Free checklist for healthcare founders
Your clinical AI works.
Do you know how it fails?
Twenty three checks that decide whether your system survives contact with a real hospital. Not retrieval metrics. The layer underneath, where reliability quietly dies.
Vantage IO builds healthcare software. Senior engineers embed with your team to build, fix, and scale clinical systems and AI that hold up in a hospital.
Get the checklist- Five sections, twenty three checks, built to print and score with your team
- Written from failure patterns in real production clinical AI
- Have your ML lead and clinical lead score it separately, then compare
Sam Morhaim Founder & CTO, Vantage IO. 25 years shipping software.
Free download
The Clinical AI Reliability Checklist
Healthcare teams we have built with
What Vantage IO does
We are the engineering team healthcare companies bring in when the software has to be right.
Senior engineers who embed with your team and ship. We work with healthcare and health tech companies on the systems that carry clinical weight, from the first build through hospital procurement and scale. The checklist below comes out of that work.
Product leadership and direction
Senior technical leadership from someone who has shipped clinical software before, sitting with your team and owning the calls.
Zero to one platform builds
We take a healthcare product from idea to a working system real clinicians and patients use, compliant from the first commit.
Codebase stabilization
Fragile or AI generated code rebuilt into architecture that holds under load, under review, and under a security questionnaire.
Clinical AI and LLM reliability
We find where your AI breaks under real clinical pressure, then design those failure modes out of the system.
Evidence retrieval systems
Retrieval over clinical literature and patient data, built so every answer traces back to the source it came from.
What is inside
Five sections. Twenty three checks.
Most teams pass the demo and fail the checklist.
Most clinical AI teams measure retrieval and ship. Then a clinician flags a wrong answer, the team traces it back, and the retrieval was perfect. The model just did not say what the document said. This is the layer underneath.
Get the definition of "wrong" right
"It hallucinated" is not measurable. Four failure types, four different fixes.
Build a test set that looks like reality
Vague questions, disagreeing sources, edge populations, and validated answers.
Measure interpretation, not similarity
Faithfulness, qualifier preservation, population fit, refusal calibration.
Design the failure modes out
Bounded synthesis, source tied output, population matching, calibrated confidence.
Prove it holds, in test and in production
Regression discipline, exact replay, live telemetry, failures routed back.
Beyond the checklist
AI reliability is usually not the only thing on the list.
I spend most of my time talking with healthcare and health tech founders about what is actually slowing their teams down. It is rarely one clean problem. These are the six that come up most.
Product and technical roadblocks
The build stalled, the architecture is fighting you, or nobody can say why shipping got slow.
HIPAA, security, and compliance readiness
A questionnaire landed and half the answers do not exist yet. Compliance was going to be a later problem.
Hospital integrations and clinical workflows
The integration works in a sandbox. Nobody has watched a real clinician try to use it mid-shift.
AI reliability and healthcare data
The demo lands every time. What happens under real clinical pressure is still a guess.
Scaling an existing platform
It held at ten customers. At a hundred the cracks are structural, not incidental.
Investor and partner diligence
Someone technical is about to look closely at what you built, and you want to know what they will find.
No pitch, no deck
Take the checklist.
Then bring me the gaps.
Twenty minutes. You tell me what your team is running into, whether that is reliability, compliance, an integration, or scale. I will tell you what I would look at first. If it turns into work, good. If it does not, you still leave with a clearer picture.
