Conversational AI facilitates mental health assessments and is associated with improved recovery rates

Abstract

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 while 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 with referral, triage, and clinical assessment of mild-to-moderate adult mental illness. Assessing this solution in the context of England’s National Health Service (NHS) Talking Therapies services, we demonstrate in a cohort study design that deploying such an AI solution is associated with improved recovery rates. We find that those NHS Talking Therapies services that introduced the conversational AI solution improved their recovery rates, while comparable NHS Talking Therapies 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 solution 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. For transparency, the authors of this paper declare our conflict of interest as employees and shareholders of Limbic Access, the AI solution referred to in this paper. Data available at a dedicated GitHub repository. Code and data supporting this study are available at a dedicated GitHub repository.