Preventing Future Falls in Older Adult ED Patients: Evaluating the Implementation and Effectiveness of a Novel Automated Screening and Referral Intervention

项目概要/摘要 福尔斯是老年人受伤和死亡的主要创伤原因。美国应急 每年有超过300万的跌倒受害者，但他们在原发性或继发性跌倒中的作用很小 预防艾德是一个理想的网站，以确定患者在未来的福尔斯风险，但在这种设置预防 不能以牺牲艾德的主要使命为代价来实施护理：提供紧急护理 在时间紧迫的环境中。随着人口老龄化，艾德继续扩大其作为主要 提供急性计划外护理的场所，迫切需要创建可扩展的干预措施，以评估 老年人跌倒风险，并将他们与出院后适当的风险降低干预措施联系起来，而不增加 增加护士或医生的工作量。 通过AHRQ K 08，我们的研究团队已经开发并验证了一种创新的自动筛选， 跌倒风险的转诊干预。这种干预利用现有数据来选择和连接患者， 在艾德就诊后提供适当的一级和二级预防服务，而不会增加护士的负担， 医生的工作量。这种干预措施的特点是智能使用自动化进行筛查和转诊任务 保持医生的决策自主权，以及根据诊所调整转诊率的独特能力 空房的这种干预措施的特点是智能使用自动化进行筛查和转诊任务维护 医生决策自主权，以及根据诊所可用性调整转诊率的独特能力。 根据我们的工作，UW Health目前正在试行干预措施，并承诺在2015年实施。 三个不同的艾德部位。本研究将调整干预措施，以便在其他研究中心实施， 调查这三个国家的自动筛选和转诊流程的实施情况和有效性 ED通过三个具体目标：1）调整自动筛选和转诊干预的设计， 实施在三个不同的艾德设置，使用人为因素的方法。2)测试 对已完成转诊至多学科跌倒预防中心的患者进行自动筛查和转诊干预 诊所和伤害性福尔斯率使用在实施过程中产生的EHR数据。3)评估执行情况 在三个不同的艾德站点使用混合方法的自动筛选和转诊干预。 这项赠款建议建立在我们以前的创新工作，开发CDS和风险- 分层算法，以提高向老年艾德患者提供护理的质量和安全性。我们将 通过演示，满足对降低艾德跌倒风险的可扩展策略的迫切和日益增长的需求 我们的新方法在一项跨越不同医院类型和患者人群的研究中的有效性。 此外，从这项工作中获得的知识将为其他可以从自动化中受益的用例提供信息。 艾德及其他领域的风险分层和护理协调。

PROJECT SUMMARY/ABSTRACT Falls are the leading traumatic cause of both injury and death among older adults. American emergency departments (EDs) see over 3 million fall victims yearly, yet they play little role in primary or secondary fall prevention. The ED is an ideal site to identify patients at risk of future falls, however in this setting preventive care cannot be implemented at the expense of the primary mission of the ED: the provision of emergency care in a time-pressured environment. As the population ages, and the ED continues to expand its role as the primary site for delivery of acute unscheduled care, there is an urgent need to create a scalable intervention to assess older adults for fall risk and link them to appropriate risk reduction interventions after discharge without adding additional workload for nurses or physicians. Through an AHRQ K08, our study team has developed and validated an innovative automated screening and referral intervention for fall risk. This intervention harnesses existing data to select and connect patients to appropriate primary and secondary prevention services after ED visits without adding burden to nurse or physician workloads. This intervention features smart use of automation for screening and referral tasks maintaining physician decision autonomy, as well as the unique ability to adjust referral rates based on clinic availability. This intervention features smart use of automation for screening and referral tasks maintaining physician decision autonomy, as well as the unique ability to adjust referral rates based on clinic availability. Based on our work, UW Health is currently piloting the intervention, and has committed to implementing it at three diverse ED sites. This study will adapt the intervention for implementation at additional sites, and investigates the implementation and effectiveness of the automated screening and referral process in all three EDs through three specific aims: 1) Adapt the design of an automated screening and referral intervention for implementation in three diverse ED settings, using a human factors approach. 2) Test the effectiveness of the automated screening and referral intervention on both completed referrals to a multidisciplinary fall prevention clinic and rates of injurious falls using EHR data generated during implementation. 3) Evaluate implementation of the automated screening and referral intervention in three diverse ED sites using a mixed methods approach. This grant proposal builds upon our previous innovative work developing both CDS and risk- stratification algorithms to improve the quality and safety of care delivered to older adult ED patients. We will address the urgent and growing need for a scalable strategy for fall risk reduction from the ED by demonstrating the effectiveness of our novel approach in a study spanning diverse hospital types and patient populations. Furthermore, knowledge gained from this work will inform other use cases which could benefit from automated risk-stratification and care coordination in the ED and beyond.