Accepted for/Published in: JMIR Formative Research
Date Submitted: Mar 21, 2022
Date Accepted: Sep 7, 2022
Optimizing Health Coaching for Patients with Type 2 Diabetes Using Machine Learning: A Pilot Study
ABSTRACT
Background:
Health coaching is an emerging intervention that has been shown to improve clinical and patient relevant outcomes for type 2 diabetes. Advances in artificial intelligence may provide an avenue for developing more personalized, adaptive, and cost-effective approach to diabetes health coaching.
Objective:
We aim to apply Q-learning, a widely used reinforcement learning algorithm, to a diabetes health coaching dataset to develop a model for recommending an optimal coaching intervention at each decision point that is tailored to a patient’s accumulated history.
Methods:
In this pilot study, we fit a two-stage reinforcement learning model on the 177 patients from the intervention arm of a community-based randomized controlled trial conducted in Canada. The policy produced by the reinforcement learning model can recommend a coaching intervention at each decision point that is tailored to a patient’s accumulated history and is expected to maximize composite clinical outcome of A1C reduction and quality of life improvement (normalized to [0,1], with a higher score being better). Our data, models and source code are publicly available.
Results:
Among the 177 patients, the coaching intervention recommended by our policy mirrored the observed diabetes health coach’s interventions in 17.51% of the patients in stage 1 and 14.12% of the patients in stage 2. Where there was agreement in both stages, the average cumulative composite outcome (0.839, 95% CI: [0.460, 1.220]) was better than those for whom the optimal policy agreed with the diabetes health coach in only one stage (0.791, 95% CI: [0.747, 0.836]) or differed in both stages (0.755, 95% CI: [0.728, 0.781]). Additionally, the average cumulative composite outcome predicted for the policy’s recommendations was significantly better than that of the observed diabetes health coach’s recommendations (paired t-statistic = 10.040, p<0.001).
Conclusions:
Applying reinforcement learning to diabetes health coaching could allow for both the automation of health coaching and an improvement in health outcomes produced by this type of intervention.
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