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

• Authors: Azade Tabaie, Emory University; Amy Zeidan, Emory University; Dabney Evans, Emory University; Randi Smith, Emory University; Rishikesan Kamaleswaran, Emory University
• Journal Title: Western Journal of Emergency Medicine
• Volume: Volume 23, Number 5
• Publisher: The Regents of the University of California | 2022-09-01, Pages 781-788
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.5811/westjem.2022.7.56726
• Subjects: Health Sciences, Medicine and Surgery; Health Sciences, Public Health; Engineering, Biomedical
• Keywords: Emergency Service, Hospital; Hospitals; Humans; Intimate Partner Violence; Sexual Partners; Violence

Abstract

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.