JMIR Publications

ABSTRACT

Background:

Addressing the burnout epidemic among health care providers remains one of the foremost challenges of modern health care systems. Past researchers have turned to passively gathered biometric data from wearable devices to identify key variables that predict burnout. However, this has resulted in limited success.

Objective:

This study aims to test the feasibility and accuracy of using a machine learning approach to classifying health care providers as at higher or lower risk of burnout based upon biometric data.

Methods:

We conducted a 3-month study where health care providers self-reported burnout every 6 weeks and wore a Garmin Venu 3 smartwatch to collect continuous biometric data. Participants were recruited from 3 pediatric urgent care sites and an inpatient hospitalist division of a pediatric health care system. We had a final sample of 41 participants who met our inclusion criteria requiring completion of burnout measurements at two of the three timepoints and having worn the watch for at least 70% of the study period. Participants were asked to wear the Garmin watch continuously over the course of the study. Burnout self-report surveys were delivered via email. Our main outcome was burnout as measured by the Maslach Burnout Inventory (MBI-HSS). The measure consists of three subscales that together constitute burnout: emotional exhaustion, depersonalization, and reduced personal efficacy.

Results:

First, we found evidence of feasibility for wearable watches to be used in situ for health care providers to gather data passively for the purposes of classifying burnout. 73% of participants met our criteria of wearing the watch 70% of the time. Second, our machine learning model achieved strong overall performance in identifying risk of burnout with strong sensitivity and specificity, demonstrating that biometric data collected by wearable devices can accurately predict burnout.

Conclusions:

Our evidence of feasibility offers promising results for a passive method of data collection to be used for classifying burnout risk in other health care settings. Further, the strong performance of our machine learning model provides a foundational step towards improving an organization’s ability to respond and mitigate burnout through thoughtful intervention strategies.