Title: CHRONOS: Deep representations of chronological events in electronic health records for real-time clinical decision support in secondary mental health care.
Funder: Department of Health and Social Care

Abstract: Background : Across England, there are approximately 1.31 to 1.39 million referrals to secondary mental health services (SMHS) each month. Each patient referred must have their referral documentation and clinical notes -- contained in the Electronic Health Record (EHR) -- reviewed by a multidisciplinary team (MDT) composed of mental health professionals. In 2018-19, the total cost to the NHS of initial assessments was £326 million. Electronic health records (EHRs) are voluminous, largely ‘free-text’ and for research reuse, usually require extensive and task-specific expert-human annotation. Aims : Automated processing of longitudinal EHR data with vector-space representations have significant limitations. Recent advances in deep neural networks and tensor algebra representations offer principled methods for overcoming these limits. We will deliver a natural language processing technology (CHRONOSIG) using these new insights that will process longitudinal free-text EHR data and deliver a ‘signature’ of the patient. These signatures will be used down-stream in three supervised-learning applications to improve efficiency for SMHS referral processes a) triaging patients referred into SMHS and b) identifying those patients with depression and psychotic disorders who will have complex care needs. To establish the tools’ utility, our emphasis will be on clinician-AI augmented decision making, rather than “data-to-diagnosis” competitions which characterise medical AI applications. Timelines : Within 2 years, using public-domain technologies and England’s largest collection of multi-site mental health EHR data (UK-CRIS), we will deliver and then evaluate three digital triaging tools for pre-clinical use and viability ‘at-scale’ for NHS roll-out. Impact : CHRONOSIG has the potential to improve the efficiency and equitability of SMHS triage and reduce the “hidden waiting lists” -- the period where patients are referred but have yet to be assessed by clinical teams. We will deliver added-value to the the UK-CRIS dataset and provide reusable technology for future mental health EHR research.

Lay abstract: Aims: We will build clinical-decision tools to support clinicians in making decisions and triaging referrals in NHS clinical practice. The tools will identify and improve access to care for people who have traditionally been excluded from, underrepresented or poorly served by mental health services in the UK, ensuring they receive equitable care. Background: People needing treatment for mental health problems are often first seen by their general practitioners, in hospital Emergency Departments or even in educational settings and staff then refer them to mental health services. This network of referral routes converges on community mental health teams who make decisions about suitability for treatment, how urgently to see people and which specialist teams need to be involved. In routine care, all referrals are assessed or 'triaged' by a multi-disciplinary team and often require the person is clinically reviewed over multiple contacts to eventually decide on treatment pathways. In 2019 this referral process cost the NHS &pound;326 million. Electronic health records contain data from clinical encounters between patients and professionals, over multiple visits and this creates a longitudinal 'fingerprint' of the patient s mental health over time. These clinical records are a rich source of information, which is particularly pertinent to mental health because mental illness is largely expressed as language and behaviour and the primary tool for the clinician is the interview, recorded as clinical notes and correspondence. However, this information is voluminous and time consuming to process (even by expert clinicians) when triaging referrals. Recent advances in artificial intelligence and natural language processing will help unlock this information to make triage more efficient and access to care more robust. Work plan: Using a decade's worth of observational data from the largest collection of the secondary care mental health electronic health records in the UK alongside state-of-the art neural network algorithms for natural language processing, we will develop, train and validate models capable of characterising a patient s trajectory directly from clinical notes. The model s effectiveness, safety and efficiency will be evaluated in simulated multidisciplinary triage teams at four NHS Foundation Trusts for pre-clinical use. Patient and public involvement: Through the Patient and Public Involvement group of the Oxford Health Biomedical Research Center, patients, carers and clinicians will be involved throughout with regular meetings for co-producing the project scope, milestones and evaluating the clinical efficacy, safety and ethical boundaries of the clinical decision support tools. Deliverables: An intelligent operational automation tool, based on a longitudinal patients trajectory derived from historical observations, will help streamline the process of being referred to an NHS mental health team. It will also help identify people with the highest care needs and suitable for services and clinical trials.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: MENTAL HEALTH