Preventing Future Falls among Older Adults Presenting to the Emergency Department

项目总结/摘要 美国老年人依赖急诊科（艾德）进行急性、不定期的护理。不幸的是，许多老年人 成年人在艾德就诊后的结果很差，这表明这些就诊代表了错过 有机会识别高风险患者并进行干预，以改善向门诊治疗的过渡。特别是， 在老年人中严重的福尔斯跌倒是一个严重且可预防的问题。计划外艾德访视提供了 有机会识别老年人福尔斯的风险高于一般初级保健人群， 跌倒风险因素可以修改，从而为初级保健之外的跌倒筛查提供理想的附加设置。 艾德提倡这种筛查，但由于提供者的时间限制，筛查干预往往失败 在紧急情况下。随着电子健康记录（EHR）系统的发展，计算机化的决策支持 提供了支持跌倒筛查的可能性，同时减少了提供者的负担。本建议的目的是确定 成人在未来的高风险福尔斯和改善他们的照顾，无论是在他们的艾德访问和出院后。 作为一名医生科学家，我的目标是领导一个独立的研究计划，以改善过渡到门诊 老年人在艾德就诊后的护理。这份为期5年的提案将通过提供必要的 在实施科学以及基于信息的干预措施方面提供支持和培训。我有一个独特的 具有工程学、急救医学和卫生服务研究的背景，并准备好 成功完成提案。我将得到一个专家导师团队的帮助， 资源和优秀的环境进行研究建议和过渡到一个独立的 在R 01支持下进行调查。 建议的目的是：1）比较电子健康档案为基础的数据提取，在未来的门诊筛查的人 跌倒风险，2）使用EHR中提供的数据，开发一种预测算法， 对艾德患者在未来6个月内发生重大福尔斯跌倒的风险进行分层，以及3）设计并试行临床决策 支持干预，以识别福尔斯高危老年人，并改善他们在艾德和之后的护理 放电这些目标将通过创建和分析连接EHR和索赔的数据库来实现 数据，包括通过自然语言处理获得的新元素，通过利用机器学习， 除了传统的统计技术之外，还通过在一个卫生系统中制定和试行干预措施。

PROJECT SUMMARY/ABSTRACT Older Americans rely on the emergency department (ED) for acute, unscheduled care. Unfortunately, many older adults experience poor outcomes after ED visits, suggesting that these encounters represent missed opportunities to identify high risk patients and intervene to improve the transition to outpatient care. In particular, significant falls among older adults are a serious and preventable problem. Unscheduled ED visits offer an opportunity to identify older adults at higher risk of falls than the general primary care population at a time when fall risk factors can be modified, and thus offer an ideal additional setting for fall screening beyond primary care. Such screening is advocated in the ED, but screening interventions often fail due to time constraints of providers in the emergency setting. As electronic health record (EHR) systems evolve, computerized decision support offers the potential to support fall screening with less provider burden. The objective of this proposal is to identify adults at high risk for future falls and improve their care both during their ED visit and after discharge. As a physician scientist, my goal is to lead an independent research program to improve transitions to outpatient care following ED visits for older adults. This 5-year proposal will advance these goals by providing the necessary support and training in implementation science as well as informatics-based interventions. I have a unique background in engineering, emergency medicine, and health services research and am well prepared to successfully complete the proposal. I will be aided by a team of expert mentors at an institution with substantial resources and an outstanding environment to conduct the research proposed and transition to an independent investigator with R01 support. The proposed aims are to: 1) Compare EHR-based data extraction to in-person screening of future outpatient fall risk, 2) Using data available in the EHR at the time of presentation, develop a predictive algorithm to risk- stratify ED patients for risk of significant falls in the next 6 months, and 3) Design and pilot a clinical decision support intervention to identify older adults at high risk of falls and improve their care both in the ED and after discharge. These aims will be accomplished by creating and analyzing a database linking the EHR and claims data, incorporating novel elements derived by natural language processing, by utilizing machine learning in addition to traditional statistical techniques, and by developing and piloting an intervention in one health system.