Using de-identified electronic health records to research mental health supported housing services: A feasibility study

Christian Dalton-Locke, Johan H. Thygesen, Nomi Werbeloff, David Osborn, Helen Killaspy

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: Mental health supported housing services are a key component in the rehabilitation of people with severe and complex needs. They are implemented widely in the UK and other deinstitutionalised countries but there have been few empirical studies of their effectiveness due to the logistic challenges and costs of standard research methods. The Clinical Record Interactive Search (CRIS) tool, developed to de-identify and interrogate routinely recorded electronic health records, may provide an alternative to evaluate supported housing services. Methods: The feasibility of using the Camden and Islington NHS Foundation Trust CRIS database to identify a sample of users of mental health supported accommodation services. Two approaches to data interrogation and case identification were compared; using structured fields indicating individual's accommodation status, and iterative development of free text searches of clinical notes referencing supported housing. The data used were recorded over a 10-year-period (01-January-2008 to 31-December-2017). Results: Both approaches were carried out by one full-time researcher over four weeks (150 hours). Two structured fields indicating accommodation status were found, 2,140 individuals had a value in at least one of the fields representative of supported accommodation. The free text search of clinical notes returned 21,103 records pertaining to 1,105 individuals. A manual review of 10% of the notes indicated an estimated 733 of these individuals had used a supported housing service, a positive predictive value of 66.4%. Over two-thirds of the individuals returned in the free text search (768/1,105, 69.5%) were identified via the structured fields approach. Although the estimated positive predictive value was relatively high, a substantial proportion of the individuals appearing only in the free text search (337/1,105, 30.5%) are likely to be false positives.

Original languageEnglish
Article numbere0237664
JournalPLoS ONE
Volume15
Issue number8 August
DOIs
StatePublished - Aug 2020
Externally publishedYes

Bibliographical note

Funding Information:
Christian Dalton-Locke is funded by a 1+3 Economic and Social Research Council (ESRC) PhD studentship. Prof Killaspy is supported by the UCLH NIHR Biomedical Research Centre. Prof Osborn is supported by the UCLH NIHR Biomedical Research Centre and he was also in part supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) North Thames at Bart's Health NHS Trust.

Publisher Copyright:
© 2020 Dalton-Locke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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