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

项目摘要/摘要 跌倒是老年人受伤和死亡的主要创伤原因。美国紧急情况 部门(ED)每年有超过300万名跌倒受害者，但他们在原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，但在这种情况下，预防 实施护理不能以牺牲急诊科的主要使命：提供紧急护理为代价 在时间紧迫的环境中。随着人口老化，教育署作为主要教育机构的角色不断扩大 提供急性计划外护理的地点，迫切需要创建可扩展的干预措施来评估 老年人的跌倒风险，并将他们与出院后适当的降低风险干预措施联系起来，而不增加 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创新的自动筛查和 针对跌倒风险的转介干预。这种干预利用现有数据来选择患者并将其连接到 急诊科就诊后适当的一级和二级预防服务，而不增加护士或 医生的工作量。这种干预的特点是巧妙地使用自动化进行筛选和推荐任务 保持医生决策自主权，以及根据临床调整转诊率的独特能力 可用性。这种干预的特点是巧妙地使用自动化来进行筛选和推荐任务维护 医生决策的自主性，以及根据临床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 三个不同的ED站点。这项研究将调整干预措施，以便在更多地点实施，以及 调查自动筛选和转介流程在所有三个国家的实施情况和有效性 EDS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，在三个不同的教育部门环境中实施。2)测试该方案的有效性 对两个完成的多学科预防跌倒转诊进行自动筛查和转诊干预 使用在实施过程中产生的电子健康记录数据的临床和伤害性跌倒比率。3)评估执行情况 使用混合方法对三个不同的ED站点进行自动筛查和转诊干预。 这项赠款提案建立在我们之前开发CDS和Risk的创新工作的基础上- 分层算法，以提高提供给老年ED患者的护理质量和安全性。我们会 满足急切且日益增长的需求，以减少ED的跌倒风险的可扩展策略，通过展示 我们的新方法在一项跨越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可能受益于自动化的用例提供信息 急诊科及以后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.