Methodologic Approaches for Using Electronic Medical Records to Identify Experiences of Violence in Transgender and Cisgender People: Closing the Gap Between Diagnostic Coding and Lived Experiences.
Journal
Medical care
ISSN: 1537-1948
Titre abrégé: Med Care
Pays: United States
ID NLM: 0230027
Informations de publication
Date de publication:
01 06 2023
01 06 2023
Historique:
medline:
15
5
2023
pubmed:
19
4
2023
entrez:
18
04
2023
Statut:
ppublish
Résumé
Transgender people experience extreme rates of violence and the electronic medical record (EMR) remains a mostly untapped resource to study the medical sequelae of such experiences. To develop and test a method for identifying experiences of violence using EMR data. Cross-sectional study utilizing EMR data. Transgender and cisgender people seen at a regional referral center in Upstate New York. We tested the utility of keyword searches and structured data queries to identify specific types of violence at various ages and in various contexts among cohorts of transgender and cisgender people. We compared the effectiveness of keyword searches to diagnosis codes and a screening question, "Are you safe at home?" using McNemar's test. We compared the prevalence of various types of violence between transgender and cisgender cohorts using the χ 2 test of independence. Of the transgender cohort, 47% had experienced some type of violence versus 14% of the cisgender cohort (χ 2P value <0.001). Keywords were significantly more effective than structured data at identifying violence among both cohorts (McNemar P values all <0.05). Transgender people experience extreme amounts of violence throughout their lives, which is better identified and studied using keyword searches than structured EMR data. Policies are urgently needed to stop violence against transgender people. Interventions are also needed to ensure safe documentation of violence in EMRs to improve care across settings and aid research to develop and implement effective interventions.
Sections du résumé
BACKGROUND
Transgender people experience extreme rates of violence and the electronic medical record (EMR) remains a mostly untapped resource to study the medical sequelae of such experiences.
OBJECTIVES
To develop and test a method for identifying experiences of violence using EMR data.
RESEARCH DESIGN
Cross-sectional study utilizing EMR data.
PEOPLE
Transgender and cisgender people seen at a regional referral center in Upstate New York.
MEASURES
We tested the utility of keyword searches and structured data queries to identify specific types of violence at various ages and in various contexts among cohorts of transgender and cisgender people. We compared the effectiveness of keyword searches to diagnosis codes and a screening question, "Are you safe at home?" using McNemar's test. We compared the prevalence of various types of violence between transgender and cisgender cohorts using the χ 2 test of independence.
RESULTS
Of the transgender cohort, 47% had experienced some type of violence versus 14% of the cisgender cohort (χ 2P value <0.001). Keywords were significantly more effective than structured data at identifying violence among both cohorts (McNemar P values all <0.05).
CONCLUSIONS
Transgender people experience extreme amounts of violence throughout their lives, which is better identified and studied using keyword searches than structured EMR data. Policies are urgently needed to stop violence against transgender people. Interventions are also needed to ensure safe documentation of violence in EMRs to improve care across settings and aid research to develop and implement effective interventions.
Identifiants
pubmed: 37072686
doi: 10.1097/MLR.0000000000001852
pii: 00005650-202306000-00009
pmc: PMC10168107
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
384-391Informations de copyright
Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.
Déclaration de conflit d'intérêts
A.A.: AHRQ T32 (HS000011), Conquer Cancer, the ASCO Foundation, and Segel/Halterman Fellowship Award. C.C.: the University of Rochester Susan B. Anthony Center. The remaining authors declare no conflict of interest.
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