Closing the accessibility gap to mental health treatment with a conversational AI-enabled self-referral tool

Accessing mental health care can be challenging, and minority groups often face additional barriers. This study investigates whether digital tools can enhance equality of access to mental health treatment. We evaluated a novel AI-enabled self-referral tool (a chatbot) designed to make entry to mental health treatment more accessible in a real-world setting. In a multi-site observational study, data were collected from 129,400 patients who referred to 28 separate NHS Talking Therapies services across England. Our results indicate that the tool led to a 15% increase in total referrals, which was significantly larger than the 6% baseline increase observed in matched services using traditional self-referral methods during the same time period. Importantly, the tool was particularly effective for minority groups, which included non-binary (235% increase), bisexual (30% increase), and ethnic minority individuals (31% increase). This paints a promising picture for the use of AI chatbots in mental healthcare and suggests they may be especially beneficial for demographic groups that experience barriers to accessing treatment in the traditional care systems. To better understand the reasons for this disproportional benefit for minority groups, we used thematic analysis and Natural Language Processing (NLP) models to evaluate qualitative feedback from 42,332 individuals who referred through the AI-enabled tool. We found that the tool’s human-free nature and its ability to improve the perceived need for treatment were the main drivers for improved diversity. These findings suggest that AI-enabled chatbots have the potential to increase accessibility to mental health services for all, and to alleviate barriers faced by disadvantaged populations. The results have important implications for healthcare policy, clinical practice, and technology development. ### Competing Interest Statement JH, SV, BC, RH and MR are employed by Limbic Limited and hold shares in the company. TH is working as a paid consultant for Limbic Limited. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The data used in this study are recorded in routine practice by all NHS Talking Therapies services and are reported to the public monthly, quarterly, and annually by NHS Digital: . I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Code and data in the present study are available upon reasonable request to the authors. The qualitative feedback data will not be available because the information could compromise participants' privacy/consent.