Medication Information that Matters: EHR-Related Insights from a National Survey of Family Medicine Physicians

Presentation Type: Oral Presentation - Regular
Presentation Time: 03:30 PM - 03:42 PM

Abstract Keywords: Workforce Automation, Communication, and Workflow Efficiency, Clinical Decision Support and Care Pathways, Health Policy, Reimbursement and Affordability, and Sustainability, Outcomes Improvement and Equity
Primary Track: Implementing Real-World Change, Digital Engagement, and Connected Health


Family medicine physicians need accurate medication information to manage polypharmacy, affordability challenges, and prior authorizations. Using 2023 ABFM CCQ data (n=4,184), we examined how family medicine physicians value access to medication information, including outside medication histories, PA determinations, and patient-specific pricing information. The perceived importance of medication information varied by physician participation in value-based care, patient population (vulnerability, rurality), and region. Findings align with federal efforts to strengthen interoperability, PA, and real-time prescription benefit tools.

Speaker(s):
Meghan Hufstader Gabriel, PhD
Office of the Assistant Secretary for Technology Policy

Author(s):
Caroline Sloane, MD - Duke Department of Medicine; Tricia Lee Rolle, PhD, PharmD - Assistant Secretary for Technology Policy; Jordan Everson, PhD - Georgetown University School of Medicine; Sara Turbow, MD, MPH - Emory School of Medicine; Anna Doar Sinaiko, PhD - Harvard TH Chan School of Public Health;

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Leadership Perspectives on Asynchronous Electronic Health Record Work in Primary Care: A Survey of Division Chiefs in General Internal Medicine

Presentation Type: Oral Presentation - Regular
Presentation Time: 03:42 PM - 03:54 PM

Abstract Keywords: Clinician Well-Being, Workforce Automation, Communication, and Workflow Efficiency, Health Policy, Reimbursement and Affordability, and Sustainability
Primary Track: Advancing Wellness for Providers and Community with Consideration of Human Factors


We surveyed Division Chiefs in the Association of Chiefs and Leaders in General Internal Medicine to characterize definitions, staff assistance, and compensation for asynchronous work. Respondents showed strong agreement on what constitutes asynchronous work and commonly provided staff assistance and protected time. However, only 24% billed for this work, and nearly 90% reported that it generated less than or equal to 5% of division revenue, highlighting a mismatch between clinical burden and financial support.

Speaker(s):
Melissa Chiang, MD, MBA
UCSF

Author(s):
Kiana Smith, BS - UCSF; Mitchell Feldman, MD, MPhil - UCSF; Ashok Reddy, MD, MS - University of Washington; Eve Kerr, MD, MPH - University of Michigan; Lisa Rotenstein, MD, MBA, MSc - UCSF;

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AI‑based constraint-optimization shift scheduling - a comparative pilot study

Presentation Type: Oral Presentation - Regular
Presentation Time: 03:54 PM - 04:06 PM

Abstract Keywords: Innovation Partnerships, Implementation Science, and Learning Health Systems, Change Management, Clinician Well-Being, Analytics, Registries, and the Digital Command Center, Outcomes Improvement and Equity, Leadership and Strategy
Primary Track: Advancing Wellness for Providers and Community with Consideration of Human Factors


Background: Resident scheduling is a complex optimization problem influencing workload, fatigue, and equity. Real-world data on AI-based scheduling in healthcare are limited.
Objective: To compare an AI-based constraint-optimization scheduler with a legacy rule-based system for pediatric residency night-call assignments.
Methods: Retrospective comparative study across two 8-month periods (legacy: Jan–Aug 2024; AI: Jan–Aug 2025) at a tertiary pediatric center. Resident-month was the analytic unit. Outcomes included workload (calls and weekend calls per resident-month), exceedances (> 6 calls, > 2 weekends), undesirable sequences, fairness (MAE-ES, RMSE-ES), schedule publication lead-time, and resident-reported satisfaction (pre/post questionnaires).
Results: Among 893 resident-months (2,144 vs 2,185 shifts), mean calls declined from 5.24 to 4.51 (Δ −0.73; p < 0.001) and weekend calls from 1.54 to 1.30 (Δ −0.24; p < 0.001). Exceedances and undesirable sequences decreased markedly, and fairness improved (MAE-ES 1.20→1.01; RMSE-ES 1.51→1.24). Schedule publication lead-time doubled (10.6→21.8 days; p < 0.001). Resident satisfaction improved for software usability (6.8→8.7; p < 0.001), schedule timeliness (3.3→4.6; p < 0.001), perceived fairness (3.0→3.6; p = 0.012), and reduced frequency of consecutive nights (3.1→4.2; p < 0.001). Interrupted-time-series models confirmed immediate and sustained gains in workload, fairness, and lead-time.
Conclusions: Implementation of an AI-based constraint-optimization scheduler significantly reduced workload, improved fairness and scheduling timeliness, and increased resident satisfaction. AI-driven scheduling offers a promising strategy to enhance equity, efficiency, and well-being in clinical training environments.

Speaker(s):
David Gilad, MD PhD
Sheba Medical Center

Author(s):
David Gilad, MD PhD - Sheba Medical Center; Tzofnat Farbstein-Aljanati, MD - Edmond and Lily Safra Children’s Hospital, Sheba Medical Center, Ramat-Gan, Israel; Reut Kassif-Lerner, MD - Edmond and Lily Safra Children’s Hospital, Sheba Medical Center, Ramat-Gan, Israel; Moshe Ashkenazi, MD, MHA - Edmond and Lily Safra Children’s Hospital, Sheba Medical Center, Ramat-Gan, Israel; Itai Pessach, MD PhD - Edmond and Lily Safra Children’s Hospital, Sheba Medical Center, Ramat-Gan, Israel;

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A State-wide Study of Provider Interests and Concerns about the use of AI Tools Within a Health Information Exchange

Presentation Type: Oral Presentation - Regular
Presentation Time: 04:06 PM - 04:18 PM

Abstract Keywords: Analytical Artificial Intelligence: ML, Digital Pathology, Imaging AI, Predictive Analytics, Governance, Innovation Partnerships, Implementation Science, and Learning Health Systems, Clinician Well-Being, Leadership and Strategy, Change Management
Primary Track: Driving Change at Scale through Effective Leadership and Governance


This mixed-methods study examined clinician perspectives on artificial intelligence (AI) integration within a state Health Information Exchange (HIE). Nine key informant interviews and a 27-item survey of prescribers in Connecticut (CT) assessed current use, perceived value, and desired AI tools. Clinicians expressed broad interest in HIE-level AI, with preferred tools varying significantly by practice setting. Findings highlight the importance of aligning AI implementation with setting-specific workflows to optimize adoption and impact.

Speaker(s):
Thomas Agresta, MD, MBI
Univ of Connecticut, St. Francis Hospital & Medical Center

Author(s):
Ryan Tran, MHS - University of New England College of Osteopathic Medicine; Thomas Agresta, MD, MBI - Univ of Connecticut, St. Francis Hospital & Medical Center; Christiane Pimentel, BS - University of CT School of Medicine; Cameron Morosky, MS - Hartford Healthcare;

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Changes in Vendor-Agnostic EHR Use Measures through a Major EHR Vendor Transition

Presentation Type: Oral Presentation - Regular
Presentation Time: 04:18 PM - 04:30 PM

Abstract Keywords: Clinician Well-Being, Change Management, Human Factors and Usability, Innovation Partnerships, Implementation Science, and Learning Health Systems, Leadership and Strategy, Outcomes Improvement and Equity, Workforce Automation, Communication, and Workflow Efficiency, Quality Informatics and Lean
Primary Track: Advancing Wellness for Providers and Community with Consideration of Human Factors


We examined clinician EHR use before and after a major EHR vendor transition by developing novel vendor-agnostic EHR use measures comparable across systems. We quantified EHR time during and outside scheduled hours from EHR platforms. Primary care clinicians spent more time on the EHR during scheduled hours, less outside scheduled hours, and showed an overall reduction in total EHR time after transition. These vendor-agnostic measures may help identify EHR optimization opportunities and support burnout-mitigation efforts.

Speaker(s):
Amrita Sinha, MD
Boston Children's Hospital

Author(s):
Amrita Sinha, MD - Boston Children's Hospital; Nate Apathy, PhD - University of Maryland; Daniel Tawfik, MD, MS - Stanford University School of Medicine; Jonathan Hron, MD - Boston Children's Hospital; Chase Parsons, DO, MBI - Boston Children's Hospital;

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EHR Use Metrics and Subjective User Experience in the Federal EHR

Presentation Type: Oral Presentation - Regular
Presentation Time: 04:30 PM - 04:42 PM

Abstract Keywords: Human Factors and Usability, Clinician Well-Being, Education and Training
Primary Track: Advancing Wellness for Providers and Community with Consideration of Human Factors


We examined relationships between subjective EHR user experience and objective EHR use metrics by linking Net EHR Experience Score (NEES) survey responses from 1,361 VA clinicians with 14 LightsOn metadata measures. Few metrics consistently correlated with user experience; however, total transaction wait time and login response time were strongly associated with several NEES items related to performance and efficiency. Findings suggest limited utility of EHR metadata for inferring overall user experience.

Speaker(s):
Nate Apathy, PhD
University of Maryland

Author(s):
Nate Apathy, PhD - University of Maryland; Ryan Sterling, PhD - VA Puget Sound Health Care System; Shaina Coogan, MPH - VA Puget Sound Health Care System; Courtney Bilodeau, MPH RDN - VA Bedford Healthcare System; Edwin Wong, PhD - VA Puget Sound Health Care System; Seppo Rinne, MD, PhD - Dartmouth College & VA Bedford Healthcare System;

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