Citation

Bergquist T, Pejaver V, Hammarlund N, Mooney SD, Mooney SJ. BMC Public Health 2020; 20(1): e46.

Affiliation

Harborview Injury Prevention & Research Center, University of Washington, Seattle, WA, USA.

Copyright

(Copyright © 2020, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1186/s12889-020-8153-7

PMID

31931781

Abstract

BACKGROUND: The increasing adoption of electronic health record (EHR) systems enables automated, large scale, and meaningful analysis of regional population health. We explored how EHR systems could inform surveillance of trauma-related emergency department visits arising from seasonal, holiday-related, and rare environmental events.

METHODS: We analyzed temporal variation in diagnosis codes over 24 years of trauma visit data at the three hospitals in the University of Washington Medicine system in Seattle, Washington, USA. We identified seasons and days in which specific codes and categories of codes were statistically enriched, meaning that a significantly greater than average proportion of trauma visits included a given diagnosis code during that time period.

RESULTS: We confirmed known seasonal patterns in emergency department visits for trauma. As expected, cold weather-related incidents (e.g. frostbite, snowboarding injury) were enriched in the winter, whereas fair weather-related incidents (e.g. bug bites, boating accidents, bicycle accidents) were enriched in the spring and summer. Our analysis of specific days of the year found that holidays were enriched for alcohol poisoning, assaults, and firework accidents. We also detected one time regional events such as the 2001 Nisqually earthquake and the 2006 Hanukkah Eve Windstorm.

CONCLUSIONS: Though EHR systems were developed to prioritize operational rather than analytic priorities and have consequent limitations for surveillance, our EHR enrichment analysis nonetheless re-identified expected temporal population health patterns. EHRs are potentially a valuable source of information to inform public health policy, both in retrospective analysis and in a surveillance capacity.

Language: en

Keywords

Data science; Electronic health records; Learning healthcare system; Population health