What are we trying to do?

Falls and fall-related injuries are a common and serious problem for older adults. It is estimated between 5-10% of those who fall sustain serious injury and falls cost the NHS around £2.3 billion a year. Falls have psychological and social consequences as well as physical, with increased concerns-about-falling, loss of confidence, activity restriction, and admission to care.

 

Falls can be prevented. There is strong evidence for strength and balance exercise as a prevention strategy. The World Falls Guidelines recommend opportunistic case finding/screening for stratification into low, intermediate and high-risk groups, who are then directed to appropriate services. However, integrating case finding into clinical workflow requires considerable clinician time. This is often unfeasible in the primary care setting and can be a major barrier to implementing evidence-based falls prevention pathways. In the UK, services are mostly aimed at high-risk individuals who have already fallen.

 

An alternative approach for primary prevention is to identify people at intermediate risk by integrating automated falls risk stratification using risk prediction models based on routine electronic health record (EHR) data.

 

Why is this important?

Such a population-based approach aimed at intermediate risk, who have not yet fallen, has the potential to transform prevention services, preventing falls for many older adults and maximising their independence.  How are we doing it?

 

Researchers at the University of Leeds have developed the ‘eFalls’ tool; an algorithm that uses existing primary care electronic patient record data to automatically identify people at risk of a fall in the next 12 months and stratifies them by their risk score.

 

Greater Mancester Combined Authority (GMCA) are currently piloting this approach in one Primary Care Network (PCN) in Greater Manchester (GM).  Using eFalls to identify people over 65 years old at intermediate fall risk (10-25% risk) for referral to suitable evidence-based exercise fall prevention programmes.

 

ARC researchers are undertaking a mixed-method evaluation of this pilot work, this includes:

 

• Analysis of routine data via pseudonymised health care datasets accessed via Secure Data Environment (SDE);
• Intervention acceptability questionnaires undertaken with all participating older adults;
• In-depth qualitative interviews with a sample of participating older adults, finding out more about their experiences taking part, barriers and facilitators to doing so, their expectations of the programme, and the impact on their life.

 

Who are we working with?

Greater Manchester Combined Authority

South Wigan Ashton Primary Care Network

 

 

Contact information

 

 

Programme Manager

Alison Littlewood
alison.j.littlewood@manchester.ac.uk