First cohort · 5 AI developer slots · Applications open now
Now admitting those pushing Clinical AI forward
Safety scorecards for clinical AI, before go-live
Tap Hospital is where clinical AI proves it is safe and sane with real clinicians and real failure testing before it ever touches a real hospital.
Real clinicians. A production-grade simulated EHR. And a longitudinal layer no sandbox can give you. No IRB. No hospital partnership.
5
Safety Domain Scored
6
Live EHR Modules
0
Real patient data
100%
Synthetic Patients
Why neutral testing, and why now
Sep 2025
URAC launches Health Care AI Accreditation
The first national accreditor moves on AI governance. Health systems now have a framework to answer for.
Jun 2026
Joint Commission opens RUAIH certification
Responsible Use of AI in Healthcare arrives at the hospital's front door. Governance, policy, and oversight become auditable.
Today
The gap: everyone audits paperwork, no one tests the product
Accreditors review governance and policy. Vendors grade their own homework. What's missing is a neutral testing lab that puts clinical AI in front of blinded clinicians, inside a production-grade simulated hospital, before go-live. That is Tap Hospital.
What every product is scored on
Every clinical AI product in the lab is scored across five safety domains, with hard-fail risk gates that can cap or override any composite score. One number never hides a dangerous failure.
01
Clinical Accuracy
Does it get the medicine right, on complex and edge cases, not just the happy path?
02
Security & Privacy
How it handles data, tokens, and access when systems misbehave.
03
Human Validation
Blinded clinician acceptance, override patterns, and trust under pressure.
04
Workflow Integration
Whether it survives a real clinical workflow, or quietly breaks it.
05
Bias & Equity
Performance divergence across patient populations, surfaced before go-live.
APPLY NOW
Prove your AI works with real clinicians, before you burn your one hospital shot
Free sandboxes tell you if your code runs. tapHospital tells you if a clinician trusts it, and exactly where it breaks. Blinded clinicians, adversarial failure testing, and an isolated EHR environment, all before you enter a single procurement cycle.
done
Blinded clinician panel: structured signal on acceptance, overrides, and failure points, not just API responses
done
Failure engine: malformed bundles, missing allergies, conflicting meds, expired tokens. The edge cases that kill real pilots
done
Isolated per-developer EHR instance with IP protection. Your product, your data, vendor-neutral
done
Complementary to Synthea and SMART on FHIR. Use them for code, come here to prove it survives a real workflow
done
5 products only in Cohort 1: curated, not crowded
Failure engine simulating clinical edge cases
HOW IT WORKS
From Sign-up to Signal
01
Join the cohort
Get an isolated EHR instance with IP protection and integrate your product into the actual clinical interface.
02
Work real scenarios and break them
The failure engine throws malformed data, missing allergies, conflicting meds, and expired tokens at your product. The edge cases real hospitals surface on day one.
03
Walk away with real signal
Acceptance rates, override patterns, and a safety scorecard across five domains. The evidence a health system asks for before it says yes.
Need more than the cohort?
Health systems, academic groups, and AI teams can commission a custom research environment: an isolated, production-grade simulated hospital configured to your workflows, populations, and failure scenarios. Zero real PHI, no IRB required.
FAQs
Frequently Asked Questions
Safety scorecards for clinical AI, before go-live

An independent testing lab for clinical AI. Products run inside a production-grade simulated hospital, complete with EHR, radiology, lab, telehealth, and patient portal, populated entirely with synthetic patients. Licensed clinicians work blinded against your product in live scenarios. You leave with a safety scorecard across five domains, before the product ever touches a real patient.

Two groups, clinical AI developers and health systems. Developers who need independent, reproducible evidence of clinical safety before a first hospital pilot. And health systems and academic groups who need neutral third-party validation of a vendor’s claims, for procurement due diligence and for readiness against Joint Commission RUAIH and URAC.

Sandboxes tell you whether your code runs. We tell you whether a clinician trusts the output, and exactly where it breaks. We use Synthea ourselves to generate the patient population. The difference is what sits on top of it: a longitudinal EHR, an adversarial failure layer, and blinded clinical review. Complementary, not competing.

No. Tap Hospital performs testing and validation. We report what we found, how we found it, and what it means, in a scorecard and a written explanation across five domains. We do not issue a seal of approval and we are not a certification body. The distinction matters. A certification asks you to trust the issuer. Our report lets you inspect the method and the evidence yourself, and tells you which scenarios broke the product, so you can act on it.

Worth stating plainly, our testing is not a step outside the accreditation path. It is the evidence that path runs on. Certification schemes and hospital AI governance committees increasingly ask for independent testing performed by someone other than the vendor and buyer, and that is exactly what a Tap Hospital engagement produces.