Model development for bespoke large language models for digital triage assistance in mental health care.

Autor: Taylor N; Department of Psychiatry, University of Oxford, Oxford, United Kingdom. Electronic address: nialltaylor24@gmail.com., Kormilitzin A; Department of Psychiatry, University of Oxford, Oxford, United Kingdom., Lorge I; Department of Psychiatry, University of Oxford, Oxford, United Kingdom., Nevado-Holgado A; Department of Psychiatry, University of Oxford, Oxford, United Kingdom., Cipriani A; Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical research Centre, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom., Joyce DW; Department of Primary Care and Mental Health, University of Liverpool, Liverpool, United Kingdom; Civic Health Innovation Labs, University of Liverpool, Liverpool, United Kingdom; Mental health Research for Innovation Centre (M-RIC), Mersey Care NHS Foundation Trust, Prescot, Merseyside, United Kingdom.
Jazyk: angličtina
Zdroj: Artificial intelligence in medicine [Artif Intell Med] 2024 Nov; Vol. 157, pp. 102988. Date of Electronic Publication: 2024 Sep 29.
DOI: 10.1016/j.artmed.2024.102988
Abstrakt: Contemporary large language models (LLMs) may have utility for processing unstructured, narrative free-text clinical data contained in electronic health records (EHRs) - a particularly important use-case for mental health where a majority of routinely-collected patient data lacks structured, machine-readable content. A significant problem for the United Kingdom's National Health Service (NHS) are the long waiting lists for specialist mental healthcare. According to NHS data (NHS Digital, 2024), in each month of 2023, there were between 370,000 and 470,000 individual new referrals into secondary mental healthcare services. Referrals must be triaged by clinicians, using clinical information contained in the patient's EHR to arrive at a decision about the most appropriate mental healthcare team to assess and potentially treat these patients. The ability to efficiently recommend a relevant team by ingesting potentially voluminous clinical notes could help services both reduce referral waiting times and with the right technology, improve the evidence available to justify triage decisions. We present and evaluate three different approaches for LLM-based, end-to-end ingestion of variable-length clinical EHR data to assist clinicians when triaging referrals. Our model is able to deliver triage recommendations consistent with existing clinical practices and its architecture was implemented on a single GPU, making it practical for implementation in resource-limited NHS environments where private implementations of LLM technology will be necessary to ensure confidential clinical data are appropriately controlled and governed. Code available at: https://github.com/NtaylorOX/BespokeLLM_Triage.
Competing Interests: Declaration of competing interest A.K. and A.N.H. declare a research grant from GlaxoSmithKline (unrelated to this work). A.N.H. also received grant funding from Novo Nordisk Pharmaceuticals unrelated to this work. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR or the UK Department of Health. This study was supported by CRIS Powered by Akrivia Health, using data, systems and support from the NIHR Oxford Health Biomedical Research Centre (BRC-1215-20005) Research Informatics Team. Other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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