Modelling Human Performance: The Key to fall prevention
Abstract
Introduction
There is little evidence supporting the efficacy of most fall prevention interventions in hospitals. Current interventions rely on education of patients, which has limited use for those with cognitive impairment. Technology has the potential to support the prevention of accidental inpatient falls (AIF) but results from prior studies are inconclusive. This study aimed to gain insights from healthcare professionals (HCPs) about managing AIF in secondary care.
Method
A multi-site, qualitative, simulation study with HCPs was implemented to explore their experiences of AIF. Purposive sampling across 3 NHS Trusts recruited 51 HCPs to 18 simulations and 9 focus groups. Thematic analysis was undertaken to understand barriers to AIF prevention. The Human Information Processing (HIP) Model was utilised to interpret the data, explaining how humans perceive, process, store and respond to information.
Results
Examination of the data using the HIP tells us that a well-designed system of work using technology improves nurses’ situation awareness. Allowing decisions to be made at a subconscious level, whilst conserving cognitive resources. Whilst technology delivers cues and feedback to users it also has the potential to relieve attention demand and provide insights to workable user centred solutions for fall prevention. Leadership, systems and culture are influential but a technology-based resource may support system design and result in culture change that drives patient safety.
Conclusion
From the perspectives of HCPs decision making regarding AIF is complex. Technology may assist with situation awareness and help highlight where cognitive load is exceeding the capacity to cope. HIP was found to be a useful tool to break down complex decision scenarios and understand technology design features contributing to AIF prevention. By improving visibility of high risk situations, technology can help timely decision making.
Comments
Great presentation, thank…
Great presentation, thank you for sharing! It's a really interesting finding that technology could be used to highlight successes which are sometimes quite difficult to evidence. I wondered if you had any thoughts on whether/how these systems could feed into the NHS Patient Safety Incident Response Framework?
Reply to Frances question
Thanks Fran, Yes. Sadly the most common fall prevention alarms in hospitals are not regulated by DCB 0160 or DCB 0129 guidance from NHS Digital. These standards ensure digital health systems are acceptably safe for patient care and do not cause harm. They are mandated under the Health and Social Care Act 2012. However hospitals are often using the cheapest and least well developed products that do not conform to this guidance. In some of the more reliable systems available they produce digital summaries (in the form of csv reports) that can easily be converted into daily or more frequent reports that help inform the system manager (HCP in charge) about staff behaviour ie response times and number of alarms. Filtering false alarms highlighted by staff (where they have not silenced the alarm for personal care or mobility) each alarm answered by a member of staff is a successful fall prevention incident. These reports can be used to help record successful fall prevention. There will be a very small % of faall scenarios where a fall is not prevented but for those that are not, a pattern of alarms prior to the fall and response times can be helpful to validate staff experiences that inform the investigation. Response times are very comforting for patients relatives when they can be informed, for example, that a member of staff was with the patient in 6 seconds, also preventing long lies. People understand that if a patient fell in that time frame it would be a challenge to prevent and often relay experiences of falls at home. In addition patient sleeping patterns and agitation patterns can be analysed to help inform clinical discussions and discharge plans. PSIRF is based on Safety Engineering Initiatve for patient safety (SEIPS) which is a Human Factors model to inform healthcare planning. It encourages the development of systems of care and all fall prevention alarms need a clear and useable system to define practice. The human processing information model demonsrtates how humans respond and react to these alarms and helps develop systems as staff and the equipment work together to personalise use of fall alarms to individual patients. Frontline patient facing clinicians need help to improve situation awareness in the ward environment and quality fall prevention technology can provide this.