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Author Notes:

Azade Tabaie, PhD, Emory University School of Medicine, Department of Biomedical Informatics, 100 Woodruff Circle, 4th Floor East, Atlanta, GA 30322. Email: a.tabaie.87@gmail.com

By the WestJEM article submission agreement, all authors are required to disclose all affiliations, funding sources and financial or management relationships that could be perceived as potential sources of bias. No author has professional or financial relationships with any companies that are relevant to this study. This work was funded by the Emory Woodruff Health Sciences Center Synergy Award (PIs: Evans, Smith and Zeidan). RK was supported by the National Institutes of Health under Award Numbers R01GM139967 and UL1TR002378.There are no conflicts of interest to declare.



  • Emergency Service, Hospital
  • Hospitals
  • Humans
  • Intimate Partner Violence
  • Sexual Partners
  • Violence

A Novel Technique to Identify Intimate Partner Violence in a Hospital Setting


Journal Title:

Western Journal of Emergency Medicine


Volume 23, Number 5


, Pages 781-788

Type of Work:

Article | Final Publisher PDF


Introduction: Intimate partner violence (IPV) is defined as sexual, physical, psychological, or economic violence that occurs between current or former intimate partners. Victims of IPV may seek care for violence-related injuries in healthcare settings, which makes recognition and intervention in these facilities critical. In this study our goal was to develop an algorithm using natural language processing (NLP) to identify cases of IPV within emergency department (ED) settings. Methods: In this observational cohort study, we extracted unstructured physician and advanced practice provider, nursing, and social worker notes from hospital electronic health records (EHR). The recorded clinical notes and patient narratives were screened for a set of 23 situational terms, derived from the literature on IPV (ie, assault by spouse), along with an additional set of 49 extended situational terms, extracted from known IPV cases (ie, attack by spouse). We compared the effectiveness of the proposed model with detection of IPV-related International Classification of Diseases, 10th Revision, codes. Results: We included in the analysis a total of 1,064,735 patient encounters (405,303 patients who visited the ED of a Level I trauma center) from January 2012–August 2020. The outcome was identification of an IPV-related encounter. In this study we used information embedded in unstructured EHR data to develop a NLP algorithm that employs clinical notes to identify IPV visits to the ED. Using a set of 23 situational terms along with 49 extended situational terms, the algorithm successfully identified 7,399 IPV-related encounters representing 5,975 patients; the algorithm achieved 99.5% precision in detecting positive cases in our sample of 1,064,735 ED encounters. Conclusion: Using a set of pre-defined IPV-related terms, we successfully developed a novel natural language processing algorithm capable of identifying intimate partner violence.

Copyright information:

© 2022 Tabaie et al

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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