Using data and artificial intelligence to improve care pathways of older people experiencing falls and frailty: Opportunities, challenges and practical considerations for clinicians

Older people living with falls and frailty are common in emergency attendances, admissions and functional decline. Artificial intelligence (AI) and machine learning (ML) are increasingly incorporated in risk prediction, service streamlining and re-engineering, yet their roles in healthcare practice remain unclear. This CME article provides a practical overview for clinicians of acute care and internal medicine with a special interest in older people's care. We summarise emerging applications of AI and AI-assisted tools across the falls and frailty care pathway, from community support through the emergency department, orthogeriatrics and post-acute rehabilitation. We highlight potential benefits: enhanced risk stratification, facilitation of comprehensive geriatric assessment (CGA), rehabilitation and delivery of care transition programmes. We then discuss challenges and ethical concerns, for instance, 'digital ageism', automation bias and weak evidence for impact. Finally, we outline pragmatic questions and steps that clinicians can adopt when using AI-enabled tools in clinical settings.