Clinical safety and patient privacy first.
AI for Healthcare
Healthcare AI has the highest cost of error we work with. Architecture starts from PHI handling and the clinician's role in every decision.
What makes this sector different
PHI governs everything
Where data may go determines which models may be used.
Clinical safety is the bar
A plausible-sounding wrong answer is a patient safety incident.
Administrative load is enormous
Documentation consumes clinical time that should be clinical.
Where it pays back
Clinical documentation support
Retrieval-augmented generationDraft notes from encounters, with the clinician reviewing and signing.
Prior authorisation and coding support
HybridExtract and assemble evidence, surfacing gaps.
Clinical knowledge retrieval
Retrieval-augmented generationAnswer from approved guidelines with the source cited — never from model memory.
Patient communication drafting
Retrieval-augmented generationDraft correspondence for clinical review before sending.
How the Healthcare assessment differs
Healthcare assessments assume PHI and human-in-the-loop on anything clinical until you establish otherwise.
Compliance we ask about first
Data sources we expect
Questions this template pushes on
- Does the workload involve PHI?
- Which outputs require clinician review before use?
- May data be processed outside your own infrastructure?
These are suggestions surfaced alongside the questions — never pre-filled answers. The assessment still asks you everything, because a score built on assumptions you never confirmed is not a score you could act on.
Questions people ask
- Can a model make a clinical decision?
- No, and we would not build one that did. These systems draft, retrieve and surface evidence; a clinician decides. The approval gating in the platform exists to make that structural rather than a matter of policy.
Scope an AI project for healthcare
The Healthcare template sharpens the questions. Ten minutes gets you a feasibility score, an architecture and a costed plan.