A large-scale evaluation of provider-patient matching in an employer-sponsored mental health program.

Many patients do not improve during their first round of psychotherapy, partly due to variability in the quality of the patient-provider relationship. Matching patients to providers based on providers' historical performance with similar patients offers a proactive, data-driven solution. In a real-world retrospective cohort study, 24,303 participants using a mental health benefit (Spring Health) from 2021-2024 chose either matched therapists (i.e., based on providers' continuity of care, their historical patient-improvement percentile, and clinical alignment) or self-selected a therapist from a general list. Patients with depression using matched rather than self-selected therapists improved 8.5% faster (b = 0.12-point reduction on PHQ-9 per log-day, [95% CI: -0.15 to -0.09], p < 0.001) and were more likely to achieve reliable change (OR = 1.09 [1.05-1.14], p < 0.001), with similar results for anxiety and PTSD. Higher patient-provider fit scores predicted stronger therapeutic alliance (b = 0.05 [0.04-0.06], p < 0.001). Matching also reduced the total cost of care by 12.9% per reliable change and 10.8% per recovery. Thus, a data-driven patient-provider matching algorithm that is informed by providers' historical records with similar patients can provide modest but consistent acceleration of symptom improvement, increase clinical outcomes, and yield cost savings.