AI tool choice should begin with the practice problem, not the product demo. A tool may look impressive and still be unsuitable if it creates privacy risk, adds workflow friction, weakens clinical review, or makes accountability unclear.
The right question is not “Is this AI tool powerful?” The better question is “Is this tool appropriate for this workflow, this information, this team, and this level of risk?”
1. Define the workflow
Be specific about the task. “We want to use AI” is too broad. A useful workflow statement sounds more like:
- draft generic patient education handouts
- help admin staff rewrite appointment instructions
- create staff in-service outlines
- summarise public research abstracts for journal club
- transcribe consultations
- draft clinical letters
- generate marketing content
- support intake triage
- create internal policies and checklists
Each workflow has different risks. A tool used for generic education drafts is very different from a tool used to process consultation recordings or recommend treatment.
Before looking at products, write down:
- what job the tool will do
- who will use it
- what information will go in
- what output will come out
- who will review the output
- where the output will be used
This prevents the practice from buying a tool first and inventing the safety process later.
2. Classify the information involved
The more sensitive the information, the more carefully the tool needs to be assessed.
Lower-risk information includes:
- public information
- fictional examples
- generic education topics
- non-sensitive admin text
- public research abstracts
- internal templates that do not include confidential details
Higher-risk information includes:
- personal information
- health information
- consultation recordings or transcripts
- clinical notes, reports, referral letters, photos, or assessment forms
- staff issues, complaints, contracts, finances, or legal correspondence
- business strategy or confidential operational information
If the tool will handle personal or health information, the practice needs to understand privacy, consent, security, retention, access, review, and deletion controls before adopting it.
3. Check the data controls
Do not rely only on marketing claims. Read the privacy information, security documentation, product terms, and admin settings. If the tool is being sold for healthcare use, ask specific questions.
A practice should understand:
- where data is stored and processed
- whether data may be processed outside Australia
- whether prompts, files, recordings, or transcripts are retained
- whether inputs are used to train or improve models
- whether humans can review inputs for support, safety, or quality purposes
- whether data can be deleted
- whether the practice has admin controls and audit logs
- whether the tool supports business or healthcare accounts
- whether staff can disable training, history, or sharing settings
- whether the vendor will sign appropriate agreements if needed
If the vendor cannot answer basic data-handling questions clearly, that is a warning sign.
4. Understand the intended use
Ahpra guidance highlights the importance of understanding the intended use of an AI tool, because that affects the risks and limitations that need to be considered.
In practice, this means asking what the tool is actually for:
- Is it a drafting tool?
- Is it a transcription tool?
- Is it a clinical documentation tool?
- Is it a decision-support tool?
- Is it a client-facing chatbot?
- Is it a marketing tool?
- Is it an automation tool?
The more the tool influences clinical decisions, client communication, documentation, access to care, or risk assessment, the more governance it needs.
A tool that claims to support diagnosis, treatment, triage, or clinical decision-making needs a much higher level of assessment than a tool that helps draft a generic in-service outline.
5. Decide who reviews the output
AI output should not flow directly into clinical records, client communication, education, reports, or marketing without review.
Before adopting a tool, decide:
- who checks the output
- what they are checking for
- whether the reviewer needs clinical qualifications
- how errors are corrected
- whether the original input and AI output are retained
- whether AI involvement should be disclosed internally or to clients
- what happens if the output is uncertain, incomplete, or unsafe
For clinical and client-facing work, the reviewer should not simply be checking spelling. They need to check clinical meaning, safety, scope, tone, and fit for purpose.
6. Assess consent and client expectations
Some AI tools are used behind the scenes. Others directly affect clients.
For AI scribes, transcription tools, client-facing chatbots, or tools that process client data, ask:
- what clients are told
- whether informed consent is required
- how consent is recorded
- whether the client can decline without losing access to care
- what happens to the audio, transcript, and summary
- whether the client can ask questions or raise concerns
Even when a tool is technically useful, it may damage trust if clients feel their information has been used in a way they did not expect.
7. Check fit with the team’s actual work
The best tool on paper may still fail if it does not fit the clinic.
Consider:
- how much training staff will need
- whether the workflow saves time after review is included
- whether staff can use it consistently
- whether it works with existing systems
- whether it creates duplicate documentation
- whether the output is good enough to justify the checking burden
- whether it helps the people doing the work, not just the person buying the tool
AI tools can create hidden labour. If the team spends more time correcting, explaining, formatting, and checking than they save, the tool may not be worth adopting.
8. Start with a small pilot
Do not roll out a tool across a clinic just because it looks promising. Start with one low-risk workflow and test it using non-identifiable information.
A useful pilot should answer:
- Does the tool actually save time?
- Are outputs accurate enough after review?
- What mistakes does it commonly make?
- What prompts or templates work best?
- What information must not be entered?
- What training do staff need?
- What policy changes are required?
- What would make this unsafe?
Document the results. If the pilot works, expand gradually. If it does not, stop before the tool becomes part of routine practice.
9. Write down the rules before rollout
Before a tool becomes available to staff, the practice should document:
- approved use-cases
- prohibited use-cases
- approved account types
- information that must not be entered
- review requirements
- consent and disclosure requirements
- escalation process
- incident process
- review date
This does not need to be complicated. A short tool-specific rule sheet is often enough for an early pilot.
A simple tool-selection test
Before adopting an AI tool, ask:
- What exact workflow will this improve?
- What information will it handle?
- Is the tool approved for that information?
- What does the vendor do with prompts, files, audio, transcripts, and outputs?
- Who remains accountable?
- Who reviews the output?
- What are clients told?
- What happens if the tool is wrong?
- Can the practice explain the workflow to a client, insurer, regulator, or staff member?
If the team cannot answer those questions, the tool is not ready for routine use.
Related reading: What Australian allied health professionals should know before using AI and Why every clinic needs an AI use policy.
Useful starting resources include Ahpra’s guidance on AI in healthcare, the OAIC’s overview of the Australian Privacy Principles, the National AI Centre’s guidance for AI adoption, and the Before You Paste Into AI checklist.