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: 10402860
• 负责人: Anthony Rosen
• 金额: $ 37.69万
• 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 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10402860
• 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; 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; radiological imaging; 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 这项研究的整理目的是：提高对医疗保健利用方式和 身体虐待的相关成本令人恐惧，以改善早期识别和干预并告知 政策。在我们先前的研究中，由Nia Gemsstar和Beeson Grant授予Pi Rosen博士的支持，我们 利用与检察官办公室的独特伙伴关系，以全面检查更多的法律案件文件 在两个大都市地区的200名受害者比200名受害者，代表了最大的回顾性系列法律。 经过调整的身体虐待案件曾经检查过。对于拟议的研究，我们计划将这些案例联系起来， 为此，我们对Medicare索赔数据进行了广泛的临床分析。该提议的具体目的 是：（1）使用Medicare索赔来描述滥用虐待之前的医疗保健利用率和模式 发现后，重点是卫生专业人员可能错过的机会，以识别虐待和 基于受害者特征的差异，（2）比较ED访问和住院的费率和模式 从Medicare索赔数据中选择算法的对照组的身体虐待恐怖，（3） 比较侵犯和控制之间的EDS/医院以外的医疗保健利用模式。这 拟议的研究将为身体老年人的医疗保健利用方式提供重要的见解 虐待可怕，着眼于是否存在错过的机会并出现暗示性模式。我们计划 员工复杂的机器学习方法，以提高我们识别暗示模式的能力 身体虐待的身体曝光。这将为卫生保健识别和干预的策略提供信息 提供者和清理知识将支持健康信息工具的未来开发，以识别 潜在的可怕潜力。关于相关费用的发现将有助于定义身体虐待的范围和影响。 这种创新的方法利用了在NIA的支持和推断中收集的现有数据 虐待儿童和亲密伴侣暴力的研究方法，同时扩大他们。没有其他 作为这项研究的一部分，人们将被预期入学，我们避免许多道德问题 老年人的虐待工作。我们多学科的虐待老年人，虐待和忽视儿童的专家团队， 亲密的伴侣暴力，急诊医学以及统计，卫生经济学和专家 计算机科学具有独特的能力来进行这项研究。以前，我们已经进行了半工作检查 通过联系成人保护服务和 医疗保健数据的警察数据库，这与该提案高度相关。我们也有丰富的经验 在使用Medicare索赔数据和机器学习中进行研究。我们研究的长期目标是 利用虐待老年人对医疗保健使用的更好理解，以提高医疗保健的能力 提供者识别，干预和防止欺诈性欺诈并为政策变更提供信息，以帮助这一脆弱性 人口。