Using Conversational AI to Facilitate Mental Health Assessments and Improve Clinical Efficiency Within Psychotherapy Services: Real-World Observational Study

Abstract

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.

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

“…Given that there are many intermediate steps that influence treatment outcomes, it is remarkable that we could reliably find highly significant effects on recovery rates. Interestingly, a companion study indicated that the effect is driven by a direct effect through an improvement of the assessment process 30. Ultimately, a randomised controlled trial would be the gold standard for conclusively validating the observed effects and investigating further which mechanisms drive these benefits.…”

“…In,31 it was shown that the AI-enabled self-referral tool increases access to mental healthcare, especially for underserved minority groups. Whereas in30 it was shown that in addition to the impact on recovery rates, the tool reduced assessment times, wait times and dropout rates.…”

Generative AI-Enabled Therapy Support Tool Improves Clinical Outcomes and Patient Engagement in NHS Talking Therapies

“…We compared pre-treatment data for patients in both groups to ensure that they did not differ based on demographic characteristics (age, gender, ethnicity and sexual orientation), wait time to treatment, and depression and anxiety baseline symptom scores. In addition, recognising that different referral methods can influence clinical outcomes, as illustrated by the AI-enabled self-referral tool increasing treatment success (Rollwage et al, 2023(Rollwage et al, , 2024, we also compared the proportion of individuals who used this tool between groups. The chi-square test was applied for categorical outcomes and the independent student t-test was used for continuous outcomes.…”