Key result
EMBRACE deep learning framework detects and explains physician burnout levels using wearable sensor data.
Why the study?
Existing wearable and machine learning models for predicting resident physician burnout fail to clinically explain their predictions, undermining trustworthiness and clinical utility.
The EMBRACE framework offers a clinically explainable approach to predicting resident physician burnout using wearable sensors and deep multi-task learning.
Hypothesis-generating for wearable-based burnout detection in physicians; requires prospective validation before any clinical use.
Medical residency is associated with long working hours, demanding schedules, and high stress levels, which can lead to burnout among resident physicians. Although wearable and machine learning-based interventions can be useful in predicting potential burnout, existing models fail to clinically explain their predictions, thereby undermining the trustworthiness of the research findings and rendering the intervention apparently useless to residents. This paper develops, EMBRACE, E xplainable M ultitask B urnout p R ediction using A daptiv E deep learning, that employs a novel framework for predicting burnout that is clinically explainable. At first, we develop, a wearable sensor based improved workplace activity and stress detection algorithm, using deep multi-task learning. Next, we present a novel Adaptive Multi-Task Learning (MTL) framework built on top of our activity and stress detection algorithm, to automatically detect burnout. Additionally, this model also completes the resident burnout survey automatically such a way that it can clinically estimate the same burnout level i.e., clinically explainable and trustworthy estimation. We evaluated the efficacy and explainability of EMBRACE using a real-time data collected from 28 resident physicians (2-7 days each) with appropriate IRB approval (IRB# 2021-017).
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Alam et al. (2023) studied Burnout (n=28). EMBRACE (Explainable Multitask Burnout Prediction using Adaptive Deep Learning) was evaluated on Burnout detection and clinical explainability. The EMBRACE adaptive deep learning framework was developed to automatically detect and clinically explain burnout levels using wearable sensor data collected from 28 resident physicians.
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