Home Solutions
Different products fail differently.
A coding assistant and a triage assistant do not break in the same ways, and should not be reviewed as though they do. Review criteria, escalation rules and reviewer mix are configured for the surface you are actually shipping.
Segment 01
Clinical decision support
Diagnostic assistance, treatment recommendation, risk stratification, guideline navigation — products whose output a clinician will act on.
Where these products get stuck
The demo goes well. Then the health system's clinical governance committee asks how outputs are validated, who is accountable when one is wrong, and what evidence exists that the system performs as claimed on their population. Without answers, the pilot stretches and the contract does not close.
- Independent clinical evidence rather than self-reported benchmarks
- A named, credentialed reviewer behind sampled recommendations
- Records your customer's safety committee can actually read
- Error rates segmented by specialty, so weak areas are visible early
Draft recommended standard dosing of a renally cleared agent for a patient with eGFR 28, without adjustment.
Reviewer: dose reduction is required at this level of renal function. Corrected, with the label section cited in the record.
Segment 02
Ambient documentation & coding
Encounter notes, summaries, letters and codes generated from conversation — high volume, and quietly consequential.
The error rate you ship, not the one you benchmark
Documentation errors are rarely dramatic. They are an omitted allergy, a subtly wrong laterality, an over-specified diagnosis that changes a code. They accumulate in the record, and internal evaluation on curated data will not surface them at the rate real encounters do.
- Sampled clinician review to establish a real, defensible error rate
- Omission detection — what the note left out, not just what it got wrong
- Coding accuracy checked against documented clinical support
- Trend tracking across model versions so regressions are caught
| Scope | Sampled share of encounter notes |
| Checked | Omission · laterality · allergy capture · medication list |
| Coding | Documented support for each submitted code |
| Output | Error rate by clinic, task type and model version |
Segment 03
Patient-facing health AI
Symptom guidance, triage, medication questions and post-visit explanation — where the reader has no clinical training and no way to catch an error.
No expert in the loop by default
When a clinician reads a wrong AI output, there is a reasonable chance they catch it. When a patient reads one, there is not. That asymmetry justifies tighter review criteria, lower escalation thresholds and specific attention to whether guidance to seek urgent care was correctly given or wrongly withheld.
- Red-flag detection — was escalation advised when it should have been?
- Readability and comprehension alongside clinical correctness
- Scope discipline — refusing to answer what should not be answered
- Tighter thresholds for second review on high-severity presentations
User reported sudden severe headache described as the worst of their life. Draft suggested rest, fluids and monitoring at home.
Reviewer: thunderclap headache requires immediate emergency assessment. Guidance replaced and the failure logged as a missed red flag.
Segment 04
Health system & payer AI teams
Internal models for utilisation review, prior authorisation, care management and population risk — built in-house, and held to the same standard as anything bought.
Independence is the point
An internal model validated by the team that built it satisfies nobody's governance requirement. External clinical review provides the separation that internal quality processes structurally cannot, and produces documentation that holds up when a decision is challenged by a member, a provider or a regulator.
- Independent review separate from the building team
- Documentation aligned to internal AI governance requirements
- Ongoing monitoring rather than a one-off validation exercise
- Evidence trail for decisions that get appealed
| Independence | Reviewers external to the model owner |
| Cadence | Continuous sampling, not a point-in-time audit |
| Coverage | Decision categories weighted by member impact |
| Output | Governance-ready documentation and trend reporting |
Building something not on this list?
These four are where we have gone deepest, not the limit of what verification applies to. If AI output in your product reaches a clinical decision, there is a version of this that fits.
Early access
Start with your hardest cases.
Design partners route a real sample and get a measured baseline back. That is a more useful conversation than any deck.