Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis

Abstract

Objective: To derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time.

Materials and methods: A cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time.

Results: Five out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time8 was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time8 after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P < .001, unless otherwise indicated): female physicians +0.58 hours; each additional clinical hour scheduled per month -0.01 hours; practicing cardiology -1.30 hours; medical subspecialties -0.89 hours (except gastroenterology, P = .002); neurology/psychiatry -2.60 hours; obstetrics/gynecology -1.88 hours; pediatrics -1.05 hours (P = .001); sports/physical medicine and rehabilitation -3.25 hours; and surgical specialties -3.65 hours.

Conclusions: For every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement.

Figure 1.

CONSORT diagram. Flow diagram stratified by health system for participant eligibility and inclusion in the analysis.

Figure 2.

Normalized EHR core measures by specialty. Distribution of EHR core measures stratified by medical specialty and health system (Yale-New Haven on left and MedStar on right) and with each institution’s median value noted with dotted coral pink line. Note that the metrics are not sufficiently aligned between Cerner and Epic to allow direct comparisons.

Figure 3.

Univariate associations between normalized EHR core measures by specialty and health system. Scatterplot matrix of several pertinent EHR core metrics in both health systems^a with regression line and Pearson’s correlation coefficient between each measure. All units are in hours except TW_ORD which is reported as percentages. ^aMedStar WOW₈ was deemed less reliable.