain points. Integral to the success of the project were steps taken to engage internal and external stakeholders as well as end users throughout the entire study process.
Speaker:
Janey Barnes, PhD
Ben Allegretti Consulting
Authors:
Jennifer Clark, AuD, MHA - Department of Veterans Affairs; Kyle Maddox, MS - Department of Veterans Affairs; Kendyl Stubleski, Master's of Health Informatics - Department of Veteran Affairs; Janey Barnes, PhD - Ben Allegretti Consulting;
Janey
Barnes,
PhD - Ben Allegretti Consulting
Electronic Clinical Decision Support System Guided Blood Culture Stewardship in Emergency Departments: Response to the National Blood Culture Media Shortage
Poster Number: P57
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Adaptive Clinical Decision Support, Clinical informatics organizational models, Care Delivery Models
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
This study examined the effect of implementation of electronic clinical decision support system (CDSS)-guided blood culture stewardship across 7 emergency departments during the national shortage of blood culture bottles. An analysis of 13,166 cultures from June to August 2024 showed a significant reduction in volume and increased diagnostic yield after CDSS implementation, with no effect on length of stay or mortality. These findings highlight the effectiveness of CDSS in optimizing resource utilization.
Speaker:
Ankit Sakhuja, MBBS, MS
Icahn School of Medicine at Mount Sinai
Poster Number: P57
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Adaptive Clinical Decision Support, Clinical informatics organizational models, Care Delivery Models
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
This study examined the effect of implementation of electronic clinical decision support system (CDSS)-guided blood culture stewardship across 7 emergency departments during the national shortage of blood culture bottles. An analysis of 13,166 cultures from June to August 2024 showed a significant reduction in volume and increased diagnostic yield after CDSS implementation, with no effect on length of stay or mortality. These findings highlight the effectiveness of CDSS in optimizing resource utilization.
Speaker:
Ankit Sakhuja, MBBS, MS
Icahn School of Medicine at Mount Sinai
Ankit
Sakhuja,
MBBS, MS - Icahn School of Medicine at Mount Sinai
Hypercoagulability Testing Optimization using Clinical Decision Support
Poster Number: P58
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Adaptive Clinical Decision Support, EHR Implementation and Optimization, Quality Measures and eCQMs / Quality Improvement
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Thrombophilia testing practices exhibit significant variability across medical specialties, leading to discrepancies in the perception of tests associated with unprovoked thrombosis and their influence on clinical decision-making. Substantial expenses related to inappropriate laboratory testing can reach up to $1 million annually. Implementing clinical decision support (CDS) has shown promise in reducing unnecessary testing and alleviating patient financial burdens. At Stony Brook University Hospital, outdated and conflicting PowerPlans within the electronic health record exacerbated these challenges.
To address this, we analyzed 4,738 thrombophilia orders before and 2,290 after implementing the redesigned PowerPlans. Results demonstrated a reduction in inappropriate testing rates, from 18.8±5.6% pre-implementation to 12.2±3.4% post-implementation (p = 0.00001). The intervention is estimated to yield annual savings of $28,608, with monthly reductions in patient costs of $2,383.97.
Our findings suggest that multi-disciplinary teams can enhance the effectiveness of thrombophilia testing, optimize care, and lower costs through targeted redesign of clinical decision support tools.
Speaker:
Lyncean Ung, D.O.
Stony Brook Medicine
Authors:
Lyncean Ung, D.O. - Stony Brook Medicine; Rachel Wong, MD, MPH, MBA, MS - Stony Brook University, School of Medicine; Veena Lingam, MBBS - Moffitt Cancer Center-USF; Suguna Raju, MD - Stony Brook Medicine; Luke Li, MD - Stony Brook Medicine; Theodore Gabig, MD - Stony Brook Medicine; Lisa Senzel, MD - Stony Brook Medicine; Michael Guido, MD - Stony Brook Medicine;
Poster Number: P58
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Adaptive Clinical Decision Support, EHR Implementation and Optimization, Quality Measures and eCQMs / Quality Improvement
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Clinical Decision Support and Analytics
Thrombophilia testing practices exhibit significant variability across medical specialties, leading to discrepancies in the perception of tests associated with unprovoked thrombosis and their influence on clinical decision-making. Substantial expenses related to inappropriate laboratory testing can reach up to $1 million annually. Implementing clinical decision support (CDS) has shown promise in reducing unnecessary testing and alleviating patient financial burdens. At Stony Brook University Hospital, outdated and conflicting PowerPlans within the electronic health record exacerbated these challenges.
To address this, we analyzed 4,738 thrombophilia orders before and 2,290 after implementing the redesigned PowerPlans. Results demonstrated a reduction in inappropriate testing rates, from 18.8±5.6% pre-implementation to 12.2±3.4% post-implementation (p = 0.00001). The intervention is estimated to yield annual savings of $28,608, with monthly reductions in patient costs of $2,383.97.
Our findings suggest that multi-disciplinary teams can enhance the effectiveness of thrombophilia testing, optimize care, and lower costs through targeted redesign of clinical decision support tools.
Speaker:
Lyncean Ung, D.O.
Stony Brook Medicine
Authors:
Lyncean Ung, D.O. - Stony Brook Medicine; Rachel Wong, MD, MPH, MBA, MS - Stony Brook University, School of Medicine; Veena Lingam, MBBS - Moffitt Cancer Center-USF; Suguna Raju, MD - Stony Brook Medicine; Luke Li, MD - Stony Brook Medicine; Theodore Gabig, MD - Stony Brook Medicine; Lisa Senzel, MD - Stony Brook Medicine; Michael Guido, MD - Stony Brook Medicine;
Lyncean
Ung,
D.O. - Stony Brook Medicine
The Effect of Ambient AI on Clinician Burnout at University of Iowa Health Care
Poster Number: P59
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Ambient documentation, Clinician Burnout, Documentation Burden, Learning Health System
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
- Definition of Problem or Gap
Healthcare burnout is a significant challenge. Electronic health records (EHRs) were meant to simplify but have instead increased workloads and stress, leading to higher burnout rates. EHRs contribute to information overload and work disruptions, reducing meaningful patient interactions and causing dissatisfaction. Studies show that for every hour spent with patients, physicians need two hours for electronic documentation and desk work, extending the workload beyond regular hours and increasing burnout.
- What did you use to address the problem or gap?
Ambient AI documentation software was launched at UIHC after a successful pilot in May 2024. The pilot showed a reduction in burnout from 69% to 43%. With our enterprise implementation, the Stanford Professional Fulfillment Index (PFI) measured burnout reductions by surveying users when they started using Ambient AI and again after 30 days.
- Results
Out of 576 initial PFI respondents, 284 completed 30-day follow-ups. Overall burnout decreased from 49% to 33% for AI users but remained unchanged for non-users (45% vs 47%). Scores improved in overall burnout (3.3 to 2.5), work exhaustion (4.2 to 3.4), and interpersonal disengagement (2.7 to 1.9) among AI users. The highest users of Ambient AI showed the greatest improvements.
- Discussion
Ambient AI led to significant decreases in burnout symptoms like work exhaustion and interpersonal disengagement, improving clinicians' experiences with patient care.
- Conclusions
Ambient AI at the University of Iowa Health Care reduced clinician burnout significantly, showcasing that well-implemented AI can serve as a powerful tool to address burnout in healthcare.
Speaker:
Jason Misurac, MD, MS
University of Iowa - Dept. of Pediatrics
Authors:
Jason Misurac, MD, MS - University of Iowa - Dept. of Pediatrics; James Blum, MD - University of Iowa;
Poster Number: P59
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Ambient documentation, Clinician Burnout, Documentation Burden, Learning Health System
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
- Definition of Problem or Gap
Healthcare burnout is a significant challenge. Electronic health records (EHRs) were meant to simplify but have instead increased workloads and stress, leading to higher burnout rates. EHRs contribute to information overload and work disruptions, reducing meaningful patient interactions and causing dissatisfaction. Studies show that for every hour spent with patients, physicians need two hours for electronic documentation and desk work, extending the workload beyond regular hours and increasing burnout.
- What did you use to address the problem or gap?
Ambient AI documentation software was launched at UIHC after a successful pilot in May 2024. The pilot showed a reduction in burnout from 69% to 43%. With our enterprise implementation, the Stanford Professional Fulfillment Index (PFI) measured burnout reductions by surveying users when they started using Ambient AI and again after 30 days.
- Results
Out of 576 initial PFI respondents, 284 completed 30-day follow-ups. Overall burnout decreased from 49% to 33% for AI users but remained unchanged for non-users (45% vs 47%). Scores improved in overall burnout (3.3 to 2.5), work exhaustion (4.2 to 3.4), and interpersonal disengagement (2.7 to 1.9) among AI users. The highest users of Ambient AI showed the greatest improvements.
- Discussion
Ambient AI led to significant decreases in burnout symptoms like work exhaustion and interpersonal disengagement, improving clinicians' experiences with patient care.
- Conclusions
Ambient AI at the University of Iowa Health Care reduced clinician burnout significantly, showcasing that well-implemented AI can serve as a powerful tool to address burnout in healthcare.
Speaker:
Jason Misurac, MD, MS
University of Iowa - Dept. of Pediatrics
Authors:
Jason Misurac, MD, MS - University of Iowa - Dept. of Pediatrics; James Blum, MD - University of Iowa;
Jason
Misurac,
MD, MS - University of Iowa - Dept. of Pediatrics
Evaluating the Impact of AI Scribe Integration in Primary Care and Subspecialty Clinics: A Mixed-Methods Pilot to Address Documentation Burden and Burnout
Poster Number: P60
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Ambient documentation, Clinician Burnout, Documentation Burden, Workflow Efficiency, Clinician Burnout, Human Factors Testing, Artificial Intelligence/Machine Learning, Usability and Measuring User Experience
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
What might the attendee be able to do after being in your session?
Understand the potential of AI scribe tools to reduce documentation burden, improve workflows, and identify implementation challenges.
Description of the Problem
EHR documentation contributes to clinician burnout (1,2). AI scribes show promise for alleviating this burden (3,4), but their utility in diverse clinical settings requires further evaluation.
Methods: What did you do to address the problem or gap?
A mixed-methods pilot involving 54 clinicians in two phases assessed a non-integrated and Epic-integrated AI scribe tool using surveys, interviews, and EHR data.
Results:
Interviews showed several positive themes (Table 1). Survey results showed increased satisfaction (p=0.004) and reduced documentation time (p=0.001) (Table 2). Burnout symptoms decreased in Phase 1 (from 3/20 to 0/14 respondents). While editing challenges persisted, feedback for complex visits was positive. Satisfaction was higher in Phase 2, linked to Epic integration and tool improvements.
Discussion of Results
The tool's utility varied; it was valuable for longer encounters but raised concerns about editing time. Qualitative feedback highlighted enthusiasm, particularly with the integrated tool. Metrics for efficiency or burnout were limited due to sample size but suggest promising trends.
Conclusion
AI scribe tools show potential for reducing documentation burden, though their impact varies by provider. More research is needed to refine implementation strategies and measure outcomes across diverse clinical settings.
Attendee’s Take-away Tool
AI scribes can improve documentation workflows, particularly for complex visits, offering a promising solution for reducing clinician burnout.
Speaker:
Leopold Arko, MD, MS
University of Minnesota
Authors:
Carly Hudelson, MD MSc - University of Minnesota; Melissa Gunderson, MD - University of Minnesota; Deborah Pestka, PharmD, PhD; Tori Christiaansen, MD - Fairview Health System; Sameer Badlani, MD, FACP - M Health Fairview; Rebecca Markowitz, MD - M Health Fairview; Genevieve Melton-Meaux, MD, PhD - University of Minnesota;
Poster Number: P60
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Ambient documentation, Clinician Burnout, Documentation Burden, Workflow Efficiency, Clinician Burnout, Human Factors Testing, Artificial Intelligence/Machine Learning, Usability and Measuring User Experience
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
What might the attendee be able to do after being in your session?
Understand the potential of AI scribe tools to reduce documentation burden, improve workflows, and identify implementation challenges.
Description of the Problem
EHR documentation contributes to clinician burnout (1,2). AI scribes show promise for alleviating this burden (3,4), but their utility in diverse clinical settings requires further evaluation.
Methods: What did you do to address the problem or gap?
A mixed-methods pilot involving 54 clinicians in two phases assessed a non-integrated and Epic-integrated AI scribe tool using surveys, interviews, and EHR data.
Results:
Interviews showed several positive themes (Table 1). Survey results showed increased satisfaction (p=0.004) and reduced documentation time (p=0.001) (Table 2). Burnout symptoms decreased in Phase 1 (from 3/20 to 0/14 respondents). While editing challenges persisted, feedback for complex visits was positive. Satisfaction was higher in Phase 2, linked to Epic integration and tool improvements.
Discussion of Results
The tool's utility varied; it was valuable for longer encounters but raised concerns about editing time. Qualitative feedback highlighted enthusiasm, particularly with the integrated tool. Metrics for efficiency or burnout were limited due to sample size but suggest promising trends.
Conclusion
AI scribe tools show potential for reducing documentation burden, though their impact varies by provider. More research is needed to refine implementation strategies and measure outcomes across diverse clinical settings.
Attendee’s Take-away Tool
AI scribes can improve documentation workflows, particularly for complex visits, offering a promising solution for reducing clinician burnout.
Speaker:
Leopold Arko, MD, MS
University of Minnesota
Authors:
Carly Hudelson, MD MSc - University of Minnesota; Melissa Gunderson, MD - University of Minnesota; Deborah Pestka, PharmD, PhD; Tori Christiaansen, MD - Fairview Health System; Sameer Badlani, MD, FACP - M Health Fairview; Rebecca Markowitz, MD - M Health Fairview; Genevieve Melton-Meaux, MD, PhD - University of Minnesota;
Leopold
Arko,
MD, MS - University of Minnesota
Differences in Ambulatory Physician EHR Use by Telemedicine Intensity
Poster Number: P61
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Telemedicine and Telehealth including mHealth, App’s etc, Care Delivery Models, Workflow Efficiency, Usability and Measuring User Experience, Documentation Burden, Data Science
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
This study investigates the impact of telemedicine on ambulatory physician EHR use across two academic medical centers, UCSF and WashU, in the post-COVID onset period. Using EHR audit log data, it analyzes how telemedicine affects physicians' EHR use, examining frequency-based (e.g., information seeking, patient communication) and time-based (e.g., EHR time, documentation time) measures across workdays. The findings reveal significant differences in EHR use patterns, influenced by institutional telemedicine infrastructure, highlighting the need for adaptable, site-specific telemedicine workflows.
Speaker:
A J Holmgren, PhD
University of California, San Francisco
Authors:
Julia Adler-Milstein, PhD - UCSF School of Medicine; Robert Thombley, BS - University of California, San Francisco; Sunny Lou, MD, PhD - Washington University, St. Louis; Elise Eiden, MS - Washington University in St. Louis; Thomas Kannampallil, PhD - Washington University School of Medicine; A J Holmgren, PhD - University of California, San Francisco;
Poster Number: P61
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Telemedicine and Telehealth including mHealth, App’s etc, Care Delivery Models, Workflow Efficiency, Usability and Measuring User Experience, Documentation Burden, Data Science
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
This study investigates the impact of telemedicine on ambulatory physician EHR use across two academic medical centers, UCSF and WashU, in the post-COVID onset period. Using EHR audit log data, it analyzes how telemedicine affects physicians' EHR use, examining frequency-based (e.g., information seeking, patient communication) and time-based (e.g., EHR time, documentation time) measures across workdays. The findings reveal significant differences in EHR use patterns, influenced by institutional telemedicine infrastructure, highlighting the need for adaptable, site-specific telemedicine workflows.
Speaker:
A J Holmgren, PhD
University of California, San Francisco
Authors:
Julia Adler-Milstein, PhD - UCSF School of Medicine; Robert Thombley, BS - University of California, San Francisco; Sunny Lou, MD, PhD - Washington University, St. Louis; Elise Eiden, MS - Washington University in St. Louis; Thomas Kannampallil, PhD - Washington University School of Medicine; A J Holmgren, PhD - University of California, San Francisco;
A J
Holmgren,
PhD - University of California, San Francisco
Early Patient Safety Report Triaging Using Large Language Models
Poster Number: P62
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Patient Safety, Artificial Intelligence/Machine Learning, Documentation Burden, Workflow Efficiency
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Manual triage of patient safety reports (PSRs) is inefficient and subjective. This study applied large language models (LLMs) to automate event-type and harm-level classification over 71,287 PSRs. Three approaches of sequence text classification, text label generation, and multi-output generation were evaluated, with sequence text classification achieving the highest performance (Accuracy:0.88, F1: 0.83) for event-type prediction. Although harm-level predictions remain challenging, LLMs show significant potential for improving efficiency and standardization in patient safety monitoring.
Speaker:
Moein Enayati, PhD
Mayo Clinic
Authors:
Moein Enayati, PhD - Mayo Clinic; Margaret Zhou, MS - Mayo Clinic; Shrinath Patel, MS - Mayo Clinic; Gavin Schaeferle, MS - Mayo Clinic; Shiba Kuanar, PhD - Mayo Clinic - Rochester; Jennifer Lamers, MBA - Mayo Clinic; Joe Nienow, MS - Mayo Clinic; Kannan Ramar, MD, MBA - Mayo Clinic; Jill Nagel, MBA - Mayo Clinic; Che Ngufor, PhD - Mayo Clinic;
Poster Number: P62
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Patient Safety, Artificial Intelligence/Machine Learning, Documentation Burden, Workflow Efficiency
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Manual triage of patient safety reports (PSRs) is inefficient and subjective. This study applied large language models (LLMs) to automate event-type and harm-level classification over 71,287 PSRs. Three approaches of sequence text classification, text label generation, and multi-output generation were evaluated, with sequence text classification achieving the highest performance (Accuracy:0.88, F1: 0.83) for event-type prediction. Although harm-level predictions remain challenging, LLMs show significant potential for improving efficiency and standardization in patient safety monitoring.
Speaker:
Moein Enayati, PhD
Mayo Clinic
Authors:
Moein Enayati, PhD - Mayo Clinic; Margaret Zhou, MS - Mayo Clinic; Shrinath Patel, MS - Mayo Clinic; Gavin Schaeferle, MS - Mayo Clinic; Shiba Kuanar, PhD - Mayo Clinic - Rochester; Jennifer Lamers, MBA - Mayo Clinic; Joe Nienow, MS - Mayo Clinic; Kannan Ramar, MD, MBA - Mayo Clinic; Jill Nagel, MBA - Mayo Clinic; Che Ngufor, PhD - Mayo Clinic;
Moein
Enayati,
PhD - Mayo Clinic
Standardizing the Combat Trauma Registry: Insights and Lessons Learned
Poster Number: P63
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Big Data, Coordination Across the Continuum of Care, Data Governance, Data Science, Data Visualization, Learning Health System
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Problem: Having accurate and data is paramount for maximizing patient outcomes. The variety of data sources, formats and timing inconsistencies create a loss of granularity, misrepresentation of semantic meaning, and syntactic collision. A common data model to aggregate and standardize complex data from a variety of sources would negate these interoperability concerns and improve trauma outcomes.
Methods: The totality of the DODTR’s 150,000 records were summarized and exported into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Next a high-level mapping from the DODTR to standard OMOP CDM V5.4 tables was designed. Code mapping from source data to standard OMOP concepts was performed with 3 clinical informaticist.
Results: The DODTR has 9100 unique ICD 9 and 10 Diagnosis and Procedure terms. 4807 (53%) had a match score of 0.9 or greater. We reviewed initial mappings and adjudicated of 8193 (90%) terms. 90% of ICD10 and 71.9% of ICD9 unique terms were mapped, however, both were mapped to at least 98% based on frequency. Equivalency showed that 5317 (58%) of ICD10 and 5102 (69.6%) mapped to ‘Equal’. ICD9 had only 138 (1.9%) wider terms while ICD10 had 2792 (31%) indicating that the granularity of ICD10 codes was lost when moving to SNOMED. Quality assurance, imputation, and custom codes were identified for future refinement.
Conclusion: This is the first described mapping from a trauma registry to the OMOP CDM. Our frameworks provides a reliable, repeatable and generalizable strategy to convert a trauma registry into the OMOP CDM.
Speaker:
Darshan Thota, MD
United States Navy
Authors:
Jonathan Stallings, PhD - Joint Trauma System; Michael Shiels, RN - Joint Trauma System; Jonpaul Trossi, RN - Joint Trauma System; Jennifer Gurney, MD - Defense Health Agency;
Poster Number: P63
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Big Data, Coordination Across the Continuum of Care, Data Governance, Data Science, Data Visualization, Learning Health System
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Problem: Having accurate and data is paramount for maximizing patient outcomes. The variety of data sources, formats and timing inconsistencies create a loss of granularity, misrepresentation of semantic meaning, and syntactic collision. A common data model to aggregate and standardize complex data from a variety of sources would negate these interoperability concerns and improve trauma outcomes.
Methods: The totality of the DODTR’s 150,000 records were summarized and exported into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Next a high-level mapping from the DODTR to standard OMOP CDM V5.4 tables was designed. Code mapping from source data to standard OMOP concepts was performed with 3 clinical informaticist.
Results: The DODTR has 9100 unique ICD 9 and 10 Diagnosis and Procedure terms. 4807 (53%) had a match score of 0.9 or greater. We reviewed initial mappings and adjudicated of 8193 (90%) terms. 90% of ICD10 and 71.9% of ICD9 unique terms were mapped, however, both were mapped to at least 98% based on frequency. Equivalency showed that 5317 (58%) of ICD10 and 5102 (69.6%) mapped to ‘Equal’. ICD9 had only 138 (1.9%) wider terms while ICD10 had 2792 (31%) indicating that the granularity of ICD10 codes was lost when moving to SNOMED. Quality assurance, imputation, and custom codes were identified for future refinement.
Conclusion: This is the first described mapping from a trauma registry to the OMOP CDM. Our frameworks provides a reliable, repeatable and generalizable strategy to convert a trauma registry into the OMOP CDM.
Speaker:
Darshan Thota, MD
United States Navy
Authors:
Jonathan Stallings, PhD - Joint Trauma System; Michael Shiels, RN - Joint Trauma System; Jonpaul Trossi, RN - Joint Trauma System; Jennifer Gurney, MD - Defense Health Agency;
Darshan
Thota,
MD - United States Navy
Optimizing Pre-hospital Blood Transfusion in Austere Environments: A Validation Study of Machine Learning Models
Poster Number: P64
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Care Delivery Models, Data Science, Data Visualization, Disruptive and Innovative Technologies, Learning Health System
Primary Track: AI and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Problem: Hemorrhagic shock is the number one cause of preventable death on the battlefield and requires aggressive treatment with blood products. Determining the optimal thresholds and variables for transfusion in needed to maximize outcomes. In this study, we will validate the accuracy of previously studied civilian machine learning prediction models to find the optimal sensitivity and specificity pre-hospital transfusion variables for OPMED.
Methods: We replicated a study by Zadorozny et al using a Fast Frugal Tree and additional machine learning models to predict blood transfusion in the pre-hospital operational medicine setting. We compared an ensemble of machine learning techniques using a fast frugal trees (FFT) to generate a clinical decision support tool using optimal transfusion cut-offs for systolic blood pressure, pre-hospital lactate, shock index, and Abbreviated Injury Score. We will compare CART, LR, RF and SVM using area under the curve and receiver operator curves and used Youden’s J index to asses accuracy.
Results: Preliminary results showed only a subset of Zadorozny’s 29 variables are captured in our registry. Our final data set resulted in choosing transfused blood, lowest systolic blood pressure, lowest shock index, lowest diastolic blood pressure, heart rate, and injury types. We found a similar level of performance using logistic regression compared to (FFT) and comparison model performance is currently underway.
Conclusion: Given the precarious nature of the deployed environment, accurate mission planning for material solutions such as blood products is critical to maximizing patient outcomes.
Preliminary results show utility in using ML models for transfusion prediction.
Speaker:
Darshan Thota, MD
United States Navy
Author:
Jonathan Stallings, PhD - Joint Trauma System;
Poster Number: P64
Click to View Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Care Delivery Models, Data Science, Data Visualization, Disruptive and Innovative Technologies, Learning Health System
Primary Track: AI and Care Outcomes
Programmatic Theme: Informatics-Driven Value-Based Healthcare
Problem: Hemorrhagic shock is the number one cause of preventable death on the battlefield and requires aggressive treatment with blood products. Determining the optimal thresholds and variables for transfusion in needed to maximize outcomes. In this study, we will validate the accuracy of previously studied civilian machine learning prediction models to find the optimal sensitivity and specificity pre-hospital transfusion variables for OPMED.
Methods: We replicated a study by Zadorozny et al using a Fast Frugal Tree and additional machine learning models to predict blood transfusion in the pre-hospital operational medicine setting. We compared an ensemble of machine learning techniques using a fast frugal trees (FFT) to generate a clinical decision support tool using optimal transfusion cut-offs for systolic blood pressure, pre-hospital lactate, shock index, and Abbreviated Injury Score. We will compare CART, LR, RF and SVM using area under the curve and receiver operator curves and used Youden’s J index to asses accuracy.
Results: Preliminary results showed only a subset of Zadorozny’s 29 variables are captured in our registry. Our final data set resulted in choosing transfused blood, lowest systolic blood pressure, lowest shock index, lowest diastolic blood pressure, heart rate, and injury types. We found a similar level of performance using logistic regression compared to (FFT) and comparison model performance is currently underway.
Conclusion: Given the precarious nature of the deployed environment, accurate mission planning for material solutions such as blood products is critical to maximizing patient outcomes.
Preliminary results show utility in using ML models for transfusion prediction.
Speaker:
Darshan Thota, MD
United States Navy
Author:
Jonathan Stallings, PhD - Joint Trauma System;
Darshan
Thota,
MD - United States Navy
OPMED Informatics: Future Fight Faster!
Poster Number: P65
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Clinical informatics organizational models, Clinical Process Automation, Coordination Across the Continuum of Care, Documentation Burden, EHR Implementation and Optimization, Innovation in Digital Care, Learning Health System
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Description of the Problem
During large-scale combat operations, military medicine will face air space superiority challenges unseen for a century. This means the Military Health System (MHS) will have to contend with more patients, sicker patients and longer hold times. In order close the Kill Chain and maximize survival, several initiatives ranging from electronic health records to common data models to generative AI solutions are being leveraged in order to maximize combat casualty care outcomes.
Methods:
An environmental scan of the Military Health Systems past, present and future electronic system, data flow and modernization efforts was performed. Key stakeholders, material solutions, software acquisitions program offices, clinical communities and end-users were identified with interviews conducted to identify pain points, needs, and barriers.
Results: Gap analysis revealed several topics and themes from the key stakeholder discussion. Key domains for the MHS to focus on are: Discrete data capture with modernized electronic healthcare records, clinical decision support based on the Joint Trauma System’s Clinical Practice Guidelines, interoperability between deployed and stateside systems, dashboard generation from near real time data, synthetic data generation for training, and passive data collection resulting in real time data collection. Several projects levering emerging technology are currently under investigation to maximize outcomes in Operational Medicine environment.
Conclusion: This presentation will give an overview of the new electronic healthcare records, integrated clinical decision support, passive data mapping, near real-time dashboards, synthetic data generation and neural network creation for operational medicine.
Speaker:
Darshan Thota, MD
United States Navy
Author:
Jennifer Gurney, MD - Defense Health Agency;
Poster Number: P65
Click to View Presentation
2025 CIC 25x5 Presentation
Presentation Time: 04:00 PM - 05:30 PM
Abstract Keywords: Artificial Intelligence/Machine Learning, Clinical informatics organizational models, Clinical Process Automation, Coordination Across the Continuum of Care, Documentation Burden, EHR Implementation and Optimization, Innovation in Digital Care, Learning Health System
Primary Track: Documentation Burden, Clinician Well-Being and Care Outcomes
Programmatic Theme: Usability, Efficiency, and User Experience
Description of the Problem
During large-scale combat operations, military medicine will face air space superiority challenges unseen for a century. This means the Military Health System (MHS) will have to contend with more patients, sicker patients and longer hold times. In order close the Kill Chain and maximize survival, several initiatives ranging from electronic health records to common data models to generative AI solutions are being leveraged in order to maximize combat casualty care outcomes.
Methods:
An environmental scan of the Military Health Systems past, present and future electronic system, data flow and modernization efforts was performed. Key stakeholders, material solutions, software acquisitions program offices, clinical communities and end-users were identified with interviews conducted to identify pain points, needs, and barriers.
Results: Gap analysis revealed several topics and themes from the key stakeholder discussion. Key domains for the MHS to focus on are: Discrete data capture with modernized electronic healthcare records, clinical decision support based on the Joint Trauma System’s Clinical Practice Guidelines, interoperability between deployed and stateside systems, dashboard generation from near real time data, synthetic data generation for training, and passive data collection resulting in real time data collection. Several projects levering emerging technology are currently under investigation to maximize outcomes in Operational Medicine environment.
Conclusion: This presentation will give an overview of the new electronic healthcare records, integrated clinical decision support, passive data mapping, near real-time dashboards, synthetic data generation and neural network creation for operational medicine.
Speaker:
Darshan Thota, MD
United States Navy
Author:
Jennifer Gurney, MD - Defense Health Agency;
Darshan
Thota,
MD - United States Navy
Designing Clinician Personas to Enhance Technology Adoption
Category
Poster - Regular
Description
Custom CSS
double-click to edit, do not edit in source
5/20/2025 05:30 PM (Pacific Time (US & Canada))