How We Evaluate Whether a Healthcare AI Is Ready for Real Clinical Workflows

Clinical review led by Dr. dr. Alfian Wika Cahyono, M.Biomed — a doctor focused on developing healthcare AI technology and products in Indonesia.

Agentic AI has moved from demos into real clinical deployment. Institutions are adopting it to improve documentation accuracy, clinical efficiency, and patient communication — and now have to judge whether a given system is ready for that work, with limited time, few clinicians to spare, and no research-lab budget. The question underneath every pitch is the same: how do you evaluate a healthcare AI rigorously, across the dimensions that actually matter — and how do you even decide which dimensions those are?

A demo can’t tell you whether a healthcare AI is safe to put in front of a doctor. It shows one good report, and tells you nothing about the hundredth patient — whether every output is complete, whether the same case stays stable on a rerun, whether each line traces back to a finding, whether a doctor can review and approve it quickly. That decision takes evidence, and the field has lacked a shared way to produce it.

So we built one: the method we use to decide whether AI-generated clinical documentation is ready for real, doctor-supervised workflows. It came out of our work on Indonesian medical check-ups (MCU) and was refined over many rounds with institutions and clinical advisors. We are sharing the method — and the first open benchmark that runs it — so any institution can evaluate the same way. The full V1 report is here.

What the method is

The method answers two questions, in order. Can the output enter a workflow at all? And is it clinically and locally right? It runs in stages — each one cheaper than a full clinical review, each one narrowing what a person has to look at.

A scope freeze fixes the workflow, prompt, schema, and model so results are comparable. A machine-side gate then checks the basics a system needs to run in production: valid, complete output; the required fields; a valid fitness-for-work label (whether the person is medically cleared for their job); zero machine-critical errors; and repeat-stability — the same case returning the same decision when you run it again.

Running cleanly is only half the test. Whether the output is actually right is scored against a published, weighted clinical and workflow rubric — five dimensions, 24 individually-scored criteria, and three safety blockers that auto-fail an output, for 100 points:

The bands are explicit: any safety blocker auto-fails the output; otherwise 80 or above with no critical issue is a POC candidate, 70–79 is monitor, and below 70 is remediation.

The rubric is grounded. It uses the physician-written, case-by-case approach proven by HealthBench — the medical-AI benchmark where clinicians, rather than a multiple-choice key, define what a good answer is — and its clinical thresholds rest on a layered reference stack:

• Recognized clinical parameters — WHO diabetes definitions, WHO hypertension guidance, and the WHO Expert Consultation BMI action points for Asian populations, alongside local laboratory reference ranges.
• Indonesian occupational-health law — UU No. 1 Tahun 1970, PER-02/MEN/1980, and Permenaker No. 5 Tahun 2018, which govern fit-to-work and worker health under Indonesia’s occupational safety and health framework (K3 / Hiperkes).
• Institution SOP and local expert interpretation — each institution’s own reporting, referral, and sign-off rules, with disputed cases adjudicated by local clinical and occupational-health reviewers.

The method itself follows the frameworks regulators and clinicians already use for health AI: WHO guidance, the NIST AI Risk Management Framework, medical-device evaluation practice (IMDRF / GMLP), and the clinical-AI reporting standards (DECIDE-AI, CONSORT-AI, TRIPOD+AI). A no-self cross-review — where the reviewer is never the model that produced the output — then surfaces the disputed cases, and local clinical experts adjudicate them.

Why a multi-model benchmark

The workflow is the product; the model is a component we can replace. So we hold the workflow, prompt, schema, and rubric fixed and run the same workflow across many foundation models from different vendors — twelve in V1. The purpose runs deeper than scoring each model.

A workflow that only one model can pass is fragile. Models get deprecated, repriced, rate-limited, restricted in a region, or regress on an update — all outside your control. If a clinical workflow depends on a single model, any of those events can break it.

The goal is broader than crowning a winner: confirm that several qualified models can run the same workflow to the same standard. When several clear the bar, you hold a portfolio — if one becomes unavailable, too costly, or unsuitable, you switch to another that already meets it, without rebuilding the workflow. Testing across vendors, rather than within one family, is what makes that fallback real. Multi-model evaluation is part of the method: it makes the workflow resilient instead of a bet on one provider.

What it’s worth to you

For an institution, the method turns an AI decision from a demo impression into evidence you can act on:

• You shortlist what to pilot on proof across many real cases, so a polished demo no longer drives the decision.
• You turn the results into concrete POC and go-live acceptance criteria.
• You point scarce doctor time at the high-risk and disputed cases the method surfaces, instead of re-reading everything.
• You compare models and vendors on one inspectable rubric — in your own language, terminology, and rules.
• You avoid single-model lock-in: with several qualified models, your workflow keeps running if one is deprecated, repriced, or restricted.

How to use it

The rubric is the core, and it belongs to the institution: what “correct” means depends on your check-up packages, your fit-to-work rules, and your SOP. There are two ways to run it:

• Your clinical, quality, and occupational-health owners write the rubric; the AI provider reviews it and flags anything unclear or unfair; then the agent is tested against it.
• You start from our reference rubric and adapt it to your scenario.

Either way, your local experts adjudicate the disputed cases, and the vendor never grades its own work.

How we can help

We can step in at whatever point you’re at:

• Share the method and the reference rubric from this benchmark.
• Help you adapt the rubric to your scenario and SOP.
• Run the staged evaluation on the models or agents you are considering.
• Bring clinical review — led by Dr. dr. Alfian Wika Cahyono, M.Biomed, a doctor focused on developing healthcare AI technology and products in Indonesia — with local reviewers for the disputed cases.

Our practice: the first benchmark, on Indonesian MCU

We ran the method first on a hard, high-volume workflow — Indonesian MCU reporting — across a twelve-model slate from different vendors, with the workflow held fixed and only the model varied.

Several models cleared the gate in both cohorts — the outcome we want. It means the workflow runs on a choice of qualified providers, so one model’s deprecation, price change, or regional restriction won’t stall it.

A machine-side pass certifies one thing: the output is structured, complete, and stable enough to enter a workflow. Clinical correctness is a separate verdict, reached through the rubric and local experts. V1 is a pilot-scale signal on one anonymized corpus under one fixed prompt; we keep it honest by publishing the protocol, anonymizing lower-tier results, and sending clinical judgment to independent and local expert review.

The agent under test is MCU CoPilot — Micromeet AI for MCU — which drafts structured medical check-up reports from documented inputs for a doctor to review. Specialist findings — ECG, imaging, audiometry, spirometry — are carried as reported by the responsible clinician, and Micromeet's MCU CoPilot stays a documentation agent under doctor supervision, not a clinical decision support system (CDSS). This is Micromeet — AI for governed healthcare: AI writes. Doctors decide.

Beyond MCU

MCU is the first edition. The stages, the rubric, and the institution-owned process are workflow-shaped, so the same evaluation applies to other healthcare-AI documentation tasks that have to enter doctor-supervised care. The method is what we are really sharing; MCU Indonesia shows it works on real data, in a real market’s language and rules. The next step — local expert adjudication of the V1 disputed cases — will be reported in a V1.1 update.

If you are weighing AI for your own clinical workflows, we are glad to share the method and the reference rubric, and to help you adapt and run it. Start with the full V1 report →

FAQ

What is the Indonesia MCU Healthcare AI Agent Readiness Benchmark? It is the first open benchmark to run Micromeet's method for deciding whether AI-generated clinical documentation is ready for real, doctor-supervised workflows. V1 holds one Indonesian medical check-up (MCU) reporting workflow fixed — the same prompt, schema, and rubric — and runs it across twelve foundation models from different vendors, with a staged machine-side gate, a published 100-point clinical and workflow rubric, and no-self cross-review. The full V1 report lives at https://www.micromeet.ai/benchmark/index.html.

How is this different from a model leaderboard? A leaderboard ranks raw model capability. This evaluates whether a specific workflow — one prompt, one schema, one model — is ready for doctor-supervised use, and routes the clinical judgment to expert review.

What does a “pass” mean? A machine-side pass is a structural and operational screen: valid, complete, stable output that can enter a workflow. Clinical correctness is judged against the rubric and by local experts.

Who owns the rubric? The institution. We provide a reference rubric and the method; your local experts decide what is correct for your scenario. Clinical review for V1 is led by Dr. dr. Alfian Wika Cahyono, M.Biomed.

How does Micromeet help institutions evaluate healthcare AI? Micromeet shares the method and the reference rubric from this benchmark, helps adapt the rubric to an institution's own scenario and standard operating procedures, runs the staged evaluation on the models or agents under consideration, and brings clinical review — with local experts adjudicating the disputed cases. The same governed healthcare AI principle applies to the agent under test: MCU CoPilot drafts structured medical check-up reports for a doctor to review. AI writes. Doctors decide.

About Micromeet AI — Micromeet AI builds governed healthcare AI infrastructure for continuous care: a runtime where institutions, clinicians, and AI agents share patient context across clinical documentation (Voice-to-EMR), institution operations (AI Care Command Center), continuity of care (Care Loop), and payer readiness (Claim Readiness). Backed by Microware Group (1985.HK).

V1 reports an automated, machine-side evaluation across anonymized Indonesian MCU cases; clinical review of disputed cases is led by Dr. dr. Alfian Wika Cahyono, M.Biomed. The full method, pass gate, rubric, and references are in the V1 benchmark report.