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

预防跌倒定制运动电子策略 (eSTEPS)。

基本信息

批准号：
10238835
负责人：
Patricia C Dykes
金额：
$ 24.38万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10238835
关键词：
Address Adopted Adult Advisory Committees Algorithms American Artificial Intelligence Caring Clinic Clinical Decision Support Systems Clinical Trials Communities Data Decision Making Development Elderly Electronic Health Record Emergency department visit Ensure Exercise Fall prevention Focus Groups Frequencies Goals Guidelines Health Health system Healthcare Home Hospitals Hour Intervention Learning Machine Learning Meta-Analysis Monitor Patient Preferences Patients Persons Prevention Guidelines Prevention program Preventive service Primary Health Care Randomized Randomized Controlled Trials Recommendation Reporting Research Risk Risk Assessment Rural Safety Screening procedure Technology Testing United States Visit base behavior change care providers clinical decision support clinical practice clinically actionable cohort control trial cost design disparity reduction effective intervention effectiveness evaluation efficacy testing evidence base evidence based guidelines exercise prescription exercise program experience fall injury fall risk falls high risk implementation protocol improved individualized prevention machine learning method neighborhood safety older patient patient oriented patient portal predictive modeling primary care setting programs rural area screening smartphone Application treatment as usual usability

项目摘要

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.

抽象的最近的荟萃分析发现，参加适当的预防跌倒锻炼计划对于老年人跌倒风险相对降低 23%，每人跌倒绝对减少 0.20 次每年。许多指南，包括美国预防服务工作组 (USPSTF)，都建议老年人有跌倒风险的成年人被推荐参加适当的跌倒预防锻炼计划（USPSTF B 级）。尽管根据这一证据，许多老年人没有得到适当的转介和支持进行预防跌倒练习，一项研究发现，不到一半的老年人表示与初级保健人员讨论过跌倒事件提供者 (PCP)。居住在农村地区的老年人跌倒的可能性更大，但参与跌倒的可能性较小预防计划。计算技术的进步可以帮助识别有跌倒风险的老年人使用临床决策支持（CDS）系统传播有关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，可以为患者的锻炼计划提供支持或通过患者门户网站上的内容。集成到电子医疗中的实施良好的 CDS 记录（EHR）可以支持处方或推荐有效的策略并让患者参与秋季预防决策，从而将循证指南融入临床实践。长期目标我们的研究计划的目的是通过减少跌倒来提高社区老年人的安全有效的以患者为中心的学习健康系统，称为 eSTEPS（定制运动电子策略）防止跌倒）。通过 eSTEPS，运动算法将集成到 EHR 中，这将触发最佳实践警报 (BPA) 和 Smart Set 在初级保健诊所工作流程中提供可操作的 CDS，并促进对患者使用 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应用程序患者就诊后查看。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来准确地识别有跌倒风险的老年人。然后我们将开发、将CDS实施到电子健康记录中这有助于初级保健提供者和老年患者制定量身定制的跌倒预防锻炼计划。我们将在城乡基层医疗卫生机构开展整群随机对照试验，检验治疗效果 eSTEPS CDS 干预。在广泛采用的 Epic EHR 中开发 eSTEPS CDS 将支持传播针对老年人的证据，重点是农村老年人。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Development and Usability Testing of an Exercise-Based Primary Care Fall Prevention Clinical Decision Support Tool.

基于运动的初级保健跌倒预防临床决策支持工具的开发和可用性测试。

DOI：
发表时间：
2023
期刊：
AMIA ... Annual Symposium proceedings. AMIA Symposium
影响因子：
0
作者：
Tejeda,ChristianJ;Garabedian,PamelaM;Rice,Hannah;Samal,Lipika;Latham,NancyK;Dykes,PatriciaC
通讯作者：
Dykes,PatriciaC