Title: Development of electronic health record data visualisation tools to support remote mental healthcare
Funder: Department of Health and Social Care

Abstract: Research question How can electronic health records (EHRs) be used to optimise clinical outcomes of remote mental healthcare? Background The COVID-19 pandemic has been associated with the rapid adoption of remote technologies to deliver mental healthcare. While restrictions resulting from the pandemic are likely to ease as it is controlled, remote technology may remain a very useful adjunct to conventional in-person consultation, by reducing geographical barriers to the delivery of care by clinicians, and access to care by patients. However, the potential benefits of remote care must be balanced against the risk that its use might, in some situations, have adverse effects on quality of care, patient satisfaction, and clinical outcomes. Because the recent adoption of remote technology has been rapid and unplanned, the evidence base to support best practice is still sparse. EHR data could be used to address this by facilitating the analysis of factors associated with good remote care outcomes. Natural language processing (NLP) tools, which enable the automated extraction of clinical information from free text EHR documents, could be applied to establish which features of mental healthcare may be best suited to remote consultation. I will apply NLP to EHR data and develop data visualisation tools to support more effective implementation of remote mental healthcare technology. Aims and objectives EHR/NLP data study: to compare the safety and effectiveness of remote vs in-person assessments from a large sample of NHS patients with mental health disorders. Data visualisation: to summarise the clinical outcomes of patients receiving remote and in-person assessments in a format readily accessible to NHS clinicians. Implementation pilot: to evaluate the utility of the data visualisation tools developed in (ii) as part of the safe and effective use of remote technology in an NHS mental health setting.Methods Source of clinical data: De-identified EHR data comprising approximately 5 million documents from 80,000 patients in the South London and Maudsley NHS Foundation Trust (SLaM) NIHR Biomedical Research Centre (BRC) Case Register. Data extraction: Clinical Record Interactive Search tool (CRIS). NLP: CRIS Comprehensive Data Extraction (CRIS-CODE) framework based on the General Architecture for Text Engineering (GATE). Data analysis and visualisation: R, Exploratory.io (population level data) and CogStack (patient level data). Implementation pilot: Quantitative and qualitative survey data on usability (using NVivo software). Timelines for delivery Years 1 and 2: EHR data extraction and NLP analysis Years 3, 4 and 5: Data visualisation tool development and implementation pilot Anticipated impact and dissemination New evidence to support safe and effective use of remote technology for mental healthcare presented at international academic meetings and published in high-impact open access journal articles and clinical guidelines. Data visualisation tools will be made publicly available. Open-access protocol to enable researchers to replicate methods in other healthcare settings. Patient contribution to study design and dissemination of findings through video production and workshops. Demonstration of data visualisation tools to healthcare service managers and policy makers through the King's Health Partners Psychosis Clinical Academic Group and NIHR South London Applied Research Collaboration (ARC).

Lay abstract: Remote technologies such as phone, video calls, text messages and e-mail allow patients and clinicians to communicate with each other when it is not possible to meet in-person. During the COVID-19 pandemic, mental health services often used remote technology to maintain clinical contact. However, the rise in remote consultation was largely unplanned, and although it highlighted the potential value of this approach in mental health care, this has yet to be systematically evaluated. Furthermore, while many patients found remote technology a useful way to access care, others did not, due to an unfamiliarity with, or a lack of the necessary technology. Research on the most effective ways for mental health services to implement remote technology is thus urgently needed. Clinicians in many NHS mental health services routinely document the care that they provide to patients in electronic health records (EHRs). Most of the information is stored as free text notes which may include details on the symptoms, diagnosis, and the treatment plan. Software that uses an approach called natural language processing (NLP) can be used to automatically extract clinical information from free text EHR documents. I have previously used NLP on EHR data to investigate which treatments are most effective and how to improve clinical care in people with mental disorders. In my fellowship, I aim to analyse EHR data using NLP to assess the impact of remote care technology on access to care and its benefit to patients. I will use this information to create data visualisation tools to support clinicians in providing care in a safe and effective way, and to test and then roll out the tools that I develop. I will analyse EHR data from around 80,000 patients receiving mental healthcare from the South London and Maudsley (SLaM) NHS Foundation Trust, using the Clinical Record Interactive Search tool (CRIS). The CRIS tool is built with robust data security measures that protect patients' privacy and the project will be overseen by a patient-led Oversight Committee. Using NLP tools on around 5 million de-identified documents, I will analyse information on symptoms that patients have experienced, their diagnoses, and what treatments they have received. The data I will analyse are de-identified and accessed through an encrypted NHS computer network to ensure that medical information stays confidential. I have worked with the CRIS Service User and Carer Advisory Group to develop my proposal and they will continue to support the research for the duration of my fellowship. I will use CogStack, a state-of-the-art data visualisation framework developed by the KCL/SLaM Centre for Translational Informatics, to create web-based dashboards which analyse the EHR data I have obtained and determine which features of remote care are most beneficial to patients. For example, young patients may find remote care more helpful than older patients. I will work with Stanford Medicine X, a pioneering research programme which places patients at the centre of digital healthcare technology development. With their support, I will design the data visualisation tools together with patient contributors to enable clinicians to support better use of remote care technology. I will survey clinicians' experiences of using the tools and analyse usage statistics from the web-based interface. I will publish my findings in academic journals and make the study protocol and example data visualisation tools publicly available, so that other researchers can use the approach in their own healthcare settings. I will present the findings to patients, carers, clinicians, researchers and healthcare policy makers through workshops (co-produced by the research team and patient contributors) to further improve the tools and support better use of remote care technology in the NHS.

Research activity codes: 7.1 INDIVIDUAL CARE NEEDS
Health categories: MENTAL HEALTH