Using health records to forecast patients’ risk of falling within the next 12 months is feasible through a newly devised calculator based on NHS data. Known as eFalls, this falls prediction model utilises readily available primary care electronic health record data, marking a pioneering initiative globally. Developed and validated by researchers from the University of Leeds, the University of Birmingham, and a collaborative team supported by funding from the National Institute for Health and Care Research (NIHR), eFalls aims to pinpoint individuals at risk of hospitalisation or emergency department visits following a fall in the upcoming year. This proactive identification enables the provision of interventions to mitigate the occurrence of falls.
Detailed in a research paper published in Age and Ageing, the study underscores the significance of addressing falls among individuals aged 65 and above, given their potential impact on personal independence. The multifactorial nature of fall risks includes factors such as mobility or balance-affecting conditions, medications, and home hazards, with a history of falls being a prominent risk indicator. Moreover, the prevalence of falls is expected to escalate with the global ageing demographic.
The implications of these findings are substantial for proactive fall risk identification. By leveraging existing primary care data, eFalls streamlines the process, reducing the necessity for exhaustive clinical fall assessments and saving valuable time for healthcare professionals. Upon identification of individuals at risk, referral to specialist fall prevention services becomes feasible, facilitating assessment and treatment to avert future falls.
According to estimates from the National Institute for Health and Care Excellence (NICE), a significant proportion of falls result in severe injuries such as major lacerations, traumatic brain injuries, or fractures, alongside other complications such as distress, pain, and loss of independence. Andrew Clegg, Principal Investigator and Professor of Geriatric Medicine at the University of Leeds, emphasises the global significance of falls as a health concern, highlighting the proactive nature of eFalls in mitigating risks and reducing the burden on healthcare systems.
Lead author Lucinda Archer, Assistant Professor in Biostatistics at the University of Birmingham, underscores the potential of the eFalls calculator to predict fall risks based on existing GP records. Promising accuracy has been demonstrated through extensive testing across large datasets. Employing eFalls to target individuals needing specialist assessment holds promise for enhancing fall prevention services in the UK.
Health Minister Andrew Stephenson lauds innovations like eFalls for their potential to prevent falls, alleviate pain, and reduce resource strain on the NHS. As part of ongoing efforts to ensure timely and appropriate care, Stephenson underscores the importance of solutions like eFalls in addressing a widespread issue.
The research team embarked on a rigorous process to develop a robust method for proactively identifying individuals for fall prevention interventions, addressing the current scarcity of such systems. They drew from a vast dataset encompassing over 750,000 healthcare records to refine and validate the eFalls tool. This extensive testing demonstrated its potential in integrating with primary care electronic patient record systems in the UK, paving the way for its future implementation.
Looking ahead, the researchers aim to integrate the eFalls prediction model into UK primary care systems, collaborating with policymakers to explore its potential contributions to health policy. By harnessing the power of predictive analytics, eFalls represents a significant step towards enhancing preventive healthcare interventions and mitigating the burden of falls among older populations.
More information: Lucinda Archer et al, Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults, Age and Ageing. DOI: 10.1093/ageing/afae057
Journal information: Age and Ageing Provided by University of Leeds
