Fall Clinical Trial
Official title:
Step Wedge Cluster Randomised Trial of a Service Intervention to Reduce Falls in Hospital
The head of nursing at University Hospital Coventry and Warwickshire (UHCW) plans to roll
out an intervention across groups of hospital wards over the next four months. The
intervention is designed to reduce falls as part of quality improvement for the hospital.
However, the head of nursing has asked the University of Warwick to help with the scientific
evaluation of the intervention - to find out whether and to what extent falls on the wards
are reduced by the intervention. The University of Warwick will have two functions:
1. To analyse data on falls to see if there has been a statistically significant drop in
fall rates before and after the intervention has been implemented across these groups
of wards
2. To determine a random order in which the groups of wards receive the intervention as
this will make it easier to distinguish cause and effect.
Falls are the most commonly reported patient safety incident with 240,000 recorded in NHS
hospitals in England each year. The national mean rate of falls per 1000 occupied bed day
(OBDs) is 6.6. The rate of harm per 1000 OBD is 0.19. The head of nursing at UHCW is
responsible for quality and safety in nursing and recognises that falls are a problem at
UHCW. Consequently she plans to implement an intervention (an educational programme for ward
staff) to reduce the potential harm caused by falls, and wishes to know whether the
intervention is effective. For logistical reasons it is not possible to introduce the
intervention to all wards at the same time, so it is necessary to roll the intervention out
across clusters of wards over time. These clusters will be the unit of study. Human Subjects
Protection Review will be exempt as the intervention is taking place at ward level, not the
patient level. The hypothesis is that fall rates will tend to decline after the intervention
is introduced. Standards for prevention of falls have been published by the Healthcare
Quality Improvement Partnership (Royal College of Physicians. National Audit of Inpatient
Falls: audit report 2015. London: RCP, 2015). The intervention will consist of education to
improve compliance with these standards.
NIHR CLAHRC West Midlands were approached to conduct an independent evaluation. However, the
service imperative does not permit any delay in implementation of the intervention. Although
the intervention must not be delayed, it cannot be implemented simultaneously across all
hospital wards; it must be rolled out incrementally. The unit of roll-out is a cluster of
wards. There are nine clusters of wards:
Cluster A:
- Ward 40 - Gerontology - Age
- Ward 20 - Gerontology
- Ward 21 - Gerontology
Cluster B:
- Ward 41 - Stroke
- Ward 42 - Neurology
- Ward 43 - Neurosurgery
Cluster C:
- Ward 30 - Respiratory
- Ward 31 - Medical ward
- Ward 34 - Clinical Haemotology
- Ward 35 - Oncology
Cluster D:
- Ward 50 - Renal
- Ward 52 - Orthopaedics
- Ward 53 - Orthopaedics
Cluster E:
- Ward 10 - Cardiology
- Ward 11 - Cardiothoracic Surgery
Cluster F:
- Cedar Ward - Orthopaedics
- Hoskyn Ward
- Mulberry Ward
- Oak Ward - Rehab
Cluster G:
- Ward 32 - Head & Neck
- Ward 33 - Surgery
- Ward 33 - Gastro
- Ward 33 - Urology
Cluster H:
- Ward 21 - Short-stay - Gen Surgery
- Ward 22 - ECU
- Ward 22 - Surgical Assessment Unit
- Ward 22A - Vascular Surgery
- Ward 23 - Gynaecology Suite
Cluster I:
- Ward 12/CDU - AMU1
- Ward 3 (AMU3)
- Ward 12 - Observation / Assessment Unit (ED)
- Ward 1
- AMU 2
The phased introduction across wards evokes the possibility of step wedge cluster RCT
(Hemming, et al. BMJ. 2015; 350:h391). However, clusters A and B must proceed first, and
clusters H and I must proceed last as these are wards with very short stay. Five clusters (C
to G) are therefore available for randomisation. Once clusters have been randomised to a
given order, there are no foreseeable reasons to change the order. Accordingly, a list of
the five eligible clusters was sent to the CLAHRC WM Director on 17/01/17 and were
randomised independently (by Dr Mark Slater at the Dept of Physics, University of
Birmingham) using Microsoft Excel (each cluster was assigned a random number, the numbers
were then sorted from smallest to largest), as below:
E - F - G - C - D.
The primary outcome is fall rates, and we hypothesise that fall rates will decrease over the
intervention period. Fall rates per 1000 bed days per month range from 0 to 34.48. Fall
rates will be collated monthly over a one year period to provide a median of approximately
six months pre- and six months post-intervention data points. The data will be expressed as
falls per 1000 OBDs and will be harvested from the routine data system. The senior nurse on
duty has a statutory requirement to collect data on falls and enter them on the hospital
computer system. We will record the date at which the intervention team start working with a
new cluster. The rate at which the intervention will be rolled out is uncertain at this
stage.
For analysis, the raw data from the system will be sent to Dr Karla Hemming at the
University of Birmingham for statistical analysis, including adjustment for calendar time.
Primary analysis will be restricted to the five randomised clusters, and secondary analysis
will include the four non-randomised clusters. The evaluation will take account of
correlation within clusters and auto-correlation over time.
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