EHR Use Patterns Linked to Physician Efficiency and Productivity
Publication Title: Electronic health record use factors linked to efficiency and productivity: an explainable machine learning analysis
Summary
- Question
- This study investigated how electronic health record (EHR) usage patterns among ambulatory care physicians relate to two key measures of efficiency: the proportion of same-day chart completions and the daily volume of patient visits. Using machine learning (ML) techniques, the researchers aimed to identify specific EHR use behaviors associated with higher efficiency and productivity, providing actionable thresholds to guide improvements.
- Why it Matters
- EHR systems are integral to modern healthcare but often contribute to physician burnout due to excessive documentation and after-hours work. Identifying specific EHR usage patterns that enhance efficiency can help reduce this burden, improve physician well-being, and optimize patient care. These findings have implications for healthcare administrators, policymakers, and providers seeking to create balanced workflows and improve care delivery.
- Methods
- The study analyzed de-identified monthly EHR use data from over 218,000 physicians across 413 U.S. healthcare organizations between May 2019 and April 2022. Using ML models, the researchers examined relationships between EHR use behaviors and two outcomes: same-day chart completion efficiency and daily visit volume. They identified key EHR usage factors and established thresholds associated with high performance.
- Key Findings
- Physicians who responded to inbox messages within 1.5 days and limited after-hours documentation to less than 25 minutes per day were more likely to complete patient charts on the same day. For higher patient visit volumes, key factors included spending less than 4.1 minutes on EHR tasks outside scheduled hours and less than 3.2 minutes reviewing patient records per visit. These patterns highlight how timely inbox management and minimizing after-hours work are linked to improved efficiency and productivity.
- Implications
- The findings suggest that targeted interventions, such as improved inbox management tools, team-based documentation support, and strategies to reduce after-hours EHR use, could enhance physician efficiency and reduce burnout. By focusing on specific thresholds for EHR use, healthcare organizations can implement tailored strategies to improve operational performance and physician well-being, ultimately benefiting patient care.
- Next Steps
- Future research should evaluate the effectiveness of interventions like scribe programs, automated inbox tools, and ambient listening technologies in improving efficiency and productivity. Studies could also explore these findings in other healthcare settings, such as inpatient or emergency care, and investigate the impact of EHR use patterns on long-term physician well-being and patient outcomes.
- Funding Information
- This research was supported by an American Medical Association Practice Transformation Initiative grant and the Gruber Foundation science fellowship. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Yale University also provided funding and support for this research.
Full Citation
Li H, Khanna V, Apathy N, Holmgren A, Loza A, Melnick E. Electronic health record use factors linked to efficiency and productivity: an explainable machine learning analysis. JAMIA Open 2026, 9: ooag018. PMID: 41767181, PMCID: PMC12936052, DOI: 10.1093/jamiaopen/ooag018.
This AI-assisted summary has been reviewed and approved by at least one of the study's authors to ensure it accurately reflects the research.
Authors
Huan Li
First AuthorTed Melnick, MD, MHS
Last AuthorAssociate Professor of Emergency Medicine and of Biostatistics (Health Informatics)
Additional Yale School of Medicine Authors
Other Authors
Research Themes
Concepts
- Electronic health records;
- EHR time;
- Visit volume;
- Physician efficiency;
- Patient visit volume;
- Longitudinal cohort study;
- Inbox management;
- Chart completion;
- Unique physicians;
- Health records;
- Patient care;
- Secondary analysis;
- Cohort study;
- Physicians;
- Work environment;
- Efficient working environment;
- US organizations;
- Quintile;
- Care;
- Machine Learning Classifiers;
- Specialty;
- Visits;
- Intervention;
- Lt;25;
- Use factors