Maximizing the use of social and behavioural information from secondary care mental health electronic health records

Abstract

The contribution of social and behavioural factors in the development of mental health conditions and treatment effectiveness is widely supported, yet there are weak population level data sources on social and behavioural determinants of mental health. Enriching these data gaps will be crucial to accelerating precision medicine. Some have suggested the broader use of electronic health records (EHR) as a source of non-clinical determinants, although social and behavioural information are not systematically collected metrics in EHRs, internationally. In this commentary, we highlight the nature and quality of key available structured and unstructured social and behavioural data using a case example of value counts from secondary mental health data available in the UK from the UK Clinical Record Interactive Search (CRIS) database; highlight the methodological challenges in the use of such data; and possible solutions and opportunities involving the use of natural language processing (NLP) of unstructured EHR text. Most structured non-clinical data fields within secondary care mental health EHR data have too much missing data for adequate use. The utility of other non-clinical fields reported semi-consistently (e.g., ethnicity and marital status) is entirely dependent on treating them appropriately in analyses, quantifying the many reasons behind missingness in consideration of selection biases. Advancements in NLP offer new opportunities in the exploitation of unstructured text from secondary care EHR data particularly given that clinical notes and attachments are available in large volumes of patients and are more routinely completed by clinicians. Tackling ways to re-use, harmonize, and improve our existing and future secondary care mental health data, leveraging advanced analytics such as NLP is worth the effort in an attempt to fill the data gap on social and behavioural contributors to mental health conditions and will be necessary to fulfill all of the domains needed to inform personalized interventions.

Andrey Kormilitzin

Senior Researcher

My research is centred around translating advances in mathematics, statistical machine learning and deep learning to address challenges involved in learning, inference and ethical decision making using complex biomedical and health data.

Andrea Cipriani

Professor of Psychiatry

My main research interest is evidence-based mental health and precision psychiatry. My research focuses on the evaluation of pharmacological, psychological and psychosocial interventions, mainly about major depression, bipolar disorder and schizophrenia

Alejo J Nevado-Holgado

Associate Professor

I am an Associate Professor of the Department of Psychiatry and the Big Data Institute, and part of Dementia Research Oxford. I am very glad to supervise the AI team in the TNDR, formed by 10 excellent machine learners and bioinformaticians. Our focus is on the applications of machine learning and bioinformatics to mental health care. In addition, I also hold a position at the Big Data Institute, where we collaborate in the application of machine learning to genomics and target discovery. I am also consultant to a number of AI companies.