MicromeetAI
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Procurement

How do you evaluate a healthcare AI vendor?

Evaluate a healthcare AI vendor on evidence from your own cases (completeness, rerun stability, traceability, safety, review burden), then pilot it.

Put the evidence in context

Review the intended task, source information, human checkpoints and observed results together. A benchmark or sandbox result has a defined scope; it does not establish suitability, approval or performance for every institution.

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Discuss this step for your team

The core mistake in healthcare AI procurement is buying on a polished demo. The durable approach is to require evidence on each of the eight readiness points, applied to your own cases and rules:

  • Structured-output proof — valid, complete, schema-stable output across many representative cases, not one demo screen.
  • Repeat stability — the same case returns the same core conclusions on a rerun.
  • Evidence traceability — every conclusion traces back to a source finding or a reference rule.
  • Safety boundaries — no fabricated findings, no unsupported reassurance, consistent escalation.
  • Localization fit — output usable in your language and local terminology.
  • Doctor review burden — measure whether an authorized clinician can inspect, edit, or reject the draft within the intended workflow.
  • Independent review — outputs reviewed by someone other than the system that produced them.
  • Change control — a rerun and regression check after any prompt, rule, or model change.

Define the intended use, exclusions, pass criteria, escalation path, monitoring owner, and rollback condition before testing. Include routine and difficult cases, then run a bounded pilot in the real workflow. Re-evaluate after material model, prompt, rule, data, or integration changes; a one-time benchmark is not permanent production approval.

This page covers whether the vendor's output works. Other parts of the evaluation have their own pages: which documents to request, Indonesia-specific compliance checks, PSE registration, and data-security questions for hospital IT.

Micromeet publishes the Indonesia MCU Healthcare AI Agent Readiness Benchmark — 12 foundation models, 30 synthetic-regenerated Indonesian MCU cases, published pass gates and a 24-criterion rubric — built to run this method. V1 measures structural readiness under its published method; it does not establish clinical accuracy or permanent production approval. Read the full report at micromeet.ai/benchmark/index.html. Micromeet builds healthcare AI infrastructure for continuous care. Clinical judgment stays with clinicians; the AI does not diagnose or sign.

Related questions

Why isn't a vendor demo enough?+
A demo is a single curated output. It cannot show whether outputs remain structurally complete across tested cases, whether the same case stays stable on a rerun, whether each line traces to a finding, or whether the clinician review burden is acceptable. Those require evidence across representative cases, using anonymized data when case material is involved.
Can I use Micromeet's benchmark to evaluate other vendors?+
Yes — that is the point. The benchmark includes an eight-point readiness checklist any institution can apply to any AI documentation vendor, including Micromeet. If a vendor can only show a polished demo, the checklist tells you what evidence to ask for before a pilot.
What should we verify before deploying a healthcare AI vendor in production?+
Verify readiness evidence on your intended workflow, security and data-governance terms, named monitoring and escalation owners, and a tested rollback path. Start with a bounded pilot, then re-evaluate after material model, prompt, rule, data, or integration changes.

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