Framework · Evaluation criteria
The seven-criteria evaluation framework, and how to reweight it
The rubric behind every score on this site, published so you can adopt it, argue with it, or bend it to a buyer who is not the one we assume.
Short answer
What criteria should you use to evaluate clinical documentation AI vendors?
Seven, weighted for a software team that must integrate and resell: API and SDK maturity 22%, clinical accuracy and output quality 20%, EHR and FHIR interoperability 16%, compliance and security 16%, latency and streaming 10%, specialty and template coverage 8%, and commercial terms and transparency 8%. The weights encode who the buyer is: a health system evaluating a finished clinician application should raise accuracy and coverage and lower API maturity, which changes the ranking order.
Cite as: Seven-criteria evaluation framework, Compare Healthcare API, last reviewed 2026-09-01.
Key findings
- Weights are an argument about the buyer, not a fact about the market. Publish them or your ranking is unfalsifiable.
- API maturity carries the highest weight here because it is the criterion that most reliably predicts total integration cost for an embedding team.
- Accuracy is weighted heavily but scored conservatively, because the public evidence base does not support precise accuracy claims.
- Two criteria — interoperability and compliance — are where deals actually die, which is why they carry 32% between them.
- A framework that produces the same winner under every weighting is not a framework; it is a preference.
Why weighted scoring rather than a feature grid
Feature grids imply every row matters equally, which is never true. Weighted scoring forces the evaluator to state what matters and by how much, which makes the conclusion contestable in a productive way: a reader who disagrees can point at a weight rather than at a vibe.
It also survives new entrants. When a vendor appears, you score it on the same seven criteria rather than redesigning the comparison around whatever it happens to be good at.
Adapting the weights to your buyer
Three worked reweightings that produce different, defensible winners.
For a health system buying a clinician-facing product, raise clinical accuracy toward 30% and specialty coverage toward 15%, and cut API maturity to single digits. Enterprise application vendors rise sharply under that weighting, and they should.
For a team building its own documentation layer, raise latency and API maturity, cut coverage and clinical output to near zero, and the ranking collapses into a straight ASR comparison led by the engines with the best developer experience and pricing.
For a company selling into large health systems as a startup, raise compliance toward 25%: the constraint is surviving security review, and a vendor whose evidence is thin will cost you deals no feature can win back.
Scoring discipline
Score each criterion independently before computing a total, and record the evidence for each score with its tier. If a fact is not published, write 'not disclosed' rather than estimating — an undisclosed retention window is a finding in itself, not a gap to fill with a guess.
Re-score on a schedule and stamp the date. In a category moving this fast, an undated score is misinformation within two quarters.
Applying the framework to your own shortlist
1Name your buyer in one sentence
Who is the user, who signs, and what must the product own? Every weight follows from this.
2Set weights before seeing vendors
Assigning weights after demos guarantees you will rationalise a favourite.
3Collect evidence by criterion
Public documentation first. Record the URL and the tier beside every score.
4Score independently, then total
No adjusting a sub-score because the total looks wrong; that is how brand bias enters.
5Write the limitations of your winner
If you cannot list three, you have not evaluated it — you have chosen it.
6Publish the weighting with the decision
Internally or externally, the weighting is what makes the decision reviewable in a year.
Frequently asked questions
- Can I reuse this framework for my own vendor selection?
- Yes. The criteria, definitions, weights and evidence tiers are published for reuse with attribution, and the vendor selector will apply your own weights to every vendor's sub-scores and export a citable report. Open the selector.
- Why is API maturity weighted highest?
- Because for a team embedding documentation into its own product, API maturity is the strongest predictor of total integration cost and of whether the feature can be maintained at all. For a health system buying a finished application, it would be weighted far lower. Methodology.
- How do you score accuracy without benchmarks?
- Conservatively, and transparently. Scores reflect documented posture — medical vocabulary handling, diarization support, template adherence, missing-information behaviour, published evaluation practice — not a measured word error rate, because no vendor publishes a reproducible one. How to test accuracy yourself.
Evidence & sources
Every factual claim on this page traces to one of the primary references below. Each entry records what it supports and its evidence tier, so documentation can be told apart from judgement.
NIST speech recognition evaluation literature · Methodology · Tier B — published methodology
Supports: Why a headline WER figure without a stated corpus, audio condition and reference-transcript protocol is not comparable across vendors.
NIST · Standard · Tier A — primary documentation
Supports: A defensible structure for governing an AI documentation feature you ship to clinicians.
AICPA · Standard · Tier A — primary documentation
Supports: What a SOC 2 Type II report does and does not attest to during a vendor security review.
- [4]HITRUST CSF
HITRUST Alliance · Standard · Tier A — primary documentation
Supports: The certification many health systems require of documentation vendors handling PHI at scale.
HL7 International · Standard · Tier A — primary documentation
Supports: Resource definitions (DocumentReference, Composition, Encounter, Condition, MedicationRequest) that clinical documentation output must map onto.
U.S. Department of Health & Human Services · Regulation · Tier A — primary documentation
Supports: The contractual clauses a documentation vendor's BAA must contain.
American Medical Association · Research · Tier B — published methodology
Supports: Professional expectations for oversight and transparency of AI-generated clinical content.
Source tiers are defined on the methodology page. Outbound links are unaffiliated and carry no commercial relationship.