Predictors of dropout from psychotherapy in community settings: a large-scale electronic health records study

Importance: Psychotherapy dropout poses a significant public health problem, predicting depression relapse and illness persistence. Identifying who is at risk of psychotherapy dropout can guide personalized and scalable strategies to mitigate risk and improve outcomes. Objective: We estimated the prevalence and predictors of psychotherapy dropout in a large community setting. Design: In this prognostic study, we analyzed electronic health record data collected between 2008-2022. We trained classifiers using logistic regression (LR) and random forest machine learning (RF) to identify predictors of dropout.Setting: Two large academic medical centers, 6 community hospitals, and their affiliated outpatient networks in Massachusetts. Participants and Exposures: Out of 423,636 individuals with depression, we included 40,732 patients (aged 18-80) who had ≥1 individual psychotherapy session and ≥1 any documented visit in the year before and after the session.Main Outcomes and Measures: Predictors included demographics, medical conditions, medical/psychiatric services (e.g. prescription, diagnostic, procedural codes). We assessed the number of individuals who stopped psychotherapy after ≤3 sessions. We trained LR and RF models to maximize predictive accuracy (Area Under the Curve; AUC) and extracted top predictors of dropout. Results: Psychotherapy dropout rate was 28.4% (n=11,571). AUC values were 0.64 for LR (95% CI: 0.63 - 0.65) and 0.66 for RF (95% CI: 0.65 - 0.67). A prior mental health encounter (e.g. group psychotherapy, psychiatric evaluation) and white and/or non-Hispanic self-identified background predicted decreased dropout likelihood. A mental disorder due to a physiological condition and a prior medical admission predicted increased likelihood of dropout.Conclusions and Relevance: Nearly a third of patients who begin psychotherapy drop out, underscoring the need for rapid and scalable risk detection and mitigation strategies. Brief mental health encounters may protect against dropout, whereas psychotherapy referrals during or following medical admissions may increase risk. Integrated care models with brief interventions embedded in medical settings may reduce dropout risk. Brief risk detection based on readily available EHR data can inform the likelihood of psychotherapy dropout and guide clinical recommendations. Together, our findings can inform targeted interventions to reduce risk of psychotherapy dropout among vulnerable populations in the community.