AI Readiness Checklist for Healthcare Aged Care in Australia (2026)

Document all hardware and software currently in use across care delivery, rostering, catering, and facilities management to identify AI integration points and legacy system constraints.

Establish baseline measurements for key quality indicators including falls rates, pressure injuries, medication errors, and unplanned hospital transfers to measure AI impact post-implementation.

Survey care staff, registered nurses, and management on their comfort with digital tools to identify training needs before introducing AI-powered systems into daily workflows.

Prioritise AI opportunities differently for residential care, home care packages, and respite services based on each facility type's unique operational challenges and data availability.

Evaluate AI vendors specifically offering Australian-hosted, Privacy Act-compliant solutions for aged care, including platforms for predictive falls, medication management, and behavioural monitoring.

Specify the volume, quality, and labelling standards required for training AI models on aged care data, particularly for unstructured clinical notes and incident reports.

Develop a structured change management approach that addresses care worker concerns about AI replacing human touch, emphasising AI as an augmentation tool that frees time for resident interaction.

Model AI investment costs against AN-ACC funding categories and operational savings to build a sustainable business case that accounts for the sector's constrained funding environment.

Deploy AI tools that continuously gather and organise evidence against National Safety and Quality Health Service Standards, reducing the manual burden of accreditation preparation.

Implement AI rostering tools that match staffing levels to predicted resident acuity, accounting for AN-ACC care minutes requirements and Fair Work Award constraints.

Build dashboards that surface AI-generated insights on falls trends, pressure injury risk scores, and behavioural changes to clinical leads for early intervention.

Create structured mechanisms for care workers to report AI inaccuracies, false positives, and missed events to continuously improve model performance in the aged care context.

Establish a cadence of quarterly reviews assessing model accuracy, drift, and relevance as the resident population profile and care practices evolve.

Publish and maintain a plain-language register of all AI systems used in care delivery, accessible to residents and their families, explaining what data is used and how decisions are influenced.

Regularly assess whether AI tools perform equitably across resident cohorts including culturally and linguistically diverse residents, First Nations elders, and those with cognitive impairment.

Monitor AI investment returns against AN-ACC funding allocations and operational savings to ensure ongoing financial viability and inform future technology investment decisions.

Create contingency plans for AI vendor failure or contract termination, ensuring resident care continuity and data portability in line with Australian data sovereignty requirements.