Frailty Index to guide Australian aged care policy

Résumé fourni par la source

Background Australia's ageing population is driving unsustainable rates of preventable hospitalisation and premature residential aged care entry, with approximately 6,900 potentially preventable admissions per 100,000 adults aged 65+ recorded in 2021–22. Early frailty identification in primary care is both a clinical imperative and a health system priority; however, no validated, automated frailty detection tool exists for the Australian context. The UK-derived electronic frailty index (eFI) uses routine electronic health record (EHR) data to generate automated, point-of-care frailty scores, offering a scalable primary care solution. Aim/Objectives This study aims to adapt and validate the eFI for Australian primary care and to model its association with hospitalisation and residential aged care entry, with an explicit equity focus on culturally and linguistically diverse (CALD) and underserved populations. Methods We are executing an 18-month, seven-stage protocol. Stages 1-4 (Data & Modelling): Using ePBRN linked dataset of 158,159 records from South West Sydney, harmonised to the OMOP Common Data Model v5.3.1. UK eFI deficits were translated into Australian terminologies. Time-to-event modelling (Cox proportional-hazards) quantifies the association between derived frailty strata and subsequent hospitalisation. Stages 5-7 (Translation & Policy): We conduct co-design workshops with GPs, aged care clinicians, policymakers, and CALD consumers to translate computational risk tiers into culturally safe, pragmatic intervention protocols (e.g., structured medication reviews). Findings This project commenced in December 2025 and is currently in the governance and data preparation phase. Anticipated outputs include a validated, OMOP-embedded eFI algorithm; a systematic review of frailty instruments; and co-designed, equity-centered intervention protocols aligned with NSW Health priorities and GP workflows. Preliminary findings from the review and implementation of the frailty instrument eFI have been achieved. Implications Embedding automated frailty detection within primary care digital systems could enable scalable and proactive care for older adults. This approach has the potential to support clinicians in early intervention, improve patient outcomes, and strengthen the sustainability of primary care systems.

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