Transformative Solutions for Reducing Frequent 911 Fall Calls in the Homes of Patients with Cognitive Impairments

项目总结 在全球范围内，老年人跌倒的热潮影响到了世界上几乎每个家庭。数以百万计 在美国，老年人每年都会摔倒，导致灾难性的伤害、死亡和医疗保健飙升 成本。在过去的十年里，911秋季电话增加了两倍，而跌倒后送往医院的交通费也增加了 显著下降。取而代之的是，911越来越多地用于电梯辅助(不会导致运输的跌倒)。 为升降机辅助部署紧急医疗服务可以将护理从更高敏锐度的紧急情况和成本中转移出来 在美国，每年超过2亿美元。有一个潜在的强大但未得到充分利用的 如果我们利用坠落事件的隐藏机会，例如不会导致灾难性的托举助攻，就可以解决问题 后果，以启动预防战略。这项研究旨在为早期开发一个可扩展的策略 识别跌倒高危人群并启动预防解决方案。我们假设一个系统化的 对脆弱、脆弱和认知环境(Face)有更广泛概念的911秋季呼叫摄入会更好 说明老年人跌倒风险的复合和连锁性质。在这个项目完成时 一个可扩展的机器学习模型，它结合了面部因素来预测瀑布的高利用率911将 被开发出来。此外，我们还将描述采用、实施和 针对有面部危险因素的患者在家中维护预防跌倒的策略。该项目将利用 融合了系统科学和基于社区的参与式研究方法和最新技术 预测性分析以阐明跌倒的表面，开发可扩展的跌倒预防解决方案 在全国范围内实施，并通知使用911秋季呼叫激活有效的更大规模的实施试验 居家预防跌倒的策略。

PROJECT SUMMARY There is a global upsurge of falls in older adults that impacts nearly every family across the world. Millions of older adults fall each year in the United States, leading to catastrophic injuries, deaths and soaring healthcare costs. Over the last decade, 911 fall calls have tripled while transport rates to the hospital after a fall have significantly decreased. Instead, 911 is increasingly used for lift assists (falls that do not result in transport). Deployment of emergency medical services for lift assists diverts care from higher acuity emergencies and costs more than 200 million dollars annually in the United States. There is a potentially powerful yet underutilized solution if we leverage the hidden opportunities of fall events, such as lift assists that do not result in catastrophic consequences, to activate prevention strategies. This study aims to develop a scalable strategy for early identification of individuals at high risk of falls and activate prevention solutions. We hypothesize that a systematic 911 fall call intake which has a broader concept of frailty, Frailty And Cognition+Environment (FaCE), will better account for the compounding and cascading nature of fall risks in older adults. At the completion of this project a scalable machine learning model which incorporates FaCE factors to predict high utilization of 911 for falls will be developed. In addition, we will characterize barriers and facilitators for adoption, implementation, and maintenance of fall prevention strategies in the home for patients with FaCE risk factors. This project will utilize a blend of systems science and community-based participatory research approaches and state of the art predictive analytics to elucidate the FaCE of falls, develop a scalable fall prevention solution that can be implemented nationwide and inform a larger-scale implementation trial for using 911 fall calls to activate effective fall prevention strategies in homes.