Development of an electronic health record model to predict law enforcement presence in pediatric emergency department encounters
Document Type
Journal Article
Publication Date
12-15-2025
Journal
The American journal of emergency medicine
Volume
100
DOI
10.1016/j.ajem.2025.12.010
Keywords
Electronic health record; Law enforcement; Pediatrics
Abstract
BACKGROUND: Law enforcement intersects with emergency care often, but the prevalence of these interactions and impact on care delivery and health outcomes is unknown. Existing qualitative literature signals potential negative impact of law enforcement officer (LEO) presence on health outcomes, yet rigorous analysis has been limited by the inability to identify LEO presence reliably and accurately in the electronic health record (EHR). This study describes the development and validation of an EHR-based model that includes unstructured data to accurately predict LEO presence during emergency department (ED) encounters. METHODS: EHR data from 661 unique ED encounters from a single children's hospital between 10/1/2021 and 10/1/2022 were used for model development and validation. Encounters were included if patients were < 22 years of age and excluded if they were transferred directly from another hospital. We initially identified a "high prevalence cohort" with higher likelihood of LEO presence for initial keyword deterministic algorithm testing (n = 406). The high prevalence cohort included arrivals via emergency medical services or public (e.g., LEO) vehicle; walk-in, private car, and air arrivals were excluded. LEO presence was identified by one discrete EHR data field (arrival method) and keyword search. The keyword search included 14 unique words chosen by pediatric ED clinicians. We manually reviewed encounters to determine LEO presence and assess algorithm performance. After the keyword algorithm was developed and validated against the reference set, an additional 255 charts from the general population were manually reviewed to ensure generalizability. Then a multivariable model including keyword algorithm result, arrival method, and chief complaint was developed. Lasso was used to perform controlled variable selection. RESULTS: In the high prevalence population using the deterministic keyword algorithm, LEO presence was detected with 95 % sensitivity, 93 % specificity, and 93 % accuracy (positive predictive value 61 %). When applied to the general population, deterministic keyword algorithm detected LEO presence with 100 % sensitivity, 54 % specificity, and 66 % accuracy (PPV 44 %). The final multivariable probabilistic model included 21 of 29 variables after shrinkage and detected LEO presence in the entire cohort (n = 661) with 90 % sensitivity, 92 % specificity, 91 % accuracy (PPV 68 %) and AUC-PR of 0.85. CONCLUSION: Retrospective analysis as well as surveillance and reporting of LEO presence during hospital encounters can be implemented with high accuracy using an EHR-based multivariable model. This will enable analysis of prevalence of LEO presence in treatments spaces and related impact on clinical care and health outcomes.
APA Citation
Abrams, Anna H.; Scarbro, Sharon; Gray, Charlotte; Pemberton, Katherine; Feinstein, James; Betz, Marian E.; Goyal, Monika K.; Deakyne Davies, Sara J.; Bajaj, Lalit; and Colborn, Kathryn, "Development of an electronic health record model to predict law enforcement presence in pediatric emergency department encounters" (2025). GW Authored Works. Paper 8399.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/8399
Department
Pediatrics