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Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot

Nature Medicine volume 30, pages 595–602 (2024)Cite this article

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Abstract

Inequality in treatment access is a pressing issue in most healthcare systems across many medical disciplines. In mental healthcare, reduced treatment access for minorities is ubiquitous but remedies are sparse. Here we demonstrate that digital tools can reduce the accessibility gap by addressing several key barriers. In a multisite observational study of 129,400 patients within England’s NHS services, we evaluated the impact of a personalized artificial intelligence-enabled self-referral chatbot on patient referral volume and diversity in ethnicity, gender and sexual orientation. We found that services that used this digital solution identified substantially increased referrals (15% increase versus 6% increase in control services). Critically, this increase was particularly pronounced in minorities, such as nonbinary (179% increase) and ethnic minority individuals (29% increase). Using natural language processing to analyze qualitative feedback from 42,332 individuals, we found that the chatbot’s human-free nature and the patients’ self-realization of their need for treatment were potential drivers for the observed improvement in the diversity of access. This provides strong evidence that digital tools may help overcome the pervasive inequality in mental healthcare.

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Fig. 1: The total number of referrals pre- and postimplementation of the personalized self-referral chatbot.
Fig. 2: Percentage change for sociodemographic groups for services implementing the personalized self-referral chatbot (pink) and the matched control services (gray).
Fig. 3: Feedback themes from minority (pink bars) and majority (gray bars) groups.
Fig. 4: Illustrations of the personalized self-referral chatbot on the NHS Talking Therapies website.
Fig. 5: The study design and treatment pathway in NHS Talking Therapies services.

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Data availability

The referral data is publicly available on the NHS Digital database. The specific data used for the referral analysis is available in a dedicated GitHub repository. The qualitative feedback data will not be publicly available because the individuals did not provide explicit consent for the public sharing of this feedback data.

Code availability

The code used for the analyses is available at a dedicated GitHub repository.

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Acknowledgements

This work was supported by Limbic Limited. We thank E. Ivanova for her contribution to the figures and illustrations and S. Pisupati for reviewing the analysis code.

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Authors and Affiliations

  1. Limbic, London, UK

    Johanna Habicht, Sruthi Viswanathan, Ben Carrington, Tobias U. Hauser, Ross Harper & Max Rollwage

  2. Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK

    Tobias U. Hauser

  3. Department of Psychiatry and Psychotherapy, Medical School and University Hospital, Eberhard Karls University of Tübingen, Tübingen, Germany

    Tobias U. Hauser

  4. German Center for Mental Health (DZPG), Tübingen, Germany

    Tobias U. Hauser

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Contributions

J.H. and M.R. conceived and planned the study with input from B.C. and R.H. J.H. analyzed the data with input from M.R. and T.U.H. S.V. performed the thematic analysis, which was further discussed with J.H. and M.R.; M.R. and J.H. developed the NLP model. J.H. wrote the first draft of the manuscript with edits from all other authors.

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Correspondence to Johanna Habicht.

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Competing interests

J.H., S.V., B.C., R.H. and M.R. are employed by Limbic Limited and hold shares in the company. T.U.H. is working as a paid consultant for Limbic Limited and holds shares in the company.

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Nature Medicine thanks Stefan Rennick-Egglestone and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Lorenzo Righetto, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Percentage change for different sexuality groups for services implementing the personalized self-referral chatbot (pink) and the matched control services (gray).

No difference between the percentage change in referrals for services using the chatbot compared to the control services was observed for non-binary individuals (OR = 1.12, CI = [0.998, 1.251], p = 0.053) or gay and lesbians (OR = 1.01, CI = [0.838, 1.228], p = 0.882). There was a significant increase in heterosexual individuals in services using the chatbot compared to matched services (OR = 1.05, CI = [1.026, 1.084], p < 0.001). We did not find any difference between the referrals from bisexual and heterosexual individuals between services (interaction term: OR = 1.06, CI = [0.944,1.190], p = 0.326), or between gay/lesbian and heterosexual individuals (OR = 0.96, CI = [0.793,1.167], p = 0.695). ***p < 0.001, n.s. p > 0.05.

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Habicht, J., Viswanathan, S., Carrington, B. et al. Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot. Nat Med 30, 595–602 (2024). https://doi.org/10.1038/s41591-023-02766-x

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