Portfolio Accelerator · Healthcare · Microsoft Azure
AI Clinical Copilot Implementation Partner: A Working Accelerator, Not a Deck
A real, working clinical copilot built on Azure FHIR R4 and GPT-4o, with clinical copilot adoption governance designed in from day one.
problem
What This Accelerator Proves Before You Sign a SOW
Most clinical AI copilot vendors and consulting firms describe governance, grounding, and clinician oversight in the abstract. This accelerator is a working system you can watch run: a clinician asks a plain-English question about a patient, the platform retrieves the relevant FHIR record, reasons over it with retrieval-augmented generation, and returns a cited, grounded answer with a mandatory clinician-verification disclaimer, not a diagnosis.
demo
Inside the Clinical Copilot Build: Architecture and Data Flow
This page showcases Kriv AI's clinical copilot accelerator, deployed on real Azure infrastructure against synthetic patient data. It runs two ways: an interactive CLI for developers and a React dashboard backed by a FastAPI service.
Synthetic Clinical Data, Real Cloud Infrastructure
The copilot connects to a live FHIR R4 record in Azure Health Data Services, built and loaded entirely with synthetic patients. No real PHI is used, and one-command deploy and teardown scripts take the environment from an empty subscription to a working dashboard and back to zero cost.
What the Copilot Actually Does: Core Capabilities
Four capabilities are built and tested: Clinical Q&A (a cited, plain-English answer grounded in the patient's record), a Medication Safety check (a GREEN/YELLOW/RED interaction and contraindication check against the patient's allergies and current medications), a Risk Dashboard (a ranked list of patients scored 1-10 by risk level), and Lab Trends (observations grouped over time with an AI interpretation of the trajectory). All four are grounded in the FHIR record; the model summarizes what the record shows, it does not diagnose. A 29-test pytest suite covers FHIR connectivity, patient-data loading, and response quality, including a check that every response carries the clinician-verification disclaimer.
governance
Clinical Copilot Adoption Governance: The Framework We Build In
Model Risk, Clinical Validation, and Human-in-the-Loop Controls
The model is instructed by design never to fabricate a medication or diagnosis and to state plainly when the record doesn't have enough data to answer, with an Azure Responsible AI content filter on the model deployment and a mandatory clinician-verification disclaimer enforced by the test suite on every response. This is decision support only. The system has not been clinically validated for production use, and we say so directly rather than implying otherwise.
Audit Trails, Access Controls, and What's Not Yet Done
Today the accelerator uses least-privilege FHIR access via an Azure AD service principal and keeps patient identifiers out of logs, but honestly: the API is not yet authenticated, per-patient audit logging and a data-retention policy are not yet built, and prompt-injection defenses on free-text input are still needed before any real patient data could be loaded. This is exactly the adoption-governance checklist a health system's security review would run, and we'd rather show you our own gap list than pretend it doesn't exist.
differentiation
Implementation Partner vs. Big 4: What 'Implementation' Actually Means Here
A Big 4 clinical AI engagement typically starts with a current-state assessment and a vendor-selection framework. Kriv AI starts from a working, deployable system: real FHIR data flow, a real model deployment, a real test suite, and a real, honest list of what's needed before production. That's the difference between advising on implementation and having already done it once.
engagement
From Accelerator to Production: Our Engagement Path
A scoped engagement closes the gaps disclosed above (authentication, audit logging, data retention, clinical validation) against your real EHR and FHIR environment, carrying the same grounded, disclaimer-enforced architecture through from day one.
checklist
Clinical Copilot Governance Checklist for Health System Buyers
Before any clinical copilot touches real patient data, ask the vendor to show: (1) how every response is grounded in a specific, citable record, not a general model response, (2) whether a clinician-verification disclaimer is enforced by an automated test, not just a UI label, (3) whether the API is authenticated and every access is logged, (4) whether there's a documented data-retention and de-identification policy, and (5) whether the vendor will tell you what's not done yet. If a vendor can't answer all five plainly, that's the answer.
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Straight answers
Frequently asked questions about AI Clinical Copilot Implementation Partner: A Working Accelerator, Not a Deck
Is the clinical copilot accelerator a real, working system?
Yes. It's a deployed system on Azure Health Data Services (FHIR R4) and Azure OpenAI GPT-4o, with 29 automated tests covering FHIR connectivity, data loading, and response quality. It runs on synthetic patient data, not real PHI.
Does the copilot diagnose patients?
No. It is decision support only. The model summarizes and cites what the patient's record shows and is instructed never to fabricate a diagnosis or medication. Every response carries a mandatory clinician-verification disclaimer, enforced by the test suite.
Is this ready to use with real patient data today?
No, and we say so directly. The API is not yet authenticated, per-patient audit logging and a data-retention policy are not yet built, and the system has not been clinically validated. Those are exactly the gaps a scoped engagement closes before production.
What does 'clinical copilot adoption governance' mean in practice?
It means grounding every response in a citable record, enforcing a clinician-verification disclaimer by test rather than just UI copy, authenticating and logging every access, and being honest with buyers about what's built versus what's still needed.
What are the four core capabilities?
Clinical Q&A (a cited, grounded answer to a plain-English question), a Medication Safety check (interaction and contraindication scoring), a Risk Dashboard (patients ranked by risk), and Lab Trends (observations over time with an AI interpretation).
Can we see this running before we engage Kriv AI?
Yes. We can walk through the live accelerator against synthetic patient data in a discovery call.
Ready to see the accelerator run against your data model?
Bring your requirements to a working session and we'll walk through the live system.
Book a Discovery Call