Associations of remote mental healthcare with clinical outcomes: a natural language processing enriched electronic health record data study protocol

Journal article

IntroductionPeople often experience significant difficulties in receiving mental healthcare due to insufficient resources, stigma and lack of access to care. Remote care technology has the potential to overcome these barriers by reducing travel time and increasing frequency of contact with patients. However, the safe delivery of remote mental healthcare requires evidence on which aspects of care are suitable for remote delivery and which are better served by in-person care. We aim to investigate clinical and demographic associations with remote mental healthcare in a large electronic health record (EHR) dataset and the degree to which remote care is associated with differences in clinical outcomes using natural language processing (NLP) derived EHR data.Methods and analysisDeidentified EHR data, derived from the South London and Maudsley (SLaM) National Health Service Foundation Trust Biomedical Research Centre (BRC) Case Register, will be extracted using the Clinical Record Interactive Search tool for all patients receiving mental healthcare between 1 January 2019 and 31 March 2022. First, data on a retrospective, longitudinal cohort of around 80 000 patients will be analysed using descriptive statistics to investigate clinical and demographic associations with remote mental healthcare and multivariable Cox regression to compare clinical outcomes of remote versus in-person assessments. Second, NLP models that have been previously developed to extract mental health symptom data will be applied to around 5 million documents to analyse the variation in content of remote versus in-person assessments.Ethics and disseminationThe SLaM BRC Case Register and Clinical Record Interactive Search (CRIS) tool have received ethical approval as a deidentified dataset (including NLP-derived data from unstructured free text documents) for secondary mental health research from Oxfordshire REC C (Ref: 18/SC/0372). The study has received approval from the SLaM CRIS Oversight Committee. Study findings will be disseminated through peer-reviewed, open access journal articles and service user and carer advisory groups.

Authors: Ahmed, MS; Kornblum, D; Oliver, D; Fusar-Poli, P; Patel, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BMJ Publishing Group
• Journal: BMJ Open
• Volume: 13
• Issue: 2
• Pages: e067254-e067254
• Publication date: 2023-02-10
• Acceptance date: 2023-02-01
• DOI: 10.1136/bmjopen-2022-067254
• EISSN: 2044-6055
• ISSN: 2044-6055
• Language: English
• Keywords: Electronic medical record; Psychiatry; Family medicine; Health care; Mental healthcare; Health records; Mental health; Data science; Electronic health record; Alternative medicine; Pathology; Medicine; Protocol (science)