Explainers · 13 October 2026 · Pathways

What is perioperative AI?

Perioperative AI is the use of artificial intelligence to support the care of surgical patients before, during and after an operation. Most of it today sits before surgery, where the work is gathering a patient's history, identifying risk and getting them ready. It isn't a robot anaesthetist; it's closer to infrastructure that gets the right information to the right clinician in time.

Perioperative AI is software that uses artificial intelligence to gather, interpret and act on patient information across the surgical journey, from the decision to operate to recovery at home. Here’s what it covers, how it works, and what to ask before trusting it.

The short answer

Perioperative AI is the use of artificial intelligence to support the care of surgical patients before, during and after an operation. Most of it today sits before surgery, where the work is gathering a patient’s history, identifying risk, and getting them ready.

It isn’t a robot anaesthetist. It’s closer to infrastructure: a way of getting the right information, about the right patient, to the right clinician in time to act on it.

Why the perioperative period?

“Perioperative” covers three phases. Preoperative runs from the decision to operate until anaesthesia begins. Intraoperative is the time in theatre. Postoperative covers recovery, the ward and the return home.

The preoperative phase is where AI has the most to offer, because its core problem is information logistics. A patient’s story is scattered across a GP summary, specialist letters, a medication list, old discharge summaries and the patient’s own memory. Pre-admission teams spend much of their time collecting and reconciling it before anyone can assess risk.

The work is also high-volume and time-bound. Thousands of patients pass through a busy hospital’s pathway each year, each with a fixed surgery date. A recent audit at Royal North Shore Hospital found that 30% of patients who missed their digital pre-op questionnaire were invited two days or less before surgery, on the day, or afterwards. The bottleneck was getting the right information at the right time.

Where perioperative AI shows up

PhaseTaskWhat AI doesExample
Before surgeryHealth historyRuns the questionnaire as a conversation, by text or voiceA voice agent phones the patient and works through their history
Before surgeryDocumentsExtracts key facts from referrals, letters and resultsPulling cardiac function and implanted devices from a cardiology letter
Before surgeryMedicationsReconciles the medication list and infers likely conditionsAnticoagulants, SGLT2 inhibitors and GLP-1 receptor agonists flagged for perioperative review
Before surgeryRisk and triageApplies screening tools and rules to route patientsA positive sleep apnoea or frailty screen sends a patient to an anaesthetist
Before surgeryPreparationReminds patients of instructions and checks they’ve followed themDay-prior calls that flag a fasting breach or a medication not stopped
During surgeryMonitoringPredicts events from physiological dataEarly warning of intraoperative hypotension
After surgeryDeteriorationWatches observations and results for worrying trendsFlagging a ward patient whose observations are heading the wrong way
After surgeryFollow-upChecks in after discharge and collects outcome measuresA post-discharge call about pain, wound healing and nausea

Not all “AI” is the same

Three quite different technologies get called AI in perioperative care.

Language models understand and produce text and speech. They are good at conversation, summarising and pulling facts out of unstructured documents. Their weakness is that they can misread or invent details, so their outputs need checking and a clear link back to the source.

Predictive models learn from historical data to estimate the probability of something, such as a complication or deterioration. Their performance depends on the data they were trained on, so they need validating on local patients and monitoring over time.

Rules and clinical logic encode guidelines and screening tools directly. Strictly speaking this isn’t AI, but it is often the backbone of a safe system: transparent, auditable and predictable.

The strongest perioperative systems combine all three. Language models collect and structure information, rules make decisions explainable, and clinicians make the judgement calls.

What perioperative AI isn’t

  • It isn’t a replacement for the anaesthetist. The anaesthetist remains responsible for the assessment and the plan.
  • It isn’t a chatbot bolted onto a web form. A conversational front end on top of a 150-question form is still a 150-question form.
  • It isn’t a fix for a broken workflow. If invitations go out at the wrong time, or to patients who have been admitted, transferred or have died, a smarter questionnaire won’t help. Integration with the medical record matters as much as intelligence.

The risks, and how good systems manage them

Errors that travel. A detail misread early can flow through the record into the anaesthetic plan. Good systems link every extracted fact to its source, weight sources by reliability (a specialist letter outranks patient recall), and put a clinician between the AI’s output and any clinical decision.

Inequity. A pathway designed for confident, English-speaking smartphone users leaves others behind. Voice channels, multilingual support, carer involvement and a human fallback help close that gap.

Stale data. AI acting on an out-of-date list will contact the wrong patients at the wrong time. It needs live events from the medical record: admissions, transfers, cancellations and deaths.

Unproven performance. Systems should be tested against clinician judgement on local patients before go-live, then monitored for as long as they run.

Regulation in Australia

  • TGA. Software intended to diagnose, monitor, predict or guide treatment can be regulated as a medical device. Some clinical decision support software is exempt, broadly where it supports a health professional’s decision rather than replacing it. The TGA continues to review how AI is regulated.
  • State frameworks. In NSW, AI projects in government agencies, including NSW Health, are assessed under the NSW AI Assurance Framework. Other jurisdictions run their own processes.
  • Privacy. Health information is covered by the Privacy Act 1988 and state health records laws, such as the NSW Health Records and Information Privacy Act 2002.

Six questions to ask before adopting perioperative AI

  1. What exactly does the AI do, and what does a clinician still do?
  2. Can every output be traced back to its source document or conversation?
  3. How was it evaluated, on which patients, and against what standard?
  4. How does it connect to our medical record, and what happens when the record changes?
  5. Who does it work less well for, and what’s the fallback for them?
  6. What is its regulatory status, and where is our patients’ data stored?

The bottom line

Perioperative AI is most useful where care is held back by the effort of collecting and making sense of information. Done well, it gives clinicians a fuller picture earlier, reaches patients a web form can’t, and frees skilled staff for the complex cases. Done badly, it automates the old problems faster. The difference lies in integration, evidence and keeping clinicians in charge.

FAQ

Related questions

Where is Pathways patient data stored?

All Pathways patient data is stored in AWS Sydney (ap-southeast-2). Patient information is not transferred outside Australia in storage, processing or backup. Data is encrypted in transit using TLS 1.2 or above and at rest with keys managed in AWS KMS.

Data sovereignty is a procurement gate for most Australian health services, so it is worth being precise about the boundary. The Australian region covers the full lifecycle: primary storage, processing during an assessment, and backups. There is no cross-region replication of patient information.

Does Pathways send patient data to AI model providers?

No. No personally identifying information is sent to model providers. Extraction and reasoning operate on de-identified clinical content, and patient information is never used to train models. The safety-critical screening rules are deterministic code rather than model output, so they do not involve a model provider at all.

This is two separate guarantees, and they are worth separating because they fail in different ways.

The first is about identity: the content that reaches a model provider is de-identified, so a model provider never receives a named patient record. The second is about training: patient information is not used to train models, by anyone, at any point.

Underneath both sits a design decision that removes the question for the parts that matter most. The rules that protect patients — the SGLT2 withholding check, the glycaemic control threshold, the OSA screen — are deterministic, versioned code. They are not a model output that happens to be reliable; they fire every time their conditions are met and cannot be argued out of it.

What is Socrates Pre-Op?

Socrates Pre-Op is the Pathways voice agent that takes a structured anaesthetic history from a patient by phone. It is delivered as a secure, time-limited SMS link — no app, no login, no paper questionnaire — and covers 12 clinical domains conversationally, producing structured, severity-tagged findings that feed the risk engine directly.

It is a purpose-built anaesthetic variant rather than a generic intake bot. The distinction matters in practice: a general-purpose intake tool collects answers to the questions it was given, whereas an anaesthetic interview needs to follow up on a positive family history of anaesthetic problems, probe functional capacity when a patient says they are “fine”, and recognise when a described medication is worth photographing.

Completion is tracked, and a finished interview automatically triggers a fresh risk analysis.

How this compares with the two methods it usually replaces — paper questionnaires and telephone pre-assessment — including where each of those still wins.

Do patients know they are speaking to an AI?

Yes. The interview opens by identifying Pathways as an automated assistant working on behalf of your practice or hospital, and tells the patient that a clinician will review everything discussed. Patients can ask to speak to a person at any point rather than continue with the assessment.

Disclosure is not a compliance box here, it is what makes the answers usable. A patient who thinks they are filling in a form gives form-shaped answers; a patient who understands they are being interviewed, and that a clinician will read it, tends to volunteer the thing that matters — the stent nobody recorded, the sleep study that was never followed up.

Telling them a clinician reviews the result is also simply accurate. Pathways issues no orders and clears no patients; the summary is a draft until someone qualified signs it.

Is Pathways a medical device, and who is responsible for clinical decisions?

Pathways is clinical decision support intended for use by qualified clinicians within Australian clinical governance frameworks. It is clinician-in-the-loop by design — it issues no automated orders, and every recommendation is reviewable and auditable. The treating clinician remains solely responsible for all clinical decisions.

The positioning is deliberate and is reflected in the architecture rather than only in the terms of service. Pathways produces risk flags with severity tiers, the evidence that triggered them, a guideline reference and a recommended action. It does not place orders, it does not clear patients, and it does not route anyone to theatre on its own authority.

Can we audit why a patient was cleared or flagged?

Yes. Pathways keeps org-wide and per-patient audit logs with full workflow tracing on every risk analysis — the inputs used, the rule version that fired, the output produced, and the human who reviewed it, all timestamped. Logs are exportable for governance committees, M&M review and accreditation.

Because the safety-critical rules are deterministic and versioned, an audit is reproducible rather than merely archived: re-running a case against the rule version recorded at the time produces the same flags. That is the property that lets a decision made in March be examined in September without argument about whether the system “would have said something different”.

Do patients need to download an app?

No. Patients receive a signed, time-limited SMS link and complete the assessment in their phone's browser. There is no app, no login and no password. Links expire, and expired links are visibly dead rather than silently reusable.

Links are signed with HMAC-SHA256 and time-limited. The visible-expiry behaviour matters more than it sounds: a link that silently still works is a data exposure, and a link that fails with no explanation generates a phone call to the practice.

What happens if a patient doesn't complete their assessment?

The tracker shows exactly where they stopped — sent, opened, part-way through, or expired — and automated reminders go out without anyone chasing. A link can be resent in one click. Because the status is visible per patient rather than inferred from silence, an incomplete assessment becomes a task on a list instead of a discovery on the day of surgery.

Non-completion is normal and expected — it is why the tracker exists. The failure mode worth designing against is not the patient who does not finish, but the service that does not know they did not finish until the pre-admission clinic.

Patients who genuinely cannot complete a remote assessment are then a known, named group who can be routed to a phone call or a clinic slot deliberately, rather than turning up unassessed.

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