ArticleApplied clinical informatics2026
Algorithmic Identification of the Daily Primary Frontline Clinician during Inpatient Medicine Encounters Using EHR Audit Log Data.
Article in Applied clinical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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7 authors.
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Abstract
Background: Interprofessional teams are central to high-quality patient care. However, identifying the daily frontline clinician who performs direct care-related tasks for a patient requires labor-intensive methodologies. Although electronic health record (EHR) audit logs offer a scalable alternative, their use for identifying frontline clinicians is underdeveloped. Objectives: This study aimed to develop and validate an algorithm utilizing EHR audit logs to identify the daily frontline clinician per patient-day of an inpatient medicine encounter. Methods: This was a cross-sectional cohort study of adult inpatient medicine encounters across 12 hospitals in one health system using a shared EHR. Admissions between February 1, 2023, and April 30, 2023, with a length of stay of at least 3 days and without an intensive care unit (ICU) admission were included. Four algorithm iterations were designed to identify the attending physician, resident, or advanced practice provider (APP) serving as the daily frontline clinician. The performance of each algorithm was compared with manual chart review on 1,401 patient-days from 246 randomly sampled patient encounters. Algorithm accuracy versus chart review was compared using McNemar's test with Bonferroni-adjusted Results: The best-performing algorithm correctly identified the daily frontline clinician on 91% of patient-days (1,268/1,401), outperforming algorithm 1, which selected the clinician who performed the most actions (78% accuracy, 1,098/1,401, Conclusion: We developed a scalable, audit log-based algorithm to determine the daily frontline clinician with high accuracy compared with manual chart review.
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