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

Azade Tabaie, PhD; Amy J. Zeidan, MD; Dabney P. Evans, PhD, MPH; Randi N. Smith, MD, MPH; Rishikesan Kamaleswaran, PhD


Western J Emerg Med. 2022;23(5):781-788. 

In This Article

Abstract and Introduction


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.


Intimate partner violence (IPV) is defined as sexual, physical, psychological, or economic violence that occurs between current or former intimate partners.[1] Although men may experience IPV, women are disproportionately affected.[2] Nearly 30% of women globally have experienced IPV, making it a serious public health concern.[3] Intimate partner violence is a significant contributor to violence-related injury and a leading cause of femicide, which is the intentional killing of women based solely on their gender.[4] In the United States one in four women and one in nine men have experienced a severe form of IPV at some point in their lifetime.[5]

Individuals who experience IPV experience both short- and long-term adverse health outcomes such as chronic pain, substance abuse disorder, and mental health disorders.[6–9] People experiencing relationship violence may seek care for IPV-related injuries in healthcare settings, including emergency departments (ED), making recognition and intervention in these facilities critical.[10–11] A recent study revealed that patients experiencing IPV have considerably higher ED visit rates and injury-related hospitalization rates.[12] Yet IPV is profoundly underdiagnosed in healthcare settings, limiting identification and response efforts. A number of screening tools have been successfully developed to detect IPV in ED settings; however, screening tools are inconsistently used. Emerging efforts have focused on using machine learning to aid in detection of conditions including non-accidental trauma and IPV.[13–15]

Information captured in the electronic health record (EHR) including clinical notes, radiology reports, and imaging tests have been widely used to predict adverse outcomes for specific medical conditions. Khurana et al proposed a machine learning algorithm that uses radiologic findings of high-risk injuries (eg, injury location and patterns specific to IPV) to identify patients who are at high risk of IPV.[16,17] Using the 2016 South African Demographic and Health Survey dataset, Amusa et al developed a machine learning model using country-specific, self-reported survey data to capture common characteristics contributing to IPV.[13] In our study, we propose a novel natural language processing (NLP)-based algorithm using data embedded in the EHR to detect IPV-related ED encounters.