Temporal Associations Between EHR-Derived Workload, Burnout, and Errors: a Prospective Cohort Study

Abstract

Background: The temporal progression and workload-related causal contributors to physician burnout are not well-understood.

Objective: To characterize burnout's time course and evaluate the effect of time-varying workload on burnout and medical errors.

Design: Six-month longitudinal cohort study with measurements of burnout, workload, and wrong-patient orders every 4 weeks.

Participants: Seventy-five intern physicians in internal medicine, pediatrics, and anesthesiology at a large academic medical center.

Main measures: Burnout was measured using the Professional Fulfillment Index survey. Workload was collected from electronic health record (EHR) audit logs and summarized as follows: total time spent on the EHR, after-hours EHR time, patient load, inbox time, chart review time, note-writing time, and number of orders. Wrong-patient orders were assessed using retract-and-reorder events.

Key results: Seventy-five of 104 interns enrolled (72.1%) in the study. A total of 337 surveys and 8,863,318 EHR-based actions were analyzed. Median burnout score across the cohort across all time points was 1.2 (IQR 0.7-1.7). Individual-level burnout was variable (median monthly change 0.3, IQR 0.1-0.6). In multivariable analysis, increased total EHR time (β=0.121 for an increase from 54.5 h per month (25th percentile) to 123.0 h per month (75th percentile), 95%CI=0.016-0.226), increased patient load (β=0.130 for an increase from 4.9 (25th percentile) to 7.1 (75th percentile) patients per day, 95%CI=0.053-0.207), and increased chart review time (β=0.096 for an increase from 0.39 (25th percentile) to 0.59 (75th percentile) hours per patient per day, 95%CI=0.015-0.177) were associated with an increased burnout score. After adjusting for the total number of ordering sessions, burnout was not statistically associated with an increased rate of wrong-patient orders (rate ratio=1.20, 95%CI=0.76-1.89).

Conclusions: Burnout and recovery were associated with recent clinical workload for a cohort of physician trainees, highlighting the elastic nature of burnout. Wellness interventions should focus on strategies to mitigate sustained elevations of work responsibilities.

Keywords: burnout; electronic health record; graduate medical education; physician wellness; workload.

Figure 1

Overview of study design and data collection. a Conceptual framework for the study. b Illustration of study design. Participants completed one survey each month for 6 months to measure burnout. EHR use was collected in the background. For each survey, EHR use for the preceding month was summarized and used in a multivariable linear mixed-effects model to explain burnout score. c CONSORT diagram of study enrollment and data collection.

Figure 2

Burnout is highly variable over time. a Trajectories of burnout score over time for all participants in the study shown in gray. Median burnout score per survey shown as red dots, with bars representing interquartile range. b Example burnout trajectory and selected EHR workload measures observed for a participant over the course of the 6-month study. Burnout trajectory shown in red with axis on the left. Total EHR time per month shown in dark blue, patient load per day shown in light blue, and chart review time per patient per day shown in gray, with axes on the right. Monthly rotation schedule for this participant shown above the plot.

Figure 3

Multivariable model for burnout score as a function of monthly EHR-derived workload measures. A linear mixed-effects model was used to examine the relationship between EHR-derived workload and burnout score, controlling for repeated measures per participant and the role of specialty and gender. Shown is the estimated effect (dot) and 95%CI (shaded area) of a 25th to 75th percentile change (shown below each variable) in each EHR workload measure. Dotted line indicates zero effect.