Why every clinic needs an AI use policy

Why allied health clinics need clear AI use policies covering approved tools, client information, staff use, output review, consent, escalation, and implementation.

Many clinics already have staff experimenting with AI. Some use it for emails. Some use it for patient handouts. Some use it to summarise information, create social posts, draft reports, prepare teaching material, or clean up admin text.

That experimentation is understandable. AI can be useful. But without a policy, a practice owner or team leader may not know what tools are being used, what information is being entered, what outputs are being relied on, or whether clients would expect their information to be handled that way.

An AI use policy is not about banning AI. It is about making AI use visible, reviewable, and aligned with professional responsibility.

AI use can become invisible

Unlike a new practice management system, AI can be adopted quietly. A browser tab, personal account, phone app, transcription tool, browser extension, or document assistant can become part of daily work before anyone has discussed privacy, consent, security, record keeping, or clinical review.

That creates several risks:

identifiable client information may be entered into an unapproved tool
staff may use personal AI accounts for work tasks
AI-generated text may be copied into client communication without review
a tool may store, retain, or use data in ways the clinic has not assessed
clients may not be told when AI is used in a consultation
managers may not know which workflows need approval
staff may assume AI output is more accurate than it is

The problem is not that staff are careless. The problem is that AI is easy to use before the team has agreed on boundaries.

A policy gives staff permission and limits

Good AI rules should make appropriate use easier, not harder. Staff should not have to guess whether they can use AI to draft a generic handout, rewrite an internal procedure, plan an in-service, or summarise public information.

A practical policy can create three categories:

Allowed without approval: low-risk tasks such as generic education drafts, non-sensitive admin templates, fictional teaching cases, public research summary prompts, meeting agendas, and plain-English rewrites.

Approval required: higher-risk tasks such as AI-assisted clinical documentation, client-facing communication, marketing claims, reports, use of any tool with identifiable information, or use of AI scribes and transcription.

Not allowed: entering identifiable client information, health information, staff matters, complaints, financial details, contracts, photos, recordings, or referral documents into unapproved public or personal AI tools.

This kind of structure gives people room to experiment while protecting the practice from the riskiest behaviours.

The policy should name approved tools

If staff are left to choose their own tools, a clinic can quickly end up with a mix of personal accounts, free tools, browser extensions, note-takers, transcription apps, and AI features inside other software.

A policy should name:

which tools are approved
which account types must be used
which tools are not approved for client or confidential information
who can approve a new tool
what checks are needed before a tool is introduced
whether staff can use personal AI accounts for work

The same product may have different risk settings depending on whether it is a free account, business account, enterprise account, or health-specific product. Naming the approved setup matters.

The policy should define what must never be entered

Every clinic should have a clear “do not paste” rule. Staff should know that general AI tools are not the place for:

names, dates of birth, addresses, phone numbers, Medicare numbers, NDIS numbers, record numbers, or appointment details
referral letters, clinical notes, assessment forms, reports, transcripts, recordings, or photos
rare conditions, small locations, family details, workplace details, or other combinations that could identify someone
complaints, staff issues, contracts, finances, legal correspondence, or confidential business information

The policy should also explain that removing a name may not be enough. De-identification needs thought, because a person can sometimes be recognised from context.

The policy should say who checks AI output

AI output can sound polished and still be wrong. It may be clinically inaccurate, incomplete, biased, too confident, misleading, out of scope, or poorly suited to the audience.

The policy should make clear who reviews AI-assisted output before it is used in:

client communication
clinical records
reports
patient education
staff training
website content
marketing
internal policies
funding or compliance documents

For clinical and client-facing work, the review should sit with a qualified person who understands the context and remains accountable for the final content.

Some AI use is invisible to clients. Other AI use directly affects them. AI scribes, transcription tools, client-facing chatbots, and tools that process client data need special attention.

A policy should answer:

when clients need to be told AI is being used
when informed consent is required
how consent is recorded
what happens if a client declines
what explanation staff should give
how errors in AI-generated notes or summaries are corrected

This is especially important for AI scribes and transcription tools, because they may listen to, record, transcribe, or summarise a real consultation.

The policy should include a simple escalation path

Staff need a clear answer to: “What do I do if I am unsure?”

The policy can say:

stop before entering the information
use a fictional or generalised version instead
ask a manager, clinical lead, privacy officer, or practice owner
record the question if it reveals a gap in the policy
update the policy when new workflows become common

This turns uncertainty into improvement rather than invisible risk.

Start small and review often

An AI use policy does not need to solve every future scenario. A short policy is better than no policy. Start with the tools and workflows your team is likely to use now, then review the policy as tools, privacy expectations, and clinical workflows change.

A sensible first version should include:

approved low-risk use-cases
prohibited information
approved tools and account types
approval process for new tools
output review rules
consent and disclosure rules
escalation process
review date

The goal is not to chase every new AI feature. The goal is to make sure AI use supports the clinic’s work without quietly undermining privacy, client trust, professional judgement, or accountability.

Useful starting resources include the Before You Paste Into AI checklist, Ahpra’s guidance on AI in healthcare, the OAIC’s overview of the Australian Privacy Principles, and the National AI Centre’s guidance for AI adoption.