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

预防跌倒定制运动电子策略 (eSTEPS)。

• 批准号: 10689265
• 负责人: Patricia C Dykes
• 金额: $ 48.09万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10689265
• 关键词: Address; Adopted; Adult; Algorithms; American; Artificial Intelligence; Caring; Clinical Decision Support Systems; Clinical Trials; Communities; Data; Decision Making; Development; Disparity; Elderly; Electronic Health Record; Electronics; Emergency department visit; Ensure; Exercise; Exercise Test; Fall prevention; Focus Groups; Frequencies; Goals; Guidelines; Health; Health system; Healthcare; Home; Hour; Intervention; Learning; Length of Stay; Machine Learning; Meta-Analysis; Monitor; Patient Preferences; Patients; Persons; Populations at Risk; Prevention Guidelines; Prevention program; Primary Care; Randomized, Controlled Trials; Recommendation; Reporting; Research; Risk; Risk Assessment; Risk Reduction; Rural; Safety; Screening procedure; Technology; Testing; United States; United States Preventative Services Task Force; Visit; behavior change; clinical decision support; clinical practice; clinically actionable; cohort; 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 engagement; patient oriented; patient portal; predictive modeling; primary care clinic; primary care practice; primary care provider; primary care setting; programs; rural area; screening; smartphone application; treatment as usual; usability

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.

摘要 最近的荟萃分析发现，参加适当的预防跌倒演习计划 老年人跌倒的风险相对减少23%，每人绝对减少0.20次跌倒 每年。包括美国预防服务工作组(USPSTF)在内的许多指南建议，较老的 有跌倒风险的成年人会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对预防跌倒练习的支持， 一项研究发现，只有不到一半的老年人报告与他们的初级保健讨论他们的跌倒 提供商(PCP)。生活在农村地区的老年人更有可能跌倒，但参与跌倒的可能性较小 预防计划。计算机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。 患者可以通过通过PCP分发的以患者为中心的App来支持他们的锻炼计划 或者通过他们的患者门户上的内容。集成到电子健康中的实施良好的CDS 记录(EHR)可以支持处方或建议有效的策略，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。长期目标 我们研究计划的一部分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(定制锻炼的电子策略 防止跌倒)。使用ESteps，锻炼算法将集成到EHR中，这将触发Best Practice Alert(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以确保循证建议是根据患者的喜好量身定做的。 由此产生的预防跌倒运动护理计划将作为笔记发送到EHR和面向患者的应用程序，以便 就诊后要查看的病人。 在这份提案中，我们将使用传统的跌倒风险筛选和机器学习方法来准确地 找出有跌倒风险的老年人。然后我们将开发CDS，将其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做的预防跌倒锻炼计划。我们会 在城乡初级保健诊所进行整群随机对照试验，以测试该方案的疗效 避免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。