An AI use-case approval workflow for aged care turns a scattered set of staff requests into a clear, defensible decision trail. When a rostering lead wants to trial a forecasting feature, or a clinician asks to draft notes with an assistant, the organisation needs a repeatable way to say yes, no or not yet, and to record who decided and why. Without that path, useful ideas stall and risky ones slip through unassessed.
This guide sets out an approval workflow that carries a request through purpose, user group, data scope, risk, human oversight and an accountable owner. It is written for quality, clinical, privacy, IT and executive staff who share these decisions. It is a practical governance process, not a general AI strategy piece, and it does not replace clinical judgement or legal advice.
Start With a Structured Use-Case Request
Approval begins with a request that describes the use case clearly enough to assess. Ask the requester to state the task in plain language, the problem it solves, the tool or feature involved, and the resident or business outcome expected. A vague request such as "we want to use AI for notes" cannot be approved; a specific one such as "draft a first version of a shift handover summary for a nurse to review and edit" can.
Capture the request in a single form so every submission carries the same fields. This makes review faster and comparison fair across teams. The how-to guide on policies shows how a consistent policy structure supports repeatable decisions, and the same discipline applies to an intake form that feeds those decisions.
Define Purpose and the User Group
Two fields shape almost everything that follows: what the tool is for, and who will use it. Purpose should be narrow and testable. "Summarise an incident for a manager to review" is assessable; "improve efficiency" is not. A tight purpose lets reviewers judge whether the benefit is real and whether the tool is being used only for the task approved.
The user group defines the blast radius. A tool used by two trained team leads carries different exposure from one opened to all floor staff. Record the roles, their training, and whether use is supervised or independent. Where a use case touches clinical work, the clinical governance framework policy template helps frame who may use a tool in a care context and under what oversight.
Set the Data Scope Precisely
Data scope is where most aged care AI risk lives. State exactly what information may enter the tool, what must never enter it, and where outputs are allowed to go. Use categories staff understand: resident identity, care notes, medication information, health details, workforce data, incident information and de-identified operational data. Do not approve "any data" when the task needs only a narrow slice.
Approval Field: Why It Matters
Purpose: Lets reviewers judge benefit and confine use to the task.
User group: Shows the exposure and the training needed.
Data scope: Sets what may enter the tool and where outputs may go.
Human oversight: Defines the review a person must perform before use.
Accountable owner: Names who approves, monitors and can pause the use.
Because these decisions involve personal and health information, ground the data scope in privacy expectations. The OAIC guidance on privacy and the use of commercially available AI products sets out what organisations should consider before entering personal information into commercial AI tools, which directly informs an approval decision.
Assess Risk in Proportion to Impact
Risk assessment should scale with the use case, not treat every request the same. A low-impact internal drafting task needs a lighter review than a tool that influences a care decision. Judge likelihood and consequence together, and consider what happens if the tool is wrong, unavailable or misused. Record the risks identified, the controls proposed, and any residual risk the approver must accept.
Tie this step to your existing risk process rather than inventing a separate one. The risk management policy template gives a consistent basis for rating and treating risk, so an AI use case is assessed on the same scale as other operational risks. This keeps decisions comparable and defensible when a governance forum reviews them.
Require Human Oversight That Fits the Task
Human oversight is the control that keeps an AI output from becoming an unchecked action. Define the review a person must perform before the output is used, and match it to the stakes. A handover draft may need a quick read and edit; content that informs a clinical decision needs a qualified person to confirm it against the resident's record and their own judgement.
State oversight as a required step, not a suggestion, and name the role that performs it. Make clear that the tool suggests and the person decides, so accountability stays with a human. The AI and automated decision-making policy template helps define oversight and escalation for automated support, so an approved use case inherits a consistent standard for when a person must intervene.
Name an Accountable Owner Before You Approve
No use case should go live without a named accountable owner who can approve continuation, monitor performance, respond to problems and pause or stop the use. This is different from the requester or the day-to-day user. The accountable owner holds the authority and carries the consequence, which is what makes the approval real rather than nominal.
Match the level of approval to the level of impact. A minor internal tool may be approved by a manager; a resident-facing or clinical use should reach a more senior decision-maker. The delegation of authority and responsibilities policy template helps set who can approve which use cases, so the sign-off sits at the right level and cannot be quietly self-authorised.
Record the Decision and Keep the Evidence
An approval is only useful if it can be found and understood later. Record the decision, the conditions attached, the owner, the review date and the reasoning. Keep the request, the assessment and the sign-off together as evidence that the use was considered rather than assumed. This also gives a clear reference if the use is questioned or an incident occurs.
Store approvals where a governance forum can review them, and keep a short register of approved use cases with their owners and conditions. Set a review date for each approval, so a use that was reasonable at sign-off is checked again after a feature update, a data-scope change or a workflow shift. An approval is a decision at a point in time, not a permanent licence, and the review date keeps it honest. The how-to guide on evidence explains how to keep proof that a control operated, which is exactly what an approval record provides. Governa's aged care compliance platform is built to keep policies, decisions and evidence connected, so an approval stays linked to the policy and obligation behind it.
Run a First Approval in Four Steps
- Request: capture purpose, user group, tool and expected outcome on a single form.
- Assess: set data scope, rate risk against your risk policy, and define human oversight.
- Decide: route to the accountable owner at the right delegation level for sign-off.
- Record and review: log the decision, conditions and review date, and keep the evidence.
Begin with the requests closest to resident information and care decisions, where a clear approval matters most. Reuse the same form and steps for every use case so decisions stay consistent and comparable. A short workflow that people follow protects residents and staff better than an elaborate process that requests bypass.
Related Resources
- delegation of authority and responsibilities policy
- AI and automated decision-making policy template
- risk management policy template
- using AI tools responsibly in aged care
- policy and evidence mapping
- OAIC guidance on commercially available AI products
Common Questions About AI Use-Case Approval in Aged Care
1. What information should an AI use-case request include?
Capture the purpose in plain terms, the user group, the tool or feature, the data involved and the expected outcome. A specific request can be assessed and approved, while a vague one cannot.
2. Who should approve an AI use case?
Match the approver to the impact, so a minor internal tool may be signed off by a manager and a resident-facing or clinical use by a senior decision-maker. Every approved use needs a named accountable owner.
3. How do we handle the data scope in an approval?
State exactly what may enter the tool, what must never enter it, and where outputs may go, using categories staff understand. Ground the decision in privacy guidance rather than approving open-ended data use.
4. What human oversight should an approval require?
Define the review a person must perform before an output is used, scaled to the stakes. The tool suggests and the person decides, which keeps accountability with a named human.
