A Pragmatic Approach to Developing Falls Prevention Interventions in a Hospital Setting

Problem

Despite evidence that some falls prevention interventions can work in hospitals, translation of evidence is difficult, possibly due to differences in settings, patient profiles and falls rates; high patient turnover, the perception that everything is being done, availability of resources and focus on screening rather than intervention. This project has developed and trialled a context specific falls prevention model to extract local data and select and implement interventions based on this data.

Design

Develop context specific falls prevention model

10 week data collection at each site about falls and fallers, falls prevention knowledge, staff work practices, patients’ perception of falls risk and an environmental audit

Data analysed and findings used to generate optimal interventions

Site-specific interventions implemented and evaluated by frontline staff for 10 weeks.

Themes & Key Findings

Patients do not think they are at risk of falling whilst in hospital because nurses will keep them safe

A roadmap for use in complex systems can achieve falls reduction in the hospital setting

Staff can be engaged in collecting local data and in selecting and implementing strategies for their unit/ward based on that local data

Falls prevention should be considered cyclical and ongoing in line with the local context.

Results

77% of AMU patients and 60% of Ward 10 patients did not think they were risk of falling whilst in hospital

Data was used by the frontline staff at each site to select and implement optimal interventions

Interventions were very different at each site despite apparent similarities between wards

AMU—focused on risk identification by patients and staff

Ward 10—focussed on increasing supervision for cognitively impaired patients

Both wards had a reduction in falls rates following implementation of the intervention

AMU— 14.2 falls to 4.5 falls per 1000 bed days

Ward 10—12.9 falls to 4.5 falls per 1000 bed days

Completed July 2017