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 跌倒是老年人受伤和死亡的主要原因。美国紧急状态 部门 (ED) 每年接待超过 300 万跌倒受害者，但他们在初发或继发跌倒中扮演的角色很少 预防。急诊室是识别未来有跌倒风险的患者的理想场所，但是在这种情况下，预防措施 护理的实施不能以牺牲急诊室的主要使命为代价：提供紧急护理 在时间紧迫的环境中。随着人口老龄化，急诊科不断扩大其作为主要医疗机构的作用 作为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来评估 老年人跌倒风险，并在出院后将他们与适当的风险降低干预措施联系起来，而无需添加 护士或医生的额外工作量。 通过 AHRQ K08，我们的研究团队开发并验证了一种创新的自动筛选和 针对跌倒风险的转诊干预。该干预措施利用现有数据来选择患者并将其连接到 急诊就诊后提供适当的一级和二级预防服务，不会增加护士或护士的负担 医生的工作量。这种干预措施的特点是巧妙地使用自动化来执行筛选和转诊任务 保持医生决策自主权，以及根据临床情况调整转诊率的独特能力 可用性。这种干预措施的特点是巧妙地使用自动化来维护筛选和转诊任务 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，威斯康星大学健康中心目前正在试行该干预措施，并承诺在 三个不同的 ED 站点。这项研究将调整干预措施以在其他地点实施，并且 调查所有三个地区自动筛选和转介流程的实施情况和有效性 急诊室通过三个具体目标：1）调整自动筛查和转诊干预的设计 使用人为因素方法在三个不同的急诊室环境中实施。 2）测试有效性 对已完成的多学科跌倒预防转诊进行自动筛查和转诊干预 使用实施过程中生成的 EHR 数据进行诊所和跌倒伤害率。 3）评估实施情况 使用混合方法在三个不同的急诊中心进行自动筛查和转诊干预。 该拨款提案建立在我们之前开发 CDS 和风险的创新工作的基础上 分层算法可提高老年 ED 患者护理的质量和安全性。我们会 通过证明，满足急诊室对降低跌倒风险的可扩展策略的迫切且日益增长的需求 我们的新方法在一项涵盖不同医院类型和患者群体的研究中的有效性。 此外，从这项工作中获得的知识将为其他用例提供信息，这些用例可以从自动化中受益 急诊室及其他地方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.