Making the invisible visible: An automated clinical decision support tool for Intimate Partner Violence Risk and Severity Prediction (AIRS)

NIH Pandemic-Era Grants

Pandemic Era Grants

2024

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Principal Investigator: Bharti  Khurana
Organization: BRIGHAM AND WOMEN'S HOSPITAL
Fiscal Year: 2024
Award: $170,705
Funding agency: National Institute of Biomedical Imaging and Bioengineering

Project Abstract
This project is focused on developing an automated clinical decision support tool for predicting
Intimate Partner Violence (IPV) risk and severity based on historical imaging and clinical data.
Despite the high prevalence and urgency of this critical public health issue, there is currently no
objective tool to diagnose IPV. The challenges in detecting IPV in the health care setting are
due to multiple factors, including the patient’s feelings of shame and fear of consequences and
physician’s lack of awareness and fear of offending the patient and partner. While imaging plays
an essential role in diagnosing nonaccidental trauma in children because of clear well-
established patterns of abuse on imaging studies, a lack of evidence-based research on IPV
related imaging patterns has led to under-recognition and underdiagnosis of IPV. By recognizing
location and imaging patterns specific to IPV on current and previous radiological studies,
radiologists can help identify IPV when the victims are not forthcoming. Our hypothesis is that a
multidimensional clinical support tool including imaging and clinical findings harvested from the
electronic medical record can provide an accurate and comprehensive calculation of IPV risk.
The automated IPV risk and severity predictions can then be integrated to transform the care
plan for survivors and make the “invisible” visible.
Aim 1: To define IPV related imaging patterns and severity by analyzing radiological studies of
known IPV survivors and matched controls
Aim 2: To determine IPV risk and severity prediction by developing a clinical decision support
tool derived from historical imaging and clinical predictors.
Aim 3: To validate the IPV prediction model on new datasets and evaluate the integration of
results in radiology workflow using a safe repository.
If our hypotheses are correct, established IPV related imaging patterns, a CDS tool derived from
historical imaging and clinical predictors integrated into clinical care will be able to diagnose IPV
objectively.

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