Multisite AI policies aged care teams can use need one stable set of central rules, a controlled way to record local procedures, and named people who can decide when a variation is justified. That structure gives staff a clearer answer at the point of work without pretending that every site has the same systems, consumer needs or service model.
For a provider operating residential homes, home care services or a mixed group, the policy challenge is practical. A group-wide rule may govern approved AI tools, personal information, human review and escalation. A site procedure may then state which local system is connected, who checks a result, and where a record is retained. The policy should make that difference visible rather than burying it in email threads.
Start With a Policy Architecture Staff Can Read
A useful policy architecture has three layers. The first is the central policy: the board-approved direction that applies across the provider. The second is a controlled procedure or local schedule: a site-level instruction that applies the central rule to a particular workflow. The third is the evidence trail: approval, version history, review date and the people who own each document.
This approach keeps the central policy focused on decisions that should not vary casually, such as who may approve an AI use case, which information may be entered into a tool, and when a staff member must stop and seek human direction. It also makes room for a genuine local difference, such as a home using a different care-management system or a regional service having a distinct handover process.
Governa's AI and automated decision-making policy template can provide a starting point for central rules on human oversight, transparency and data handling. The document should still be reviewed and adapted by the provider's accountable people. A template is a starting point, not a substitute for local decision-making.
Set Central Rules That Do Not Drift by Site
Central rules are the guardrails that make the policy recognisable at every service. They should define approved and prohibited uses, roles with authority to approve a new tool or use case, required human review, minimum records, incident and concern pathways, and review triggers. Write these in plain language that a nurse, care worker and manager can locate quickly.
For example, a central rule could say that AI-generated content cannot replace a clinician's professional judgement or a staff member's required assessment. Another could require a documented review before a resident-related workflow is connected to an AI product. Those rules can apply everywhere even when a site uses different software.
The guide to using AI tools responsibly in aged care is useful background for explaining why staff need to understand both an AI tool's limits and their own escalation duties. The OAIC guidance on commercially available AI products also says organisations should consider due diligence, human oversight, privacy and security risks when adopting commercially available AI products. Those sources support a policy conversation; they do not remove the need for provider-specific legal and clinical advice.
Allow Local Procedures Without Creating Shadow Policy
A local procedure should answer operational questions that the central policy deliberately leaves open. It might name the local workflow owner, the relevant software configuration, the approved prompt or form, the place where a review is recorded, and the local contact for a problem. It should not quietly weaken a central restriction or create a new approved use case without authority.
Use a standard variation form or schedule with a short rationale. Record the central clause affected, the proposed local wording, affected roles, risk considerations, approvals, effective date and review date. If the local need is permanent and appears across several services, the policy owner should consider updating the central policy rather than multiplying exceptions.
A system that maps documents to requirements can help a group find related material, but the ownership decision remains human. Governa's article on AI policy and evidence mapping describes policy and evidence mapping as a way to connect documented material and related evidence. It is most useful when the organisation has already decided which document is authoritative.
Give Every Layer an Owner and a Decision Right
Multisite governance fails when everyone can comment but no one can approve. Assign an executive or delegated policy owner for the central policy, a local procedure owner for each service, and a document controller for publication and withdrawal. Separate advice from approval. Clinical, privacy, quality, technology and workforce teams may all contribute, while one named role records the final decision.
A simple responsibility table can prevent confusion: the central owner approves group-wide rules; the site manager proposes or maintains local procedures; a subject-matter reviewer checks the practical detail; the document controller publishes the controlled version; staff use the current version and report problems. Include a route for urgent temporary instructions and a date by which they must be reviewed or retired.
This distinction matters when staff ask an AI-supported question. how facility policies guide AI answers shows why answers grounded in facility protocols are different from generic internet answers. A local procedure must point to the current site protocol, while the central policy sets the boundaries of that use.
Make Variations Traceable and Easy to Retire
Each variation needs an identifier, status and expiry or review date. Keep it linked to the parent policy and make the relationship clear in the document header. Staff should not have to compare several folders to know whether a local variation is still active. A register can show the service, owner, reason, approval, start date, next review and superseded document.
Review triggers are more useful than a fixed annual date alone. Trigger a review when a site changes systems, a new AI use case is proposed, an incident reveals a gap, a consumer-facing process changes, or the central rule is amended. Retiring a variation should be a recorded act: withdraw the local document, notify affected roles, and retain the decision record under the provider's document-control approach.
Build a Practical Rollout Sequence
Begin by listing existing group rules and site procedures that refer to AI, automated decisions, information handling, clinical review, records and procurement. Mark the authoritative document for each topic. Then identify conflicts and gaps before drafting new material. This avoids treating a new AI policy as a stand-alone document when staff already rely on other instructions.
- Draft the central policy with its scope, non-negotiable rules and approval path.
- Issue a local-procedure template that can only vary identified fields.
- Assign owners and publish a register of current local schedules.
- Brief affected staff on how to locate the correct layer and raise a variation request.
- Sample a small number of sites after rollout to check whether the documents match real work.
Governa Connect describes a product approach that links policies and evidence with aged care standards. Whatever tool a provider selects, the group should keep its document hierarchy and approval record intelligible without relying on a product screen alone.
What Good Multisite Control Looks Like in Practice
Good control does not mean identical words everywhere. It means staff can identify the central rule, the applicable local procedure and the person accountable for a decision. It means a local exception has a reason and a review date. It means an outdated schedule is withdrawn rather than left beside a current policy.
The Aged Care Quality and Safety Commission AI transparency statement is an example of a public body describing its AI adoption approach and publishing a dated statement. Aged care providers can take the same practical lesson: make governance information clear, current and owned. The aim is a workable record of decisions, not a claim that a policy alone prevents every risk.
Related Resources
- AI and automated decision-making policy template
- policy and evidence mapping
- Governa Connect compliance infrastructure
- delegation of authority and responsibilities policy
- managing the Governa Policies section
- OAIC guidance on commercially available AI products
Common Questions About Multisite AI Policies in Aged Care
1. What should sit in a central AI policy for an aged care group?
Put stable, group-wide rules in the central policy: scope, approved-use authority, data boundaries, human review, escalation, records and review triggers. Leave site-specific systems and work steps to controlled local procedures.
2. When is a local variation appropriate?
A variation is appropriate when a real site difference affects how a central rule is applied, such as a different system or workflow. It should not be used to bypass a group restriction or approve a new use case without the required authority.
3. Who should approve a local AI procedure?
Set this in the central policy. A common model is local drafting by the site owner, subject-matter review, and approval by the delegated central policy owner or another role with documented authority.
4. How can staff tell which document applies?
Give documents clear identifiers, effective dates and links between the central policy and local schedule. Maintain one current register and remove withdrawn versions from everyday access points.
5. Does a central policy mean every site must use the same AI tool?
No. A central policy can govern decision rights and safeguards while allowing approved local systems. The important point is that the local procedure identifies the system and does not contradict the group rules.
