Dropout from psychotherapy is common, often occurring early in treatment, but predicting which patients will leave care remains difficult. In this retrospective cohort study of 31,146 patients receiving individual outpatient therapy through an employer-sponsored digital mental health platform, we examined whether language from first-session transcripts could be used to predict subsequent dropout (failure to attend a fourth session within 1 year of the first session). We combined clinical, demographic, and utilization variables with features derived from transcripts using natural language processing and large language models.
Models incorporating transcript-derived features improved prediction compared with pre-session data alone (area under the curve, 0.642 vs 0.569). Provider behaviors, particularly whether follow-up care was clearly discussed, were strongly associated with retention, while patient-level constructs such as motivation, ambivalence, and therapeutic alliance were not consistently predictive. Although overall discrimination was modest, transcript-derived features improved identification of high-risk patients.
These findings suggest that early-session dynamics contain actionable signals of engagement and may support scalable approaches to improving retention in mental health care. The authors would like to thank Sahar Mirzaee for help with the data pipeline, and Namrata Doshi, Regina Ramos, and Emma LeClair for their early feedback on this project. Spring Health, Health Economics and Outcomes Research, New York, NY, USA Emily J.
Dworkin, Matt Hawrilenko, Millard Brown & Adam M. Chekroud Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA Financial and Non-Financial Interest: E.J.W., E.R.D., M.H., M.B. and A.M.C. reported being employed by and holding equity in Spring Care Inc outside the submitted work. E.R.D. also reported receiving consulting fees from Flourish Science Inc. and holding equity in August Research Services, LLC.
A.M.C. reported holding equity in Carbon Health Technologies Inc, Wheel Health Inc, Parallel Technologies Inc, Healthie Inc, and UnitedHealthcare; receiving consulting fees from Fortress; and providing unpaid advisory services to health care technology startups outside the submitted work. Pending Patents: A.M.C. is the lead inventor on three patent submissions relating to treatment for major depressive disorder (US Patent and Trademark Office number Y0087.70116US00 and provisional application numbers 62/491 660 and 62/629 041). No other disclosures were reported.
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