Electronic Health Record Usage Patterns among Well-being Survey Responders and Non-responders: Longitudinal Observational Study (Preprint)

Abstract

Physician surveys provide indispensable insights into physician experience, but the question of whether responders are representative can limit confidence in conclusions. Ubiquitously-collected electronic health record (EHR) usage data may improve understanding of the experiences of survey non-responders in relation to responders, providing clues regarding their well-being.

To identify EHR usage measures corresponding with physician survey response and examine methods to estimate population-level survey results among physicians.

Longitudinal observational study from 2019 through 2020, among academic and community primary care physicians. We quantified EHR usage using vendor-derived and investigator-derived measures, quantified burnout symptoms using emotional exhaustion and interpersonal disengagement subscales of the Stanford Professional Fulfillment Index, and used an ensemble of response propensity-weighted penalized linear regressions to develop a burnout symptom prediction model.

Among 697 surveys from 477 physicians, always-responders were similar to non-responders in gender [204 (60%) vs 38 (58%) women, P=.22] and age [50 (IQR 40-60) vs. 50 (IQR 37.5-57.5) years, P=.43], but with higher clinical workload [148.5 (IQR 71-232) vs. 39 (IQR 0-116) appointments, P<0.001], efficiency [5.1 (IQR 3.9-6.1) vs. 4.5 (IQR 0-5.7), P<0.001], and proficiency [7.0 (IQR 5.5-8.5) vs. 3.3 (0-6.3), P<0.001]. Survey response status prediction showed an out-of-sample AUROC 0.88 (95%CI 0.77-0.91). Burnout symptom prediction showed an out-of-sample AUROC 0.63 (95%CI 0.57-0.70). Predicted burnout prevalence among non-responders was 52%, higher than the observed prevalence of 28% among responders, resulting in an estimated population burnout prevalence of 31%.

EHR usage measures showed limited utility for predicting burnout symptoms, but allowed discrimination between responders and non-responders. These measures may enable qualitative interpretations of the effects of non-responders and may inform survey response maximization efforts.