Evidence-based proactive risk stratification for population-level falls prevention – results of a pilot implementation study
Abstract
Introduction
Falls are a crucially important issue for older adults with one-third 65years+ falling each year. In Greater Manchester (GM) fall rates are above the national average in 6/10 localities, leading to the highest inpatient spend in England on hip/thigh injuries. However, falls can be prevented via strength and balance exercises. World guidelines recommend older people be screened for fall risk when they visit a doctor, and based on their risk, referred to appropriate services; but there is currently no easy way to do this. A new tool, eFalls, automatically calculates fall risk from GP records. This pilot uses eFalls to identify older adults at intermediate fall risk (10-25%) in one Primary Care Network (PCN) in GM and refers them to community exercise programmes proven to reduce fall risk.
Method
A mixed-method evaluation of the pilot was undertaken incorporating; routine electronic health data of intermediate risk group identified via eFalls; service programme delivery data (e.g. adherence/concerns-about-falling); participant acceptability via Theoretical Framework of Acceptability (TFA) questionnaire; semi-structured interviews with participating older adults.
Results
1158 adults 65years+ at intermediate risk were identified via eFalls. After exclusions, 740 patients were contacted via PCN. 198 accepted and 160 attended face-to-face appointments to discuss the intervention. 125 patients were referred to community strength/balance classes; 54 patients started/completed at least one session. Acceptability of the intervention across all TFA constructs was high. Thematic analysis of 21 qualitative interviews offer insight into the implementation/uptake/barriers/facilitators/acceptability of the eFalls pilot.
Conclusion(s)
A barrier to population-level approaches to falls prevention has been the absence of a risk stratification tool to automate patient identification without resource-intensive clinical assessment. This pilot implementation study offers insight into how we can begin to transform falls prevention services from reactive to proactive via community-based preventative care supported through a data-driven, digital approach.
Comments
Interesting and well-presented poster
Really interesting study and a nice example of how falls risk prediction can be translated into a real-world prevention programme. I particularly liked the mixed-methods approach, as the qualitative findings helped explain why people engaged with the intervention and what aspects were most important to participants.
A very important topic
I find this topic particularly important as this is a very good solution to prevent falls before they even happen. I would be very interested to see how this works at scale in GP practice, not only with GPs but with ACPs and FCPs too.