# Electronic health record use factors linked to efficiency and productivity: an explainable machine learning analysis

**Authors:** Huan Li, Varada V Khanna, Nate Apathy, A Jay Holmgren, Andrew J Loza, Edward R Melnick

PMC · DOI: 10.1093/jamiaopen/ooag018 · JAMIA Open · 2026-02-25

## TL;DR

This study uses machine learning to identify how physicians' use of electronic health records relates to their efficiency and productivity.

## Contribution

The novel use of explainable machine learning to analyze EHR use patterns and link them to physician efficiency and productivity metrics.

## Key findings

- Physicians with inbox message turnaround under 1.5 days showed higher chart completion efficiency.
- EHR time outside scheduled hours under 4.1 minutes per visit was associated with higher patient visit volumes.
- After-hours documentation under 25 minutes per scheduled day was linked to improved efficiency.

## Abstract

To explore the relationship between ambulatory physician electronic health record (EHR) use characteristics and proxies for physician efficiency.

A longitudinal cohort study was conducted to examine physician-month EHR use metadata in 413 US organizations between May 2019 and April 2022. A multi-model machine learning classifier was developed to predict physician efficiency. The main outcomes of the study were physician efficiency, measured as the proportion of same-day chart completion by specialty, and productivity, measured as daily patient visit volume, both segmented into quintiles.

The study included 218 610 unique physicians with 5 193 385 physician-month observations from 413 organizations with an average chart completion efficiency of 72.9% and 10.8 visits per scheduled day. The primary ML analysis achieved an accuracy of 0.74 in classifying physician-months with high chart completion efficiency and highlighted associations with key features, such as inbox message turnaround time <1.5 days and after-hours documentation <25 min/scheduled day. A secondary analysis achieved an accuracy of 0.84 in classifying physician-months with high visit volumes, indicating that factors such as EHR time outside scheduled hours <4.1 min/visit and clinical review time <3.2 min/visit were associated with higher visit volumes.

Implementing specific EHR use measures with distinct thresholds, such as inbox management and after-hours documentation, could help target interventions to enhance productivity, providing actionable insights to create balanced and efficient work environments that improve patient care and reduce EHR time.

## Full-text entities

- **Genes:** SHROOM4 (shroom family member 4) [NCBI Gene 57477] {aka MRXSSDS, SHAP, shrm4}
- **Diseases:** CDI (MESH:D000075902), infectious disease (MESH:D003141), burnout (MESH:D002055), ML (MESH:C537366), Drug Abuse (MESH:D019966)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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## References

34 references — full list in the complete paper: https://tomesphere.com/paper/PMC12936052/full.md

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Source: https://tomesphere.com/paper/PMC12936052