A synthetic-data clinical validation assistant that helps a nurse reviewer work through DRG documentation cases. I built this from nearly 13 years as an ICU registered nurse combined with hands-on AI-enabled workflow systems experience. Runs as a protected demo behind Cloudflare Access — no real PHI, reviewer-in-the-loop, no automated overrides.
DRG validation review is documentation-heavy, exception-heavy, and easy to get wrong under time pressure. Reviewers spend a large share of their time reading unstructured clinical narrative, cross-checking documentation against criteria, and manually tracking what has and has not been checked on a given case.
Tooling in this space tends to either (a) drop AI suggestions into a freeform interface with no workflow, or (b) automate hard decisions that should belong to a qualified reviewer. Both modes erode trust and create rework. The opportunity is the middle: structure the workflow, capture evidence as the reviewer moves through it, and keep the human as final authority.
A synthetic-data clinical validation assistant designed around the way a nurse reviewer actually works the case: surface the relevant documentation, identify gaps against criteria, flag low-confidence inferences for human attention, and route the final disposition through a reviewer-controlled gate.
Every AI-assisted suggestion is treated as draft support content. The reviewer accepts, edits, or rejects it. The reviewer’s judgment is what advances state. The system never auto-applies a finding and never closes a case without a reviewer action.
The demo runs at drg-demo.twosevens.ai and is gated by Cloudflare Access. Visiting the URL directly will not grant access; reviewers must request approval first. Use the gateway below — that is where access requests are accepted.
The boundaries below are not aspirational — they are how the demo is actually run.
No real patient records, no protected health information, no de-identified production data. Cases are constructed for demonstration.
AI output is draft support content. A qualified nurse reviewer is the final authority on every disposition.
This is a demonstration surface. It is not a HIPAA-cleared production system and makes no PHI-readiness claim.
Exact-email allow-list managed by hand. External reviewers are added only after a request is submitted and reviewed.
The shape, not the secrets. No internal hostnames, no network-level identifiers, no Cloudflare Access policy IDs.
Next.js application that renders the reviewer surface and serves the validation workflow. Containerised for reproducible build and deploy.
Postgres holds synthetic case content, validation state, and reviewer activity. Schema is purpose-built for demo cases — no PHI columns, no real patient identifiers.
Cloudflare Access fronts the demo with an exact-email allow-list and a tunnel route. Approval is manual; nobody reaches the application surface without prior review.
Extraction → normalization → validation → human review. Each transition is explicit. The nurse reviewer is the final authority before any output is treated as decision-ready.
Lint, type-check, build, and audit gates run on every change. Releases are reviewed; nothing ships ad-hoc.
External reviewers only get access after an explicit approval. There is no public path to the live demo.
External reviewers receive access only after a request is submitted through the gateway and the email is added to the allow-list by hand.
Requests are typically reviewed within 24 hours. There is no self-service signup and no automated provisioning to the live demo.
Approved reviewers see the synthetic-data demo only. Operator surfaces, configuration, and any internal tooling stay out of scope.
Reviewers, healthcare AI evaluators, and hiring teams: submit a request through the gateway. Access is granted by hand after a manual review of the request.
Request protected demo accessBackground — Why I can build this responsibly
I built this work from nearly 13 years as an ICU registered nurse, including about 11 years in travel nursing across multiple hospital systems. That background gives me a practical view of how clinical documentation, handoffs, staffing pressure, fragmented workflows, and operational risk actually show up on the floor.
I combine that clinical experience with hands-on work in AI-enabled workflow systems, distributed infrastructure, automation, and production-style software design. My focus is not replacing clinical judgment. It is building tools that reduce friction, preserve accountability, and keep humans in control of high-consequence decisions.
The same workflow discipline applied to adjacent problems — extraction-to-structured-output automation, workflow gating with evidence capture, and reliability practice on distributed operations.
Inconsistent source inputs become validated, structured outputs through extraction → normalization → validation → human review.
What the buyer gets
Workflow-heavy operations get staged state, required-action gates, evidence capture, and controlled closeout.
What the buyer gets
A distributed environment operated as production: service separation, recoverability, queue-backed execution.
What the buyer gets
What I bring to employer teams working on clinical AI, workflow redesign, and infrastructure-heavy automation.
I take on workflow audits, clinical AI implementation, and technical solutions engineering through scoped work with concrete artifacts.