Using conversational AI to facilitate mental health assessments and improve clinical efficiencies within psychotherapy services in a large real-world dataset (Preprint)

Background Most mental health care providers face the challenge of increased demand for psychotherapy in the absence of increased funding or staffing. To overcome this supply-demand imbalance, care providers must increase the efficiency of service delivery. Objective In this study, we examined whether artificial intelligence (AI)–enabled digital solutions can help mental health care practitioners to use their time more efficiently, and thus reduce strain on services and improve patient outcomes. Methods In this study, we focused on the use of an AI solution (Limbic Access) to support initial patient referral and clinical assessment within the UK’s National Health Service. Data were collected from 9 Talking Therapies services across England, comprising 64,862 patients. Results We showed that the use of this AI solution improves clinical efficiency by reducing the time clinicians spend on mental health assessments. Furthermore, we found improved outcomes for patients using the AI solution in several key metrics, such as reduced wait times, reduced dropout rates, improved allocation to appropriate treatment pathways, and, most importantly, improved recovery rates. When investigating the mechanism by which the AI solution achieved these improvements, we found that the provision of clinically relevant information ahead of clinical assessment was critical for these observed effects. Conclusions Our results emphasize the utility of using AI solutions to support the mental health workforce, further highlighting the potential of AI solutions to increase the efficiency of care delivery and improve clinical outcomes for patients.