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

让不可见变得可见：用于亲密伴侣暴力风险和严重程度预测 (AIRS) 的自动化临床决策支持工具

基本信息

批准号：
10707143
负责人：
Bharti Khurana
金额：
$ 79.32万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-21 至 2026-06-30
项目状态：
未结题

项目摘要

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.

项目摘要该项目的重点是开发一种自动临床决策支持工具来预测基于历史成像和临床数据的亲密伴侣暴力（IPV）风险和严重性。尽管这个关键公共卫生问题的患病率很高和紧迫性，但目前尚无诊断IPV的客观工具。在医疗保健环境中检测IPV的挑战是由于多种因素，包括患者的震惊和对后果的恐惧和物理学缺乏意识和害怕冒犯病人和伴侣的恐惧。成像播放时由于明确的良好在成像研究上建立的滥用模式，缺乏基于证据的IPV研究相关的成像模式导致IPV的识别率不足和诊断不足。通过认识在当前和以前的放射学研究中，IPV的位置和成像模式，当受害者不出现时，放射科士可以帮助识别IPV。我们的假设是多维临床支持工具，包括从中收获的成像和临床发现电子病历可以提供IPV风险的准确而全面的计算。然后可以集成自动化的IPV风险和严重性预测以改变护理计划生存并使“隐形”可见。目的1：通过分析放射学研究来定义IPV相关的成像模式和严重性已知的IPV表面和匹配的控件目标2：通过建立临床决策支持来确定IPV风险和严重性预测源自历史成像和临床预测因子的工具。目标3：在新数据集上验证IPV预测模型并评估集成使用安全的存储库产生放射学工作流程。如果我们的假设是正确的，已建立的IPV相关成像模式，则来自整合到临床护理中的历史成像和临床预测因子将能够诊断IPV 客观地。