Industry Solutions

AI in Australian Aged Care

2026-05-23 · By Solve8 Team · 16 min read

AI in Australian Aged Care

AI in Australian aged care: the regulatory stack providers must navigate

Aged Care AI Sits Under a Tighter Regulator Than General Healthcare

The Aged Care Act 2024 (Cth) commenced on 1 July 2025, replacing the Aged Care Act 1997 and reshaping the regulatory baseline for every approved provider in Australia. It is a rights-based, person-centred Act, written in direct response to the Royal Commission into Aged Care Quality and Safety Final Report (2021). The Aged Care Quality and Safety Commission (ACQSC) has been given stronger enforcement powers, the Strengthened Aged Care Quality Standards now apply, and the Serious Incident Response Scheme (SIRS) sits alongside notifiable breach obligations under the Privacy Act 1988.

Layer AI on top of that stack and the picture is clear. AI in aged care is regulated more tightly than AI in a typical general practice or allied health setting, because the regulator already operates under heightened post-Royal-Commission scrutiny, the resident population includes a high proportion of people with diminished decision-making capacity, and the funding model (AN-ACC, care minutes) is now legally tied to evidence of care delivery.

This article maps that regulatory stack for the CEO, CIO, and Quality and Compliance Lead of an Australian aged care provider working across residential, in-home, community, or retirement living. It is the fourth regulated-industry article in our series, sitting alongside financial services AI compliance under APRA and ASIC, healthcare practice AI and patient automation, and AI government contracts and compliance requirements. If you have not yet read why DIY AI fails without understanding the stack or the staffing gap that bites in production, start there first. Aged care concentrates every governance question we have raised so far into one regulated environment.

Part 1: The Regulatory Stack for AI in Australian Aged Care

There is no single "AI in aged care" law. There are seven instruments that interact, and a provider needs a control mapped to each. The table below is the working map.

The Seven-Layer Regulatory Stack

Metric
Instrument
What It Triggers for an AI Use Case
Improvement
Aged Care Act 2024 (Cth)Replaces the 1997 Act. Rights-based, person-centred framework. Approved provider obligations, statement of rights, Code of Conduct.AI must support, not subvert, the resident's rights. Provider remains accountable for any care decision an AI informs.Board-level accountability
Strengthened Aged Care Quality StandardsRevised standards under the new Act, covering the person, the organisation, care and services, the environment.AI-touched processes must demonstrate compliance against each applicable standard during ACQSC quality audits.Audit-grade evidence
ACQSC enforcement powersCompliance notices, directions, civil penalty applications, sanction and revocation powers.An AI-related quality failure can attract enforceable undertakings, sanctions, or registration consequences.Real penalty exposure
Serious Incident Response Scheme (SIRS)Mandatory reporting of priority 1 incidents within 24 hours and priority 2 within 30 days to the Commission.AI-attributable incidents (a missed alert, a wrong recommendation acted on) sit inside SIRS if a reportable incident category is met.Reporting clocks
Privacy Act 1988 + sensitive informationResident health information is sensitive information. APP 6 (use), APP 8 (cross-border), APP 11 (security), Notifiable Data Breaches scheme apply.AI processing of resident PI needs lawful basis, vendor controls, breach pathways to the OAIC.Privacy by design
State guardianship lawEach state and territory has substitute decision-making and guardianship legislation (e.g. NSW Guardianship Act, Vic Guardianship and Administration Act).Where a resident lacks capacity, AI-supported care decisions sit under substitute decision-maker authority, not the provider alone.Consent governance
WHS Acts (state-based)Worker safety obligations including reasonable management of psychosocial risk and monitoring practices.AI rostering or worker-monitoring tools must not breach WHS or Fair Work obligations.Worker protection

Two layers warrant emphasis. First, the Aged Care Act 2024 introduces a statement of rights and a strengthened Code of Conduct for Aged Care that applies to the provider, its workers, and its governing persons. AI that affects care delivery is treated as the provider's conduct, not the vendor's. Second, SIRS reporting timelines do not pause for "we are investigating whether the AI caused it". The 24-hour priority 1 clock starts on awareness of the incident.

For the broader Privacy Act backdrop, our GDPR vs Privacy Act comparison for Australian business sets out the underlying obligations that aged care providers inherit.

Part 2: Five High-Risk AI Use Cases in an Aged Care Provider

These are the use cases we see providers consider first. Each carries a distinct regulatory trigger and needs a distinct control.

High-Risk AI Use Cases and Their Regulatory Triggers

Metric
Use Case
Regulatory Triggers and Required Control
Improvement
1. Resident monitoring (falls, sleep, behaviour) via sensors + AICameras, bed sensors, wearables, AI inference on behaviour patterns. Marketed as falls prevention.Privacy Act (continuous PI capture), Aged Care Quality Standards (dignity, restrictive practice rules), state guardianship (consent for residents lacking capacity). Control: documented consent pathway, substitute decision-maker sign-off, restrictive-practice review where applicable.Consent + restrictive practice review
2. AI-augmented clinical documentation and handoverVoice-to-text progress notes, AI summarisation of shift handover, automated care-plan updates.AHPRA professional obligations for registered nurses, accuracy of the clinical record, retention rules. Control: clinician review and sign-off before the record is finalised; AI never the final author.Human-in-the-loop sign-off
3. Rostering and staffing allocation against care minutesAI rostering tools that allocate staff to meet 215-minute care-minute targets (including 44 RN minutes).Care minutes mandates under the Act, Fair Work Act roster obligations, WHS psychosocial risk, enterprise agreements. Control: AI rosters as recommendations; rostering manager approves; care minutes evidence is reportable to government.Care-minute auditability
4. Family and resident-facing chatbotsConversational AI for visit booking, FAQ, care queries, complaints intake.ACCC consumer law (no misleading conduct), accessibility for older users, privacy for resident PI surfaced in chat, complaints handling under the Act. Control: scope limits, escalation paths, ACCC consumer-law review.Scope-bounded chatbot
5. Predictive risk analytics (deterioration, dementia behaviour, falls)Models that flag residents at elevated risk so clinicians can intervene.Clinical decision support obligations, bias risk for cohorts (CALD, ATSI residents), Aged Care Quality Standards, audit evidence of model performance. Control: documented model risk, bias testing, override logging, board-level review of clinical AI.Model risk file

For the consumer-law angle on chatbots and AI-delivered information, see our note on ACCC consumer guarantees and AI implementation. For the worker-monitoring exposure, see Fair Work compliance and AI automation.

Part 3: Care Minutes, Staffing AI, and the Aged Care Act 2024

The Aged Care Act 2024 has put the care minutes mandate at the centre of operational compliance. From 1 October 2024, every residential aged care home is required to deliver an average of 215 minutes of direct care per resident per day, of which 44 minutes must be delivered by a Registered Nurse (Department of Health and Aged Care, 2024). Most homes also have a 24/7 RN requirement.

That single mandate has three consequences for AI:

  1. AI rostering systems must increase, not decrease, evidenced care minutes. If an AI roster pushes the home below targets, the financial and regulatory consequences fall on the provider, not the vendor.
  2. Care delivered must be evidenced. This is where automated time-capture, mobile charting, and roster integration earn their place, provided the integrity of the evidence is auditable.
  3. Worker-monitoring features attract WHS and Fair Work scrutiny. AI tools that track location, idle time, or task completion need to respect worker rights, enterprise agreements, and psychosocial risk obligations.

If you are evaluating an AI rostering or workforce-management vendor, the AI vendor selection questions checklist is the right starting filter. The Fair Work compliance and AI automation guide covers the worker-facing controls.

Indicative Care-Minutes Funding Exposure (100-Bed Metro Home)

Care-minutes supplement at risk per bed per day (indicative)Up to ~$31/day
Daily exposure across 100 bedsUp to ~$3,100
Indicative annualised exposure if targets not metUp to ~$1.1m
Worth verifying against your AN-ACC classification and ACFR dataProvider-specific

These figures are indicative only. Each provider should model their specific AN-ACC classification and Aged Care Financial Report data against current Department of Health and Aged Care guidance.

Part 4: SIRS and AI Incident Reporting

The Serious Incident Response Scheme covers eight categories of reportable incidents, including unreasonable use of force, neglect, unlawful sexual contact, psychological or emotional abuse, stealing or financial coercion, inappropriate use of restrictive practices, unexpected death, and unexplained absence from care. Priority 1 incidents must be reported to the Commission within 24 hours; priority 2 within 30 days. Both categories require an internal investigation and documented response.

AI changes how an incident can occur (a missed sensor alert, a flawed handover summary, a roster that left a unit understaffed), but it does not change the reporting obligation. If a reportable incident category is met, SIRS applies regardless of whether AI was involved.

SIRS Incident Flow When AI Is Involved

Detect
Incident identified by staff, family, sensor alert, or AI flag
Triage
Quality and clinical lead assess: SIRS category, priority 1 or 2
Notify ACQSC
Priority 1 within 24 hours, priority 2 within 30 days, via My Aged Care provider portal
Investigate
Document AI role: which model, which output, who acted on it, override status
Remediate
Care plan changes, staff support, vendor escalation, model re-review
Close-out
Final report to ACQSC, OAIC notification if a privacy breach also occurred

The investigation step is where most providers will be unprepared. You need to know which model produced the output, what inputs it had, who reviewed it, and whether a human override was available and used. If you cannot answer these questions for a SIRS investigation, your AI governance is not ready for production. The AI agent governance, data access, privacy, and human override article in this series sets the baseline.

Part 5: Privacy and Substitute Decision-Making

Resident health information is sensitive information under the Privacy Act 1988. AI use cases in aged care touch APP 1 (open and transparent management), APP 3 (collection), APP 5 (notification), APP 6 (use and disclosure), APP 8 (cross-border disclosure), and APP 11 (security). The Notifiable Data Breaches scheme administered by the OAIC applies when a breach is likely to result in serious harm.

The aged-care-specific complication is consent. A material proportion of residents in residential care have diminished or absent decision-making capacity. Consent for AI processing then sits with the substitute decision-maker under the relevant state framework (for example, the NSW Guardianship Act 1987, the Victorian Guardianship and Administration Act 2019, or the Queensland Powers of Attorney Act 1998). Family members are not automatically substitute decision-makers.

Four privacy questions every provider should be able to answer:

  1. What resident PI does each AI tool process, and is it sensitive information? If yes, default rules require consent and APP 6 limits.
  2. Is the AI vendor offshore? APP 8 cross-border disclosure obligations apply, including reasonable steps to ensure the recipient complies with the APPs.
  3. Where consent is needed and the resident lacks capacity, is the substitute decision-maker documented? Family preference is not a substitute decision-maker.
  4. What is the breach pathway? Internal incident, OAIC notification under the NDB scheme, and ACQSC notification under SIRS may all apply.

For background on cross-border AI services, our review of AWS, Azure, and GCP AI services in Australia is worth a read, alongside our data sovereignty Australia guide.

Part 6: The Control Stack from Pre-Deployment to Incident Response

A practical control stack sits in three phases.

Control Stack for AI in Aged Care

1
Pre-deployment
Risk and ethics review
Use-case risk assessment, model risk file, bias testing for CALD and ATSI cohorts, DPIA against APPs, vendor due diligence under APP 8, board sign-off for clinical AI.
2
Pre-deployment
Consent and rights design
Consent pathway documented per use case, substitute decision-maker workflow, statement of rights review, restrictive practice review where monitoring is involved.
3
Pre-deployment
Clinical governance integration
Quality and clinical governance committee approval, AHPRA-relevant staff briefing, integration with existing clinical risk register.
4
Live operation
Human-in-the-loop and override
Every clinical AI decision reviewed and signed by a clinician; override logging; AI never the final author of a clinical record.
5
Live operation
Monitoring and audit
Care minutes evidence verified, model performance logged, drift monitored, ACQSC audit pack maintained.
6
Live operation
Worker safeguards
WHS psychosocial risk review of AI-driven rostering, Fair Work consultation, enterprise agreement compliance.
7
Incident response
SIRS, OAIC, internal
SIRS triage within 24-hour clock, OAIC notification where the NDB scheme is engaged, internal investigation with full AI traceability, vendor escalation.
8
Incident response
Post-incident review
Model recalibration, training updates, board reporting, ACQSC close-out.

For the operating discipline beneath this control stack, our companion piece on operating AI agents in production for Australian business covers monitoring, drift, and handover practice.

Part 7: Which Regulator and Instrument Is Your AI Use Case Primarily Exposed To?

Use this as a fast triage tool. It does not replace legal or clinical governance review, but it sharpens the conversation.

Regulatory Exposure Triage for an Aged Care AI Use Case

What does the AI use case do?
It directly affects care delivery (clinical decision support, falls, medication, monitoring)
→ Primary exposure: Aged Care Act 2024, Strengthened Standards, ACQSC, SIRS, AHPRA. Treat as clinical AI: model risk file, clinician sign-off, board review.
It monitors workers (rostering, location, idle time, productivity)
→ Primary exposure: Fair Work Act, WHS Acts (psychosocial risk), enterprise agreements. Consultation, not just notification, may be required.
It processes resident PI offshore (US, EU, India)
→ Primary exposure: Privacy Act APP 8 cross-border disclosure, OAIC, and the NDB scheme. Vendor controls and contract terms become primary evidence.
It makes or informs decisions for residents lacking capacity
→ Primary exposure: state guardianship law plus the Aged Care Act statement of rights. Substitute decision-maker consent pathway is mandatory.
It is family/resident-facing (chatbot, portal, info service)
→ Primary exposure: ACCC consumer law (misleading conduct), accessibility, privacy. Scope-bound the bot and document escalation to human staff.
It generates reports for ACQSC, AN-ACC, or My Aged Care
→ Primary exposure: ACQSC accuracy obligations, Commonwealth fraud-control framework. Auditability of inputs and outputs is the central control.

Part 8: 12-Question Readiness Checklist for the CEO and Quality Lead

Take this to your next executive or board meeting. If you cannot answer "yes, with evidence" to most of these, your AI use cases are not ready for production in an aged care setting.

  1. Do we have an inventory of every AI tool in use across residential, in-home, and community services, including pilots?
  2. Has each use case been classified by risk against the Strengthened Aged Care Quality Standards?
  3. For each clinical AI use case, do we have a documented model risk file, including bias testing for CALD and ATSI cohorts?
  4. Is there a human-in-the-loop control on every clinical AI output, with a clinician signing the final record?
  5. For each AI tool processing resident PI, do we have a documented consent pathway and APP 8 vendor controls?
  6. Where residents lack capacity, do we have a documented substitute decision-maker workflow, distinct from family preference?
  7. Does our SIRS process explicitly cover AI-attributable incidents, with traceability to which model produced which output?
  8. Do our care minutes evidence systems hold up to ACQSC audit, including any AI rostering or time-capture tools?
  9. Have AI rostering and worker-monitoring tools been reviewed against Fair Work, WHS psychosocial risk, and enterprise agreement obligations?
  10. Is there a board or quality and clinical governance committee oversight item on AI, at least quarterly?
  11. Do we have a vendor exit plan for each AI tool, including data return, deletion, and continuity of care?
  12. Have we mapped which staff role is accountable for each AI tool, and have they been trained on incident triage and override?

For deeper governance scaffolding, the AI security checklist for Australian businesses gives you the controls layer, and the automation business case template gives you the financial framing for the board.

What This Means for Aged Care Providers

On its own, AI is neither a quality risk nor a quality gain in aged care. The risk and the gain both come from how it is governed against an already-tight regulatory stack. The Aged Care Act 2024 has elevated the baseline of provider accountability; the ACQSC is operating with greater enforcement appetite; SIRS, the NDB scheme, and the Strengthened Standards each create reporting and evidence obligations that an AI tool will inherit by default.

The providers that get this right will treat AI the way they treat any other clinical or operational intervention: with a documented risk file, governance sign-off, human-in-the-loop control, audit evidence, and a clear accountability owner. Those that do not will find their AI investments turning into ACQSC findings, SIRS reports, and OAIC notifications.

Treat the AI program as a clinical governance program, not an IT project. Your residents, your workers, your regulator, and your board will all be reading the same evidence.

Want to map your AI use cases against the Aged Care Act, the Strengthened Standards, and SIRS?

We work with Australian aged care providers on AI governance, model risk, vendor due diligence, and audit-grade evidence. Book a 30-minute consultation with our team: calendly.com/solve8/30min.

You can also explore our AI Strategy service and Managed AI Services for ongoing governance support.

Related Reading:

Sources:

Research synthesised from the Aged Care Act 2024 (Cth) and explanatory materials, Aged Care Quality and Safety Commission published guidance (2024-2025), Department of Health and Aged Care care minutes and AN-ACC publications (2024), Royal Commission into Aged Care Quality and Safety Final Report (2021), Office of the Australian Information Commissioner Australian Privacy Principles guidelines, Department of Industry, Science and Resources Voluntary AI Safety Standard (2024), and state guardianship legislation. Verify all figures and obligations against current Department of Health and Aged Care and ACQSC publications before relying on them.