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

基本信息

批准号：
10493369
负责人：
Carmen Quatman
金额：
$ 24.24万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10493369
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 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 machine learning model older patient patient stratification predictive modeling prognostic value risk stratification success tool

项目摘要

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.

项目概要全球老年人跌倒人数激增，几乎影响到世界各地的每个家庭。数百万美国每年都有老年人跌倒，导致灾难性的伤害、死亡和医疗费用的飙升成本。在过去十年中，911 跌倒电话增加了两倍，而跌倒后送往医院的交通费用也增加了显着下降。相反，911 越来越多地用于升降辅助（不会导致运输的跌倒）。部署电梯辅助紧急医疗服务可以将护理从更严重的紧急情况和成本中转移出来美国每年收入超过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举升辅助），则可以找到解决方案的后果，以启动预防策略。本研究旨在为早期制定可扩展的策略识别跌倒高风险人群并启动预防解决方案。我们假设一个系统的 911 秋季电话接听具有更广泛的虚弱概念、虚弱与认知+环境 (FaCE)，会更好解释老年人跌倒风险的复合和级联性质。在这个项目完成时一个可扩展的机器学习模型，该模型结合了 FaCE 因素来预测跌倒时 911 的高利用率得到开发。此外，我们还将描述采用、实施和实施的障碍和促进因素。针对具有 FaCE 危险因素的患者，维持家中跌倒预防策略。该项目将利用系统科学和基于社区的参与性研究方法以及最先进的技术的融合预测分析来阐明跌倒的 FaCE，开发可扩展的跌倒预防解决方案全国范围内实施，并通知开展更大规模的试点，利用911电话激活有效家庭跌倒预防策略。