TY - JOUR
T1 - Identifying Military Service Status in Electronic Healthcare Records from Psychiatric Secondary Healthcare Services
T2 - A Validation Exercise Using the Military Service Identification Tool
AU - Leightley, Daniel
AU - Palmer, Laura
AU - Williamson, Charlotte
AU - Leal, Rahul
AU - Chandran, David
AU - Murphy, Dominic
AU - Fear, Nicola
AU - Stevelink, Sharon
N1 - Funding Information:
Conflicts of Interest: NTF is partly funded by the United Kingdom’s Ministry of Defence. NTF sits on the Independent Group Advising on the Release of Data at NHS Digital. NTF is also a trustee of one military-related charity. DM is employed by Combat Stress, a national charity in the United Kingdom that provides clinical mental health services to veterans and is a trustee of the Forces in Mind Trust (the funder for the project). DL is a reservist in the UK Armed Forces. This work has been undertaken as part of his civilian employment. CW is currently in receipt of a PhD studentship via the King’s Centre for Military Health Research Health and Wellbeing Study funded by the Office for Veterans’ Affairs (OVA), UK Government. SAMS is supported by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and the National Institute for Health and Care Research, NIHR Advanced Fellowship, SAMS, NIHR300592. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR, the OVA or the UK Ministry of Defence or the Department of Health and Social Care.
Funding Information:
Funding: This study was funded by the Forces in Mind Trust (Project: FiMT18/0525KCL), a funding scheme run by the Forces in Mind Trust using an endowment awarded by the National Lottery Community Fund. The salary of S.A.M.S. was partly paid by the NIHR Biomedical Research Centre at the SLaM NHS Foundation Trust and King’s College London. In addition to the listed authors, the study involved support from the NIHR Biomedical Research Centre. NIHR Biomedical Research Centre is a partnership between the SLaM NHS Foundation Trust and the Institute of Psychiatry, Psychology, and Neuroscience at King’s College London.
Funding Information:
This study was funded by the Forces in Mind Trust (Project: FiMT18/0525KCL), a funding scheme run by the Forces in Mind Trust using an endowment awarded by the National Lottery Community Fund. The salary of S.A.M.S. was partly paid by the NIHR Biomedical Research Centre at the SLaM NHS Foundation Trust and King’s College London. In addition to the listed authors, the study involved support from the NIHR Biomedical Research Centre. NIHR Biomedical Research Centre is a partnership between the SLaM NHS Foundation Trust and the Institute of Psychiatry, Psychology, and Neuroscience at King’s College London.
Publisher Copyright:
© 2023 by the authors.
PY - 2023/2/10
Y1 - 2023/2/10
N2 - Electronic healthcare records (EHRs) are a rich source of information with a range of uses in secondary research. In the United Kingdom, there is no pan-national or nationally accepted marker indicating veteran status across all healthcare services. This presents significant obstacles to determining the healthcare needs of veterans using EHRs. To address this issue, we developed the Military Service Identification Tool (MSIT), using an iterative two-staged approach. In the first stage, a Structured Query Language approach was developed to identify veterans using a keyword rule-based approach. This informed the second stage, which was the development of the MSIT using machine learning, which, when tested, obtained an accuracy of 0.97, a positive predictive value of 0.90, a sensitivity of 0.91, and a negative predictive value of 0.98. To further validate the performance of the MSIT, the present study sought to verify the accuracy of the EHRs that trained the MSIT models. To achieve this, we surveyed 902 patients of a local specialist mental healthcare service, with 146 (16.2%) being asked if they had or had not served in the Armed Forces. In total 112 (76.7%) reported that they had not served, and 34 (23.3%) reported that they had served in the Armed Forces (accuracy: 0.84, sensitivity: 0.82, specificity: 0.91). The MSIT has the potential to be used for identifying veterans in the UK from free-text clinical documents and future use should be explored.
AB - Electronic healthcare records (EHRs) are a rich source of information with a range of uses in secondary research. In the United Kingdom, there is no pan-national or nationally accepted marker indicating veteran status across all healthcare services. This presents significant obstacles to determining the healthcare needs of veterans using EHRs. To address this issue, we developed the Military Service Identification Tool (MSIT), using an iterative two-staged approach. In the first stage, a Structured Query Language approach was developed to identify veterans using a keyword rule-based approach. This informed the second stage, which was the development of the MSIT using machine learning, which, when tested, obtained an accuracy of 0.97, a positive predictive value of 0.90, a sensitivity of 0.91, and a negative predictive value of 0.98. To further validate the performance of the MSIT, the present study sought to verify the accuracy of the EHRs that trained the MSIT models. To achieve this, we surveyed 902 patients of a local specialist mental healthcare service, with 146 (16.2%) being asked if they had or had not served in the Armed Forces. In total 112 (76.7%) reported that they had not served, and 34 (23.3%) reported that they had served in the Armed Forces (accuracy: 0.84, sensitivity: 0.82, specificity: 0.91). The MSIT has the potential to be used for identifying veterans in the UK from free-text clinical documents and future use should be explored.
UR - http://www.scopus.com/inward/record.url?scp=85148736748&partnerID=8YFLogxK
U2 - 10.3390/healthcare11040524
DO - 10.3390/healthcare11040524
M3 - Article
SN - 2227-9032
VL - 11
JO - Healthcare (Basel, Switzerland)
JF - Healthcare (Basel, Switzerland)
IS - 4
M1 - 524
ER -