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Quoted text in the mental healthcare electronic record: An analysis of the distribution and content of single-word quotations

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article numbere049249
JournalBMJ Open
Volume11
Issue number12
DOIs
Published30 Dec 2021

Bibliographical note

Funding Information: Funding LJ, SV and RS are part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and King’s College London. RS is additionally funded by a Medical Research Council (MRC) Mental Health Data Pathfinder Award to King’s College London; an NIHR Senior Investigator Award; the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. Funding Information: LJ, SV and RS are part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and King?s College London. RS is additionally funded by a Medical Research Council (MRC) Mental Health Data Pathfinder Award to King?s College London; an NIHR Senior Investigator Award; the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King?s College Hospital NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. Publisher Copyright: © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

King's Authors

Abstract

Objective To investigate the distribution and content of quoted text within the electronic health records (EHRs) using a previously developed natural language processing tool to generate a database of quotations. Design χ 2 and logistic regression were used to assess the profile of patients receiving mental healthcare for whom quotations exist. K-means clustering using pre-trained word embeddings developed on general discharge summaries and psychosis specific mental health records were used to group one-word quotations into semantically similar groups and labelled by human subjective judgement. Setting EHRs from a large mental healthcare provider serving a geographic catchment area of 1.3 million residents in South London. Participants For analysis of distribution, 33 499 individuals receiving mental healthcare on 30 June 2019 in South London and Maudsley. For analysis of content, 1587 unique lemmatised words, appearing a minimum of 20 times on the database of quotations created on 16 January 2020. Results The strongest individual indicator of quoted text is inpatient care in the preceding 12 months (OR 9.79, 95% CI 7.84 to 12.23). Next highest indicator is ethnicity with those with a black background more likely to have quoted text in comparison to white background (OR 2.20, 95% CI 2.08 to 2.33). Both are attenuated slightly in the adjusted model. Early psychosis intervention word embeddings subjectively produced categories pertaining to: mental illness, verbs, negative sentiment, people/relationships, mixed sentiment, aggression/violence and negative connotation. Conclusions The findings that inpatients and those from a black ethnic background more commonly have quoted text raise important questions around where clinical attention is focused and whether this may point to any systematic bias. Our study also shows that word embeddings trained on early psychosis intervention records are useful in categorising even small subsets of the clinical records represented by one-word quotations.

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