Mental health services across the globe are overburdened due to increased patient need for psychological therapies and a shortage of qualified mental health practitioners. This is unlikely to change in the short-to-medium term. Digital support is urgently needed to facilitate access to mental healthcare whilst creating efficiencies in service delivery. In this paper, we evaluate the use of a conversational artificial intelligence (AI) solution (Limbic Access) to assist both patients and mental health practitioners around referral, triage, and clinical assessment of mild-to-moderate adult mental illness. Assessing this solution in the context of England’s NHS Improving Access to Psychological Therapies (IAPT) services, we demonstrate that deploying such an AI solution is associated with improved recovery rates. We find that those IAPT services that introduced the conversational AI solution improved their recovery rates, while comparable IAPT services across the country reported deteriorating recovery rates during the same time period. Further, we provide an economic analysis indicating that the usage of this AI tool can be highly cost-effective relative to other methods of improving recovery rates. Together, these results highlight the potential of AI solutions to support mental health services in the delivery of quality care in the context of worsening workforce supply and system overburdening.

BACKGROUND Most mental health service providers face the challenge of increasing demand in the absence of increases in funding or staffing. To overcome this supply-demand imbalance, providers need to increase efficiencies to cope with the demand. OBJECTIVE Here, we test whether artificial intelligence (AI) enabled solutions can enable mental health practitioners to use their time more efficiently, and thus reduce strain on the service and improve patient outcomes. METHODS In this study, we focus on the usage of an AI solution (Limbic Access) in the referral and assessment process in UK’s national health service (NHS) first-line psychotherapy service. Data was collected from 9 Improving Access to Psychological Therapies (IAPT) services across England from 64,862 patients. RESULTS We show that the use of this AI solution improves clinical efficiency by reducing the time clinicians spend on mental health assessments. Furthermore, we find improved outcomes for patients using the AI solution in a number of key metrics, such as reduced wait times, re- duced dropout rates, improved allocation to accurate treatment pathways and, most importantly, improved recovery rates. When investigating the mechanism by which the AI solution achieved these improvements, we find that the provision of clinically relevant information ahead of a clinical assessment was critical for these observed effects. CONCLUSIONS Our results emphasise the utility of using AI solutions to support the mental health workforce and highlight that AI solutions can increase efficiencies and in parallel improve mental healthcare for patients.