Work

Algorithmic Identification of the Daily Primary Frontline Clinician during Inpatient Medicine Encounters Using EHR Audit Log Data

Year: 2026

Type: article

Abstract: Background Interprofessional teams are central to high quality patient care. However, identifying the clinician primarily responsible for a patient requires labor-intensive methodologies. Although electronic health record (EHR) audit logs offer a scalable alternative, its use for identifying frontline clinicians is underdeveloped. Objective To develop and validate an algorithm utilizing EHR audit logs to identify the primary frontline clinician per patient day of an encounter and to describe care continuity patterns. Method This was a cross-sectional cohort study of adult inpatient medicine encounters at 12 hospitals in a single health system using a shared EHR. Admissions from February 1, 2023–April 30, 2023, with length of stay of at least 3 days and without an intensive care unit admission were included. Four algorithm iterations were designed to identify the attending physician, resident, or advanced practice provider primarily responsible for patient care on each patient-day. Performance of each algorithm was compared with manual chart review on 1,401 patient-days from 246 randomly sampled patient encounters. Accuracy between an algorithm and the chart review standard was compared using McNemar’s test with Bonferroni adjusted p-values. Results The best performing algorithm correctly identified the primary clinician responsible for patient care on 91% of patient-days (1,268/1,401), outperforming the naïve approach using frequency of actions (78% accuracy, 1,098/1,401, p<0.001). Algorithm errors were attributable to misidentified specialty and ambiguity on days with transitions of care or shared responsibilities between clinicians. The best performing algorithm was applied to the entire cohort (5,801 encounters and 34,001 patient-days) where it identified attending physicians, resident physicians, and APPs as the frontline clinician for 26,750 (79%), 3,106 (9%), and 4,145 (12%) of patient days respectively. Each encounter had a median of 1 (IQR 0-2) handoff between frontline clinicians. Conclusions We developed a scalable, audit log-based algorithm to determine the front-line clinician with excellent accuracy compared with manual chart review.

Source: Applied Clinical Informatics

DOI: 10.1055/a-2903-0478