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Clinician-recalled quoted speech in electronic health records and risk of suicide attempt: a case-crossover study

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Original languageEnglish
Article numbere036186
Pages (from-to)e036186
JournalBMJ Open
Issue number4
Published22 Apr 2020

Bibliographical note

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.


  • LJayasinghe_2020_BMJOpen.full

    LJayasinghe_2020_BMJOpen.full.pdf, 276 KB, application/pdf

    Uploaded date:11 May 2020

    Version:Final published version

    Licence:CC BY

King's Authors


Clinician narrative style in electronic health records (EHR) has rarely been investigated. Clinicians sometimes record brief quotations from patients, possibly more frequently when higher risk is perceived. We investigated whether the frequency of quoted phrases in an EHR was higher in time periods closer to a suicide attempt. Design A case-crossover study was conducted in a large mental health records database. A natural language processing tool was developed using regular expression matching to identify text occurring within quotation marks in the EHR. Setting Electronic records from a large mental healthcare provider serving a geographic catchment of 1.3 million residents in South London were linked with hospitalisation data. Participants 1503 individuals were identified as having a hospitalised suicide attempt from 1 April 2006 to 31 March 2017 with at least one document in both the case period (1-30 days prior to admission) and the control period (61-90 days prior to admission). Outcome measures The number of quoted phrases in the control as compared with the case period. Results Both attended (OR 1.05, 95% CI 1.02 to 1.08) and non-attended (OR 1.15, 95% CI 1.04 to 1.26) clinical appointments were independently higher in the case compared with control period, while there was no difference in mental healthcare hospitalisation (OR 0.99, 95% CI 0.98 to 1.01). In addition, there was no difference in the levels of quoted text between the comparison time periods (OR 1.09, 95% CI 0.91 to 1.30). Conclusions This study successfully developed an algorithm to identify quoted speech in text fields from routine mental healthcare records. Contrary to the hypothesis, no association between this exposure and proximity to a suicide attempt was found; however, further evaluation is warranted on the way in which clinician-perceived risk might be feasibly characterised from clinical text.

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