AI oversight built by a nurse
Nurses catch AI mistakes every day. No one tracks them.
ClinicFlow is built to turn those catches into a clear record, so you can show your AI is safe after go-live.
Now accepting a few design partners.
At the bedside
Two taps. The save is on the record.
When a nurse overrides an AI alert, ClinicFlow is designed to open beside the chart and ask one question: why? Tap a reason, tap Save. That judgment becomes data your committee can use.
"Finish later" never blocks care.
The problem
AI is validated once. It fails at the seam.
AI tools are tested in isolation, then fail where they meet real patients and real workflows. Your clinicians catch those failures every day. The save is never recorded.
That leaves you signing off on AI you can't see.
A clinician's "no, that's wrong" should never disappear.
What's at stake
Alert fatigue
Alerts that fire too often get tuned out.
Blame lands at the bedside
With no record of what the AI said, accountability falls on whoever was there.
Patient harm
An alert quietly degrades, and a decline is missed or caught late.
Silent change
A vendor update changes the model. No one notices until something goes wrong.
Falling behind
Accreditors now recommend monitoring AI after go-live. Boards and patients will expect it too.
Sources: Joint Commission and Coalition for Health AI voluntary guidance (Sept 17, 2025); Joint Commission voluntary Responsible Use of AI in Healthcare certification (June 1, 2026). References do not imply affiliation or endorsement.
Free AI Oversight Readiness Assessment
A short questionnaire and a 45-minute call with our founder. Within a week, a written report: your AI inventory, how you compare with emerging national guidance, your top three gaps, and where to start.
Why I built ClinicFlow
I was the nurse handed an AI tool and told to trust it.
AI tools get validated in isolation. No one examines the seam where the tool meets people, workflows, and clinical judgment. That's where things break.
When AI gets it wrong, a nurse or doctor usually catches it and moves on. No one keeps track of that save. Too often, an AI failure is treated as a liability to hide. I treat it as the most important thing that happened.
I'm a nurse and a woman founder in a field where too few of the people building the technology have stood at a bedside.
Registered nurse (progressive care and ICU stepdown) and U.S. Navy veteran, trained as a sonar technician to catch faint signals before they become failures. Her framework is the subject of two U.S. provisional patent applications.
BSN, cum laude, Arizona State University · MSN in progress · Stanford AI in Healthcare certificate in progress · IHI Open School · Member: ANA, ANIA, AMIA
How it works
A second set of eyes on the AI you already use
ClinicFlow is designed to spot when an AI tool fails, record how and why, and hand it to the people responsible for safety. It doesn't replace your AI or slow anyone down.
Two-tap override capture
A reason in about two taps. No extra charting.
Silent degradation detection
Designed to flag shifts in alert or override patterns as they happen.
Alert fatigue measurement
See where an alert fires too often to be trusted.
Wellness Score™
One signal per AI system, combining overrides, degradation, and alert fatigue.
Monthly committee reports
Built to cut the hours spent pulling reports.
Audit trail
Who acted, on what, and when.
Today vs. with ClinicFlow
| Today | With ClinicFlow (as designed) |
|---|---|
| Validated once, then trusted | Watched every day after go-live |
| Override reasons lost in free text | Structured reasons in about two taps |
| Problems surface after a near-miss | Changes flagged as they happen |
| No way to show how AI is overseen | A clear record for clinical, quality, and risk teams |
Works with what you have
ClinicFlow is designed to strengthen your existing tools, not replace them.
Vendor and EHR validation tools
ClinicFlow adds: why your clinicians chose not to follow the model.
AI governance and risk platforms
ClinicFlow adds: whether that AI still gets it right for your patients after go-live.
Technical monitoring tools
ClinicFlow adds: bedside judgment, translated for clinical and quality leaders.
Incident reporting systems
ClinicFlow adds: everyday overrides, long before an incident report.
The difference is the source. Our patent-pending Failure Ecology™ Framework treats clinician overrides as structured signals, grounded in nursing ethics.
Who it's for
CMIOs
You signed off on AI that passed validation once. ClinicFlow is built to give you an independent view of how it performs every day after go-live.
Quality and safety leaders
If AI causes harm you can't see, your safety program has a blind spot. ClinicFlow is designed to turn clinician overrides into a record you can report on.
How to get started
Apply
Tell us about your organization and the AI alert that concerns you most.
Scope it together
Pick a champion, one alert model, and what success looks like.
Shape it and prove it
Meet monthly, see what overrides reveal, and co-author the results.
- BAA signed before any patient data is shared
- Your data never trains models
- We lead setup and integration
- Override notes never block care
- We monitor the AI, not your clinicians
- Success measures agreed up front
Design Partner Program
Help shape how clinical AI is governed.
We're inviting a small group of health systems and hospitals to monitor one AI alert model with us and build the oversight their teams need. Two ways to join:
Validation Partner
Help prove the method in a complex, multi-hospital setting.
- No-cost access during the prototype phase
- Learning goals, data rights, and publication terms agreed in writing up front
- A fixed scope: one AI alert model
- Working sessions with our founder that shape the roadmap
- The chance to co-author a publication on the results
Founding Partner
Be among the first to bring ClinicFlow into everyday oversight.
- A paid pilot on one AI alert model, with the fee credited toward your first year
- Founding-partner pricing locked for your first contract
- Hands-on setup through your first committee review
- Working sessions with our founder that shape the roadmap
- The chance to co-author a case study
What we ask
- A named champion, such as a CNO or nursing informatics leader
- A kickoff and about an hour a month of feedback
- One AI alert model, with no patient data wherever possible
- Success measures agreed before the pilot starts
- Serving as a reference if the pilot meets its goals
What we'll measure together
- Overrides captured with a structured reason
- How fast a change in the alert's behavior is flagged
- Committee prep time against your own baseline
- Whether the output supports a board or quality-committee presentation
- Whether clinicians feel their override reasons are heard
Questions
Does ClinicFlow monitor or grade our nurses?
No. ClinicFlow monitors the AI, not your clinicians. An override is recorded as clinical judgment, never graded as an error.
Will this add work for clinicians?
It's designed not to. A reason takes about two taps, typing is optional, and "Finish later" never blocks care.
Our AI vendor already monitors its model. Why do we need ClinicFlow?
Vendor monitoring shows how a model performs on data. It can't show why your clinicians chose not to follow it. ClinicFlow adds that view, independent of any vendor.
Is this another tool to manage?
ClinicFlow is designed to strengthen the tools you already have, not replace them. It covers the one gap they don't: clinician judgment after go-live.
Can't our informatics team build this?
Many teams can build a prototype. Keeping it running and updated across alerts and sites is the hard part. ClinicFlow is built to carry that work.
How is ClinicFlow different from other AI governance tools?
Many tools look at AI from the data side. ClinicFlow looks through clinician judgment, turning overrides into insight your committee can act on.
Does ClinicFlow replace clinical judgment?
Never. ClinicFlow informs and never overrides. Licensed professionals remain responsible for every care decision.
Is our data used to train models?
No. We never use customer data to train ClinicFlow or third-party models.
How do you protect patient data?
ClinicFlow is built for healthcare from day one. We sign a Business Associate Agreement before any patient data is shared, never use customer data to train models, and are designing the platform around HIPAA safeguards, encryption in transit and at rest, U.S.-only hosting, and SOC 2 Type II controls. During the design partner phase, we work with no patient data wherever possible.