Technology Supported Improvement, Management and Prevention of Accidental Falls in Hospitals

To discover how work practice and behaviour adheres or varies from policy

Data will be gathered by observing staff in their usual work environment over a 12 month period to determine how fall prevention practice is shaped by technology. Up to 200 staff will be observed by the researcher on various shifts. Hierarchical task analysis will be used to compare how work varies from the policy/protocol and any workarounds that have been developed (Work as done will be compared to work as imagined). Adherence to policy/protocol or variance from policy/protocol will be recorded and collated as a count of deviations.

To collate evidence of the context of falls in a context log to identify potential contributory factors to accidental falls in hospital.

A thematic analysis of accidental fall incident forms will be undertaken comparing contextual details at the time of the accidental fall to identify common themes. Previously uncollated facts such as the exact location of fall (bedside or bathroom), lighting at the time and ability to alter the lighting (automatic switch on /off versus dimmer switch), footwear (own or provided in hospital) and whether walking aids in place or not etc. These will be compared before the implementation of fall prevention alarms versus after implementation to see if the implementation of fall prevention alarms has impacted on falls in any specific contextual category. This will identify if there is a specific context in which fall prevention alarms prevents falls. This will allow more accurate measurement of success of technology as there may be a specific type of fall that can be prevented by the technology.

To discover how accidental falls are being measured and recorded in hospital by observation and comparing live data measurement against standard data measurement.

The current way of calculating falls/1000 bed days is flawed. Occupancy rate and number of admissions are not considered. The outcome will compare standard falls/1000 OBD's versus a contextual measurement that better represents outcomes. Instead of taking average hospital occupancy data the calculation of the number of falls/1000 occupied bed days will be calculated using actual data from ward level occupancy. If the hospital uses an electronically generated occupancy measurement it can give falsely high measurements of falls on a specific ward as it reports empty beds at midnight. These empty beds at midnight are often an electronic delayed transfer rather than actual empty beds. measurement according to stafff reported figures will be compared.

To identify through thematic analysis using Nvivo 15 task critical attributes and user requirements for future fall prevention technology design

The observation of the use of technology in practice will provide themes where practice is impeded or enhanced by the use of technology. This will be identified by the themes identified during observation of staff. These themes will be analysed and recommendations for future fall prevention alarms will be deduced.

Staff interviews

Staff will be questioned during their shift with 'go along questions' (quick questions in between work tasks) to record their rationale for completing fall prevention tasks in the way they have. Answers will be anonymously recorded to provide themes to be analysed using Nvivo 15.