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. 2023 Dec 11:14:1217649.
doi: 10.3389/fpsyt.2023.1217649. eCollection 2023.

Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records

Affiliations

Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records

Rina Dutta et al. Front Psychiatry. .

Abstract

Background: Individualising mental healthcare at times when a patient is most at risk of suicide involves shifting research emphasis from static risk factors to those that may be modifiable with interventions. Currently, risk assessment is based on a range of extensively reported stable risk factors, but critical to dynamic suicide risk assessment is an understanding of each individual patient's health trajectory over time. The use of electronic health records (EHRs) and analysis using machine learning has the potential to accelerate progress in developing early warning indicators.

Setting: EHR data from the South London and Maudsley NHS Foundation Trust (SLaM) which provides secondary mental healthcare for 1.8 million people living in four South London boroughs.

Objectives: To determine whether the time window proximal to a hospitalised suicide attempt can be discriminated from a distal period of lower risk by analysing the documentation and mental health clinical free text data from EHRs and (i) investigate whether the rate at which EHR documents are recorded per patient is associated with a suicide attempt; (ii) compare document-level word usage between documents proximal and distal to a suicide attempt; and (iii) compare n-gram frequency related to third-person pronoun use proximal and distal to a suicide attempt using machine learning.

Methods: The Clinical Record Interactive Search (CRIS) system allowed access to de-identified information from the EHRs. CRIS has been linked with Hospital Episode Statistics (HES) data for Admitted Patient Care. We analysed document and event data for patients who had at some point between 1 April 2006 and 31 March 2013 been hospitalised with a HES ICD-10 code related to attempted suicide (X60-X84; Y10-Y34; Y87.0/Y87.2).

Findings: n = 8,247 patients were identified to have made a hospitalised suicide attempt. Of these, n = 3,167 (39.8%) of patients had at least one document available in their EHR prior to their first suicide attempt. N = 1,424 (45.0%) of these patients had been "monitored" by mental healthcare services in the past 30 days. From 60 days prior to a first suicide attempt, there was a rapid increase in the monitoring level (document recording of the past 30 days) increasing from 35.1 to 45.0%. Documents containing words related to prescribed medications/drugs/overdose/poisoning/addiction had the highest odds of being a risk indicator used proximal to a suicide attempt (OR 1.88; precision 0.91 and recall 0.93), and documents with words citing a care plan were associated with the lowest risk for a suicide attempt (OR 0.22; precision 1.00 and recall 1.00). Function words, word sequence, and pronouns were most common in all three representations (uni-, bi-, and tri-gram).

Conclusion: EHR documentation frequency and language use can be used to distinguish periods distal from and proximal to a suicide attempt. However, in our study 55.0% of patients with documentation, prior to their first suicide attempt, did not have a record in the preceding 30 days, meaning that there are a high number who are not seen by services at their most vulnerable point.

Keywords: assessment; electronic health records; language; risk; suicide.

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Conflict of interest statement

MH leads the RADAR-CNS consortium, a private–public pre-competitive collaboration on mobile health, through which King’s College London receives in-kind and cash contributions from Janssen, Biogen, UCB, Merck, and Lundbeck. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Derivation of the patient cohort and corpora of distal and proximal documents.
Figure 2
Figure 2
Venn diagram showing n = 2,787 patients contributing data to the analysis, with n = 15,226 documents pertaining to the distal period (between 365 and 300 days prior/following a suicide-related hospital admission) and n = 25,848 documents relating to the proximal period (between 31 days and 1 day prior to a suicide-related admission).
Figure 3
Figure 3
Graph showing monitoring level for patients indicating an increase in monitoring level proximal to a suicide attempt. Monitoring level numerator in blue and denominator in red.
Figure 4
Figure 4
Confusion matrix showing the degree of interannotator agreement across the 17 categories.

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References

    1. APA . The American Psychiatric Association practice guidelines for the psychiatric evaluation of adults: guideline III. Assessment of suicide risk. Arlington, VA: American Psychiatric Association; (2016).
    1. Velupillai S, Hadlaczky G, Baca-Garcia E, Gorrell GM, Werbeloff N, Nguyen D, et al. . Risk assessment tools and data-driven approaches for predicting and preventing suicidal behavior. Front Psych. (2019) 10:36. doi: 10.3389/fpsyt.2019.00036, PMID: - DOI - PMC - PubMed
    1. Pirkis J, Nicholas A, Gunnell D. The case for case-control studies in the field of suicide prevention. Epidemiol Psychiatr Sci. (2019) 29:e62. doi: 10.1017/S2045796019000581 - DOI - PMC - PubMed
    1. Abdelrahman W, Abdelmageed A. Medical record keeping: clarity, accuracy, and timeliness are essential. BMJ. (2014) 348:f7716. doi: 10.1136/bmj.f7716 - DOI
    1. Suryanarayanan P, Epstein EA, Malvankar A, Lewis BL, DeGenaro L, Liang JJ, et al. . Timely and efficient AI insights on EHR: system design. AMIA Annu Symp Proc. (2020) 2020:1180–9. PMID: - PMC - PubMed