Personalised psychotherapy in primary care: evaluation of data-driven treatment allocation to cognitive–behavioural therapy versus counselling for depression

BACKGROUND: Various effective psychotherapies exist for the treatment of depression; however, only approximately half of patients recover after treatment. In efforts to improve clinical outcomes, research has focused on personalised psychotherapy – an attempt to match patients to treatments they are most likely to respond to. AIM: The present research aimed to evaluate the benefit of a data-driven model to support clinical decision-making in differential treatment allocation to cognitive–behavioural therapy versus counselling for depression. METHOD: The present analysis used electronic healthcare records from primary care psychological therapy services for patients receiving cognitive–behavioural therapy (n = 14 544) and counselling for depression (n = 4725). A linear regression with baseline sociodemographic and clinical characteristics was used to differentially predict post-treatment Patient Health Questionnaire (PHQ-9) scores between the two treatments. The benefit of differential prescription was evaluated in a held-out validation sample. RESULTS: On average, patients who received their model-indicated optimal treatment saw a greater improvement (by 1.78 PHQ-9 points). This translated into 4–10% more patients achieving clinically meaningful changes. However, for individual patients, the estimated differences in benefits of treatments were small and rarely met the threshold for minimal clinically important differences. CONCLUSION: Precision prescription of psychotherapy based on sociodemographic and clinical characteristics is unlikely to produce large benefits for individual patients. However, the benefits may be meaningful from an aggregate public health perspective when applied at scale.