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

Journal: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Published Date: Jun 26, 2025

Abstract

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.

Authors

  • Jennifer L Lee
    Lyra Health, Burlingame, CA, USA; Department of Pediatrics, Emory University, Atlanta, GA, USA. Electronic address: lee.jennifer.phd@gmail.com.
  • Chris Billovits
    Lyra Health, Burlingame, CA, USA.
  • Shih-Yin Chen
    Lyra Health, Burlingame, CA, USA.
  • Robert E Wickham
    Department of Psychological Sciences, Northern Arizona University, Flagstaff, AZ, USA.
  • Bob Kocher
    Department of Health Policy, Stanford University, Palo Alto, CA, USA; Lyra Clinical Associates, Burlingame, CA, USA.
  • Connie E Chen
    Lyra Health, Burlingame, CA, USA.
  • Anita Lungu
    Lyra Health, Burlingame, CA, USA.

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