Our recent webinar explored the role of artificial intelligence in the aged care sector, with a focus on practical applications, responsible adoption, and the realities of bringing these tools into everyday practice.

Setting the Scene

The session began with a discussion about perceptions of AI. Participants shared a mix of excitement and concern, with themes such as automation, privacy, data security, and even sci-fi associations. This set the tone for unpacking what AI really is: not a human replacement, but a powerful tool for recognising patterns, making predictions, and handling repetitive tasks at speed.

Where AI Fits in Aged Care

A core theme was that AI is not a substitute for human empathy or complex care decisions. Instead, its strength lies in augmenting the work of staff, reducing admin burdens, and providing insights that free people to focus on personalised, human-centred care.

Use Cases and Examples

Several practical use cases were highlighted:

• Remote monitoring and predictive care: Using sensors and wearables to detect subtle health changes early, enabling proactive interventions.

• Enhanced communication and onboarding: AI intake agents bridging language barriers for non-English-speaking clients, improving client experience from day one.

• Documentation support: Voice-to-text case notes, smart document audits for missing information, and natural language queries to generate summaries.

• Scheduling optimisation: AI models factoring in staff skills, travel times, and compliance to reduce burnout and improve efficiency.

• Billing and claims: Automated checks, coding, and matching to reduce errors and speed up processing.

• IT testing: Tools that can simulate user interactions, reducing repetitive manual testing time.

Adoption Challenges

The discussion also tackled adoption barriers. Staff learning curves, fears about job displacement, and trust in AI outputs were front of mind. Strategies like phased rollouts, clear communication about AI as an assistant (not a replacement), and the concept of “human in the loop” were emphasised. Broader concerns such as data sovereignty, environmental impact, and security were addressed, with reassurance that mature cloud and local models can manage these risks.

Key Takeaways

1. Start small: Pilot projects and quick wins help build confidence and trust.

2. Data matters: Clean, structured data is essential before trialling AI.

3. Integration is key: AI should work within existing systems, not in isolation.

4. Plan for the long term: Building towards AI maturity is a multi-year journey, not a quick fix.

Closing Thoughts

The message was clear: AI will not replace the compassion and judgement central to aged care, but it can dramatically reduce inefficiencies, support decision-making, and enhance quality of care when adopted thoughtfully. Organisations that take a measured, well-planned approach will be best placed to benefit from this transformative technology.