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

Author:

Tabaie Azade1,Zeidan Amy2,Evans Dabney3,Smith Randi4,Kamaleswaran Rishikesan5

Affiliation:

1. Emory University School of Medicine, Department of Biomedical Informatics, Atlanta, Georgia

2. Emory University School of Medicine, Department of Emergency Medicine, Atlanta, Georgia

3. Emory University, Rollins School of Public Health, Hubert Department of Global Health, Atlanta, Georgia; Emory University, Rollins School of Public Health, Department of Behavioral, Social and Health Educations Sciences, Atlanta, Georgia

4. Emory University, Rollins School of Public Health, Department of Behavioral, Social and Health Educations Sciences, Atlanta, Georgia; Emory University School of Medicine, Department of Surgery, Atlanta, Georgia

5. Emory University School of Medicine, Department of Biomedical Informatics, Atlanta, Georgia; Emory University School of Medicine, Department of Emergency Medicine, Atlanta, Georgia; Georgia Institute of Technology and Emory School of Medicine, Department of Biomedical Engineering, Atlanta, Georgia

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.

Publisher

Western Journal of Emergency Medicine

Subject

General Medicine,Emergency Medicine

Reference28 articles.

1. CDC. Intimate Partner Violence. 2020. Available at: https://www.cdc.gov/violenceprevention/intimatepartnerviolence/index.html. Accessed September 29, 2021..

2. Fulu E, Jewkes R, Roselli T, et al. Prevalence of and factors associated with male perpetration of intimate partner violence: findings from the UN Multi-country Cross-sectional Study on Men and Violence in Asia and the Pacific. Lancet Glob Health. 2013;1(4):e187-207.

3. W. H. Organization. WHO multi-country study on women’s health and domestic violence against women. 2005. Available at: https://www.who.int/reproductivehealth/publications/violence/24159358X/en/. Accessed September 29, 2021.

4. United Nations. Global study on homicide. 2019. Available at: https://www.unodc.org/unodc/en/data-and-analysis/global-study-on-homicide.html. Accessed September 29, 2021.

5. NCADV. Domestic violence. 2020. Available at: https://assets.speakcdn.com/assets/2497/domestic_violence-2020080709350855.pdf?1596811079991. Accessed September 29, 2021.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3