Using Machine Learning to Match Clients and Therapy Providers: Evaluating Clinical Quality and Cost of Care

Objectives: Matching clients in need of mental healthcare with providers who will deliver high quality treatment presents a substantial challenge. Machine learning models hold potential for predicting the best pairings from a multitude of data points, leveraging relevant characteristics to recommend providers. Methods: Propensity score matching was used to match individuals who searched for a psychotherapy providers using either a pragmatic algorithm (leveraging logistical and clinical relevance features) or a value-based algorithm (adding provider-specific clinical outcomes and cost features). Postmatching cohorts included on average 1677 pairs with clinically elevated symptoms of anxiety. Symptom improvement from before to after treatment was calculated. Total costs of care were compared between algorithm cohorts. Results: After matching, participants were on an average of 34 years of age, 54% to 55% White, and 63% to 66% female. Mean level of anxiety symptom change from before to after treatment was statistically significant for both groups (Pragmatic: −5.82; Value based: −5.57, P < .001) with large effect sizes. People searching for therapy providers with either algorithm had similar rates of reliable improvement or recovery in anxiety (Pragmatic: 71.74%, Value based: 70.02%). Participants using the Value-based care algorithm group had 20% lower total cost of care, using 2.08 fewer therapy sessions. Depression outcomes were similar to those for anxiety and thus are presented in the Supplemental Materials. Conclusions: Results indicate that a value-based machine learning matching algorithm integrating historical provider performance and cost metrics may result in better provider-client pairings that reduce the total cost of care with no effect on outcomes. Further research is needed to establish the generalizability of these algorithms.