Identifying Patterns of Health Care Utilization among Physical Elder Abuse Victims Using Medicare Data and Legally Adjudicated Cases

使用医疗保险数据和法律裁决案件确定老年人身体虐待受害者的医疗保健利用模式

• 批准号: 10224085
• 负责人: Anthony Rosen
• 金额: $ 43.8万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10224085
• 关键词: Accident and Emergency department; Adult; Age; Algorithms; Ambulatory Care; Area; Automobile Driving; Case Series; Characteristics; Child; Child Abuse; Child Abuse and Neglect; Clinical; Continuity of Patient Care; Control Groups; Data; Databases; Detection; Development; Diagnostic radiologic examination; Early Intervention; Early identification; Economic Burden; Elder Abuse; Elderly; Emergency Medicine; Emergency department visit; Enrollment; Ethics; Family; Fracture; Future; Glean; Goals; Grant; Health; Health Care Costs; Health Personnel; Health Professional; Healthcare; Hospital Departments; Hospitalization; Impaired cognition; Influentials; Injury; Intervention; Knowledge; Legal; Limb structure; Link; Machine Learning; Medical; Medicare; Medicare claim; Methodology; Morbidity - disease rate; Nursing Homes; Outcome; Pattern; Police; Policies; Preventive service; Primary Health Care; Public Health Informatics; Research; Research Support; Roentgen Rays; Seminal; Series; Services; Specialist; Suggestion; Testing; Victimization; Visit; Vulnerable Populations; Work; X-Ray Computed Tomography; abuse victim; adjudicate; aged; base; comorbidity; computer science; convict; cost; experience; functional disability; health assessment; health care service utilization; health economics; hospital utilization; improved; informatics tool; innovation; insight; intimate partner violence; legal cases; maxillofacial; medication compliance; member; metropolitan; mortality; multidisciplinary; perpetrators; prevent; prospective; statistics

PROJECT SUMMARY The over-arching aim of this research is to: improve understanding of the patterns of health care utilization and associated costs of physical elder abuse victims to improve early identification and intervention and to inform policy. In our prior research, supported by an NIA GEMSSTAR and Beeson grant to PI Dr. Rosen, we leveraged unique partnerships with prosecutors’ offices to comprehensively examine legal case files from more than 200 victims in two large metropolitan areas, representing the largest retrospective series of legally adjudicated physical elder abuse cases ever examined. For the proposed study, we plan to link these cases, for which we have done extensive clinical analysis, to Medicare claims data. The Specific Aims of this proposal are: (1) To use Medicare claims to describe rates and patterns of health care utilization of victims before and after detection, with a focus on potentially missed opportunities by health professionals to identify abuse and differences based on victim characteristics, (2) To compare rates and patterns of ED visits and hospitalizations of physical elder abuse victims to control groups selected algorithmically from Medicare claims data, and (3) To compare patterns of health care utilization other than EDs/hospitals between victims and controls. The proposed research will provide important insight into the patterns of health care utilization for physical elder abuse victims, focusing on whether missed opportunities exist and suggestive patterns emerge. We plan to employ sophisticated machine learning approaches to increase our ability to identify patterns suggestive of physical elder abuse exposure. This will inform strategies for identification and intervention by health care providers, and knowledge gleaned will support the future development of a health informatics tool to identify potential victims. Findings on associated costs will help define the scope and impact of physical elder abuse. This innovative approach leverages existing data gathered with NIA support and extrapolates from successful research approaches in child abuse and intimate partner violence while expanding on them. As no additional subjects will be prospectively enrolled as part of this research, we avoid many of the ethical concerns typical in elder mistreatment work. Our multi-disciplinary team of experts in elder abuse, child abuse and neglect, intimate partner violence, and emergency medicine as well as specialists in statistics, health economics, and computer science is uniquely able to conduct this research. Previously, we have done seminal work examining health care usage and health-related outcomes of elder abuse victims by linking adult protective services and police databases to health care data, which is highly relevant for this proposal. We also have deep experience in using Medicare claims data and machine learning for research. The long-term goal of our research is to leverage a better understanding of health care use by elder abuse victims to improve the ability of health care providers to identify, intervene, and prevent victimization and to inform policy changes to help this vulnerable population.

项目摘要 本研究的主要目的是：提高对卫生保健利用模式的认识， 身体虐待老年人受害者的相关费用，以改善早期识别和干预，并告知 政策在我们之前的研究中，由NIA GEMSSTAR和Beeson资助PI博士罗森，我们 利用与检察官办公室的独特伙伴关系，全面审查来自更多国家的法律的案卷， 在两个大都市地区的200多名受害者，代表了最大的回顾系列的法律 曾审理过的虐待老人案件。在拟议的研究中，我们计划将这些病例联系起来， 我们已经做了广泛的临床分析，医疗保险索赔数据。本提案的具体目标 （1）使用医疗保险索赔来描述受害者之前和之后的医疗保健利用率和模式。 在发现后，重点关注卫生专业人员可能错过的机会，以查明虐待行为， 基于受害者特征的差异，（2）比较艾德就诊和住院的比率和模式 从医疗保险索赔数据中通过算法选择的对照组中，以及（3） 比较受害者和对照组之间除急诊室/医院以外的卫生保健利用模式。的 拟议的研究将提供重要的洞察模式的健康护理利用的身体老年人 虐待受害者，重点是是否存在错过的机会和暗示模式出现。我们计划 使用复杂的机器学习方法来提高我们识别暗示性模式的能力， 身体虐待老人这将为卫生保健部门的识别和干预战略提供信息 提供者和收集的知识将支持未来开发健康信息学工具， 潜在的受害者有关相关成本的调查结果将有助于确定身体虐待老年人的范围和影响。 这种创新方法利用了在NIA支持下收集的现有数据，并从成功的 研究虐待儿童和亲密伴侣暴力的方法，同时扩大这些方法。因为没有额外 受试者将作为本研究的一部分前瞻性招募，我们避免了许多典型的伦理问题， 虐待老人的工作我们的多学科专家团队在虐待老人，虐待儿童和忽视， 亲密伴侣暴力和急诊医学以及统计学，卫生经济学和 计算机科学是唯一能够进行这项研究的。此前，我们已经做了开创性的工作， 通过将成人保护服务与 从警察数据库到医疗保健数据，这与本提案高度相关。我们也深有体会 使用医疗保险索赔数据和机器学习进行研究。我们研究的长期目标是 更好地了解老年虐待受害者的医疗服务使用情况，以提高医疗服务能力 提供者识别，干预和防止受害，并告知政策变化，以帮助这些弱势群体 人口