Algorithmic Identification of the Daily Primary Frontline Clinician during Inpatient Medicine Encounters Using EHR...

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.

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.

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 p-values.

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, p < 0.001). Algorithm errors were attributable to misidentified specialties 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 daily frontline clinicians for 26,750 (79%), 3,106 (9%), and 4,145 (12%) of patient-days, respectively. Encounters had a median of 1 (interquartile range [IQR]: 0-2) handoff between frontline clinicians.

We developed a scalable, audit log-based algorithm to determine the daily frontline clinician with high accuracy compared with manual chart review.