A groundbreaking tool called the Electronic Frailty Index 2 (eFI2) is now accessible to 60% of England’s general practitioners (GPs), research conducted by the University of Leeds and UCLans published in Age and Ageing. The eFI2 uses data on 36 health issues, such as dementia and falls, to more accurately identify frailty in older adults, allowing for earlier interventions like personalized care plans and fall prevention. This advancement aims to improve patient independence and reduce NHS costs, which currently total £6 billion annually due to frailty-related care.

The original eFI, developed by Leeds researchers in 2016, was a world-first system that helped prevent an estimated 2,300 falls and saved the NHS nearly £7 million in 2018 alone. Its success inspired similar tools in countries like the US and Canada. The new eFI2 builds on this legacy by incorporating additional health variables, such as weight loss and prescription counts, to enhance prediction accuracy.

A study published in Age and Ageing confirms that the eFI2 can better predict outcomes like the need for home care, falls, or care home admissions. The tool is now integrated into Optum software, widely used by GPs in England.

Professor Andrew Clegg, the study’s lead author, emphasized the eFI2’s potential to transform care for older adults: “This tool enables GPs to identify frailty earlier and provide targeted treatments, helping patients maintain independence and reducing costly adverse outcomes.”

Professor Marian Knight of NIHR added, “The eFI2 is a game-changer, offering personalized care while delivering significant savings to the health system.” Kate Walters, a GP and co-author from UCL, noted, “This tool simplifies frailty identification, empowering GPs to support vulnerable patients effectively.”

The eFI2 represents a major leap forward in frailty management, combining data-driven precision with clinical judgment. By enabling earlier interventions, it promises to enhance the quality of life for older adults while alleviating financial pressures on the NHS. Future efforts will focus on expanding the tool’s reach and refining its predictive capabilities.