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

• 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
• 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
• 来源: https://reporter.nih.gov/project-details/10707143
• 关键词: Accident and Emergency department; Acute; Age; Algorithms; Automated Clinical Decision Support; Awareness; COVID-19 pandemic; Caring; Chronic; Chronic Disease; Chronology; Clinical; Clinical Data; Clinical/Radiologic; Computerized Medical Record; Cues; Data; Data Set; Decision Support Model; Dedications; Diagnosis; Dimensions; Early Diagnosis; Early identification; Elderly; Feedback; Feeling; Fracture; Fright; Gender; Goals; Harvest; Health; High Prevalence; Homicide; Image; Injury; Institution; Intervention; Knowledge; Label; Language; Lead; Life; Location; Machine Learning; Medical; Methods; Modality; Modeling; Neural Network Simulation; Non-accidental; Pathology; Patient Care; Patient Self-Report; Patients; Pattern; Performance; Phase; Physicians; Play; Public Health; Questionnaires; Race; Radiology Specialty; Recording of previous events; Reporting; Research; Resource Sharing; Risk; Risk Factors; Role; Safety; Screening procedure; Sensitivity and Specificity; Severities; Shame; Site; Social Workers; Survivors; System; Testing; Time; Translating; Visit; Visualization; Woman; case control; clinical care; clinical decision support; clinical practice; clinical predictors; clinical risk; cohort; convolutional neural network; craniofacial; evidence base; experience; face bone structure; health care settings; imaging study; improved outcome; interdisciplinary approach; intimate partner violence; limb bone; machine learning algorithm; machine learning model; men; model building; multidisciplinary; multimodality; musculoskeletal injury; non-verbal; novel; pandemic disease; patient population; pediatric trauma; pilot test; point of care; predictive modeling; predictive tools; prevent; psychological violence; radiologist; recurrent neural network; repository; risk stratification; serial imaging; severe injury; sexual violence; social; social stigma; support tools; telehealth; tertiary care; tool

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

项目成果

Effect of the NFL's Super Bowl on emergency department visits for assault-related injuries.

NFL 超级碗比赛对因袭击相关伤害而前往急诊室就诊的影响。

• DOI: 10.1007/s10140-023-02188-9
• 发表时间: 2024
• 期刊: Emergency radiology
• 影响因子: 2.2
• 作者: Khurana,Bharti;Prakash,Jaya;Chopra,RohanR;Loder,RandallT
• 通讯作者: Loder,RandallT