Research Objectives: To implement and evaluate a best practice model for reducing hospital falls that incorporates the UK clinical guideline to cease using a traditional fall risk prediction tool (FRAT) for all patients aged 65 years or older; and patients aged 50-64 years who are judged by a clinician to be at higher risk of falling because of an underlying condition, compared with usual care.Design: Cluster randomised controlled clinical trial in 10 Australian hospitals.Setting: De-identified data were collected from all adults in 10 major Australian hospitals, in Victoria, Queensland and Western Australia over a 3-month period in 2019.Participants: All adult patients in 10 Australian hospitals.Interventions: For the control group hospitals, FRAT screening to detect patients at high falls risk continued as usual. For the experimental group hospitals, we created a new screening tool that removed the FRAT screening component at the top of the existing form and associated summary scores and numerical risk ratings.Main Outcome Measures: The primary outcome was fall rates (per 1000 bed days), from the hospital incident management system.Results: The mean fall rates in the control (5 hospitals) and experimental (5 hospitals) observed over the 3 months of the trial were as follows: (i) Control group mean (95%) = 3.95 (2.62, 5.28) falls /1000 bed days; (ii) Experimental group mean (95%) = 3.11 (2.37, 3.85) falls /1000 bed days. The incidence rate ratio (IRR) of falls in the control versus experimental hospitals over the 3 observed months when analysed using generalised linear mixed models (negative binomial family) was IRR (95%CI) =0.809 (0.538, 1.217) . Note: IRR < 1.00 represents lower falls rate in experimental condition.Conclusions: The experimental (newer) form was not inferior to the control (older) form for screening falls risk and reducing falls in hospitals. Given that the traditional FRAT was time-consuming to complete, it is recommended that disinvestment be implemented and clinicians trained to use their clinical decision making to quickly detect patients most at risk of falling, and put in place mitigation strategies.
|Number of pages||1|
|Journal||Archives of Physical Medicine and Rehabilitation|
|Publication status||Published - Nov 2020|
|Event||American Congress of Rehabilitation Medicine Annual Conference 2020 - |
Duration: 19 Oct 2020 → 24 Oct 2020
Conference number: 97th