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.
There is substantial evidence that conducting multifactorial fall risk assessments and developing personalized prevention plans in primary care settings can reduce falls, but many barriers exist to integrating this evidence into practice. The US Preventive Service Task Force recommends fall prevention exercises for community- dwelling adults who are at increased risk for falls, however less than half of older persons report discussing their falls with their primary care providers (PCPs). The goal of this project is to develop, test, and evaluate shareable, standards-based fall prevention clinical decision support, eSTEPS (electronic Strategies for Tailored Exercise to Prevent FallS) that can used by primary care providers to facilitate personalized activity and exercise prescriptions and reduce falls in at risk older adults.
