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

用于减少认知障碍患者家中频繁拨打 911 跌倒电话的变革性解决方案

• 批准号: 10339728
• 负责人: Carmen Quatman
• 金额: $ 24.2万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10339728
• 关键词: 911 call; Address; Adoption; Caring; Cessation of life; Clinics and Hospitals; Cognition; Cognitive; Communities; Data; Early identification; Elderly; Emergency Situation; Emergency medical service; Engineering; Environment; Evaluation; Event; Fall prevention; Family; Focus Groups; Future; Goals; Health Care Costs; Health Personnel; Home; Home environment; Home visitation; Hospitals; Impaired cognition; Individual; Injury; Intake; Interview; Lead; Lifting; Machine Learning; Maintenance; Medical; Methods; Modeling; Nature; Outcome; Paramedical Personnel; Patients; Phenotype; Predictive Analytics; Prevention; Prevention strategy; Protocols documentation; Provider; Public Health; Recurrence; Resources; Risk; Risk Factors; Science; System; Time; United States; Visit; Work; base; community based participatory research; cost; experience; fall risk; falls; follow-up; frailty; high risk; implementation strategy; implementation trial; improved; innovation; machine learning algorithm; older patient; patient stratification; predictive modeling; prognostic value; risk stratification; success; tool

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.

项目摘要 全球老年人福尔斯人数激增，几乎影响到世界各地的每个家庭。数百万 在美国，每年都有老年人摔倒，导致灾难性的伤害、死亡和医疗保健费用的飙升。 成本在过去的十年里，911秋季电话增加了两倍，而在跌倒后送往医院的交通费用增加了两倍。 显著下降。相反，911越来越多地用于提升辅助（不会导致运输的福尔斯）。 紧急医疗服务的部署，以电梯协助转移护理从更高的紧急情况和成本 在美国每年超过2亿美元。有一个潜在的强大但未充分利用 如果我们利用跌倒事件的隐藏机会，例如不会导致灾难性事故的提升辅助， 后果，启动预防战略。这项研究旨在制定一个可扩展的战略， 识别福尔斯高危人群，启动预防方案。我们假设一个系统的 911秋季呼叫摄入量具有更广泛的脆弱概念，脆弱和认知+环境（FaCE）， 说明老年人跌倒风险的复合和级联性质。在这个项目完成后 一个可扩展的机器学习模型，它结合了FaCE因素来预测911在福尔斯的高利用率， 发展。此外，我们还将描述采用、实施和 对有FaCE风险因素的患者在家中维持跌倒预防策略。该项目将利用 融合了系统科学和基于社区的参与性研究方法和最先进的技术 预测分析来阐明福尔斯的FaCE，开发可扩展的跌倒预防解决方案， 在全国范围内实施，并通知更大规模的实施试验，使用911秋季电话，以激活有效的 家庭预防跌倒策略。