AI for Clinical Documentation in Australia: A Practical Guide (2026)

Clinical documentation is one of the most time-consuming parts of a doctor's working day. Australian GPs and specialists spend hours each day writing consultation notes, referral letters, discharge summaries, and care plans — time that comes at the expense of patient contact and contributes to professional burnout.

AI-powered clinical documentation tools are changing this. This guide explains how they work, what's available in Australia, and how to adopt them safely and effectively.

How AI Clinical Documentation Works

AI clinical documentation tools — often called ambient scribes or AI scribes — work by listening to a consultation and automatically generating a structured clinical note. The process typically works as follows:

The doctor starts a recording at the beginning of the consultation, either on a smartphone or through a dedicated device. The AI transcribes the conversation in real time, then processes the transcript to identify clinically relevant information — the presenting complaint, history, examination findings, assessment, and plan — and organises it into a structured note format.

The doctor reviews the draft note, makes any necessary edits, and approves it for inclusion in the patient's record. The entire review process typically takes less than a minute, compared to several minutes of typing for a manually written note.

Heidi Health: Australia's Leading AI Scribe

Heidi Health is an Australian-developed AI medical scribe that has become one of a widely used documentation tools among Australian GPs. It supports a range of note formats including SOAP notes, referral letters, and discharge summaries, and integrates with major Australian practice management systems including Best Practice, Medical Director, and Zedmed.

The platform is designed specifically for Australian clinical practice and is familiar with Australian medical terminology and MBS requirements.

Doctors using Heidi report time savings of between two and five minutes per consultation — which translates to one to two hours per day for a typical GP. Many also report that the quality of their clinical notes improves, because the AI captures details that might be missed when typing notes from memory after a consultation.

Other AI Documentation Platforms

Nabla Copilot is another ambient AI scribe that is gaining traction in Australian clinical settings. It supports multiple note formats and can be configured to match a practice's preferred documentation style. Nabla is used in both primary care and specialist settings and offers integration with a range of practice management systems.

Freed is a US-developed AI scribe that is used by some Australian doctors. It is designed for speed — generating a draft note within seconds of the consultation ending — and supports a range of clinical specialties.

Dragon Medical One from Nuance is a voice recognition and documentation platform that has been used in Australian hospitals and specialist practices for many years. It uses AI to improve transcription accuracy and can be integrated with hospital electronic medical record systems.

What AI Scribes Can and Cannot Do

AI scribes are excellent at transcribing spoken content and organising it into structured formats. They are less reliable for:

• Complex clinical reasoning: The AI captures what is said, but cannot infer clinical reasoning that isn't explicitly stated. If a doctor's assessment is based on subtle examination findings or clinical intuition, this needs to be articulated clearly for the AI to capture it accurately.

• Highly technical or rare terminology: AI scribes perform best with common clinical language. Unusual terminology, abbreviations, or highly specialised language may be transcribed incorrectly and should be checked carefully.

• Non-verbal information: Physical examination findings that are observed but not described aloud will not be captured. Doctors need to narrate their examination findings for the AI to include them.

Referral Letters and Discharge Summaries

Beyond consultation notes, AI tools can significantly reduce the time spent on referral letters and discharge summaries. Rather than writing these from scratch, doctors can use AI to generate a draft based on the consultation note or patient record, which they then review and personalise.

For referral letters, AI tools can pull relevant information from the patient's record — diagnosis, medications, relevant history, investigation results — and structure it into a professional letter format. The doctor reviews the draft, adds any specific clinical context, and approves it for sending.

For discharge summaries, AI tools can generate a structured summary from the inpatient record, including the admission diagnosis, treatment provided, investigations performed, and follow-up plan. This is particularly valuable in hospital settings where discharge summaries are often delayed due to time pressure.

Privacy and Consent Considerations

Recording consultations for AI documentation purposes raises privacy considerations that doctors need to address. Under Australian privacy law, patients have a right to know how their information is being used, and recording a consultation for AI processing constitutes a use of personal health information.

Most AI scribe platforms recommend informing patients that the consultation will be recorded for documentation purposes and obtaining their consent. This can be done verbally at the start of the consultation or through a notice in the waiting room. Some practices include a consent clause in their patient registration forms.

Doctors should also ensure that any AI platform they use stores and processes patient data in accordance with Australian privacy law, including the requirement that health information be stored in Australia or in a jurisdiction with equivalent privacy protections.

Integrating AI Documentation into Your Practice

The most effective approach is to start with a trial period using the free tier of a platform like Heidi Health, using it for a subset of consultations to get comfortable with the workflow before rolling it out more broadly.

Key steps for successful adoption include:

• Informing patients about the recording process and obtaining consent
• Establishing a review workflow that ensures every AI-generated note is checked before finalisation
• Training reception and nursing staff on the new workflow
• Reviewing the quality of AI-generated notes regularly, particularly in the early weeks

Most doctors find that the workflow becomes natural within a few weeks, and the time savings become significant once the review process is streamlined.