Run the pre-admission service your waiting list needs.

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.

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.

No. Pathways runs standalone from day one. The SMS-driven workflow needs no patient management or EMR integration to start, so a service can screen its next elective list without an IT project, an interface engine, or a change window. Integration is available later but is not a precondition.

This is usually the difference between a deployment measured in days and one measured in quarters. Most of the value — screening every booked patient early enough to act — does not depend on a bidirectional interface, so requiring one up front only delays the point at which the first cancellation is prevented.

When you do want it, integration is HL7 or FHIR against your patient administration and EMR systems: automated list ingestion from theatre scheduling in one direction, and write-back of the signed pre-operative summary to the patient record in the other. That is an Enterprise capability, available on Business, and it is scoped after the standalone deployment is running rather than before it.

Pathways screens each case with deterministic, versioned rules referenced to perioperative guidelines. Every flag carries a severity tier, the clinical rationale, the triggering evidence, the guideline reference and a recommended action, plus the rule version that produced it. The same inputs always produce the same flags, months later.

Screening covers the domains that drive perioperative outcomes: unstable cardiac symptoms, marked hypertension, diabetes control and SGLT2 management, obstructive sleep apnoea, frailty, malnutrition, functional capacity, smoking and discharge support.

Pathways also states what it does not know. Missing investigations and unconfirmed timings are listed explicitly on every profile, and extraction confidence is shown rather than hidden — a profile that is 86% confident about a patient-reported HbA1c says so.

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”.

They proceed. Every completed profile carries a triage recommendation — anaesthetist review, pre-admission clinic, telephone review, or no further assessment — so clinic slots go to moderate-risk patients and anaesthetist time goes to red flags. Low-risk patients are not routed into a clinic appointment they do not need.

This is the throughput argument, and it runs in both directions. A pre-admission clinic booked out three weeks ahead with mostly healthy ASA 1 patients is not short of capacity so much as misallocating it. Screening every patient at booking makes the queue orderable by risk rather than by whoever called first.

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.

They are identified as a group rather than missed. The assessment needs only a phone that receives SMS and opens a link — no app, no login, no account. Patients who cannot complete it that way show as incomplete on the tracker, so the service can route them to a telephone assessment or a clinic slot deliberately instead of discovering the gap later.

The design point is that remote assessment does not have to work for every patient to be worth doing. It has to work for enough of them that the clinic’s finite capacity can be pointed at the ones it does not work for — which includes patients without a suitable phone, patients who need an interpreter, and patients who would simply rather come in.

What Pathways changes is that this group is visible in advance and small enough to plan around, instead of being indistinguishable from everyone else on the list.