electronic Strategies for Tailored Exercise to Prevent FallS (eSTEPS).

摘要 最近的荟萃分析发现，参加适当的预防跌倒运动计划， 相对而言，老年人可将福尔斯跌倒的风险降低23%，每人可绝对减少0.20次福尔斯跌倒 每一年。许多指南，包括美国预防服务工作组（USPSTF），建议老年人 有福尔斯跌倒危险的成年人被推荐参加适当的预防跌倒运动计划（USPSTF B级）。尽管 这一证据表明，许多老年人没有得到适当的转介和支持，以防止跌倒的练习， 一项研究发现，不到一半的老年人报告说，他们与他们的初级保健讨论他们的福尔斯 供应商（PCP）。生活在农村地区的老年人更容易摔倒，但不太可能参加秋季运动 预防方案。计算技术的进步可以帮助识别有福尔斯风险的老年人， 使用临床决策支持系统传播关于最有效干预措施的指导。 通过PCP分发的以患者为中心的应用程序，患者可以在其锻炼计划中获得支持 或者通过他们的患者门户网站上的内容。实施良好的CDS，集成到电子健康 记录（EHR）可以支持处方或推荐有效的策略，并在秋季吸引患者 预防决策，从而将循证指南纳入临床实践。远景目标 我们的研究计划的一个重要目的是通过减少福尔斯， 一个有效的以病人为中心的学习健康系统，称为eSTEPS（电子策略为定制的运动， 防止福尔斯）。有了eSTEPS，EHR中将集成一个运动算法， Practice Alert（BPA）和Smart Set可在初级保健诊所工作流程中提供可操作的CDS， 与患者一起使用CDS，以确保基于证据的建议适合患者的偏好。 由此产生的跌倒预防运动护理计划将作为注释发送到EHR，并发送到面向患者的App， 患者在就诊后查看。 在本提案中，我们将使用传统的跌倒风险筛查和机器学习方法， 识别有福尔斯跌倒风险的老年人。然后我们将开发，CDS实施到电子健康记录中 帮助初级保健提供者和老年患者制定量身定制的预防跌倒锻炼计划。我们将 在城市和农村初级保健诊所开展一项随机对照试验，以检验 eSTEPS CDS干预。在广泛采用的Epic EHR中开发eSTEPS CDS将支持 为老年人传播证据，重点是农村老年人。

ABSTRACT Recent meta-analyses have found that participation in the appropriate fall-prevention exercise program for an older adult reduces the risk of falls by 23% in relative terms, for an absolute reduction of 0.20 falls per person per year. Many guidelines, including the US Preventive Service Task Force (USPSTF), recommend that older adults at risk of falls are referred to appropriate fall-prevention exercise programs (USPSTF Level B). Despite this evidence, many older adults do not receive appropriate referrals and support for fall-prevention exercises, with one study finding that less than half of older persons report discussing their falls with their primary care providers (PCPs). Older people living in rural areas are more likely to fall but are less likely to participate in fall prevention programs. Advances in computing technology can help to identify older people at risk of falls and disseminate guidance about the most effective interventions using clinical decision support (CDS) systems. Patients can be supported in their exercise programs through a patient-focused App distributed through the PCP or through content on their patient portal. Well-implemented CDS that is integrated into the electronic health record (EHR) can support prescribing or recommending effective strategies and engaging patients in fall prevention decision-making thus integrating evidence-based guidelines into clinical practice. The long-term goal of our research program is to enhance the safety of community-based older adults by reducing falls through an effective patient-centered learning health system called eSTEPS (electronic Strategies for Tailored Exercise to Prevent FallS). With eSTEPS, an exercise algorithm will be integrated into the EHR which will trigger a Best Practice Alert (BPA) and Smart Set to provide actionable CDS within primary care clinic workflows and facilitate the use of CDS with patients to ensure evidence-based recommendations are tailored to patient preferences. The resulting fall prevention exercise care plan will be sent to the EHR as a note and to a patient-facing App for the patient to view after their visit. In this proposal we will use traditional fall risk screening and machine learning approaches to accurately identify older adults at risk for falls. We will then develop, CDS implemented into the electronic health record that helps primary care providers and older patients develop a tailored fall prevention exercise plan. We will conduct a cluster randomized control trial in urban and rural primary care clinics to test the efficacy of the eSTEPS CDS intervention. Development of the eSTEPS CDS within the widely adopted Epic EHR will support dissemination of evidence for older adults, with a focus on rural elders.