Predicting dropout from therapy based on first-session transcripts

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.