A falls risk stratification algorithm and virtual falls service model for community aged care clients
Abstract
Introduction
Best-practice guidelines recommend falls risk stratification algorithms to identify community aged care clients at risk of falls, and determine who could benefit from Allied Health Professional (AHP) input. However, implementation is constrained by logistical challenges of in-home service delivery. Virtual care delivery offers a promising solution. This study aimed to develop and test a falls risk stratification algorithm and a virtual, AHP-led falls service model for community aged care.
Methods
We conducted a four-phase, mixed methods study. Phase 1 scoping reviews of falls risk screening questions and assessments, along with phase 2 focus group feedback from AHPs and care staff were consolidated in phase 3 to develop a falls risk stratification algorithm and virtual falls service model. Phase 4 tested the algorithm and model with one Australian community aged care provider and included surveys to evaluate user acceptance and perceived utility.
Results
Through phases 1 to 3 the World Falls Guideline’s algorithm was adapted to a question-based tool, and a template of suitable falls assessments that could be delivered virtually was developed. In phase 4 participants included 13 clients (mean age 81 [SD 7.87] years, 69% females), 4 AHPs and 16 care staff. Clients were screened and 2 were classified as low priority, 2 as intermediate priority, and 9 as high priority for early falls prevention input. The most common virtual AHP-led assessment recommendation was provision of activities of daily living equipment. Surveys were completed by five clients, 13 care staff and four AHPs. The effectiveness and usability of the falls risk stratification algorithm and virtual falls service model were rated as good to very good by all clients and 71% of staff.
Conclusion
This evidence-based falls risk stratification algorithm and virtual, AHP-led falls service model showed promising acceptance and utility among community aged care clients and staff.