Problem
Hospital coding and CDI teams spend too much time manually reviewing encounters for missing specificity, weak documentation, and denial risk. Most AI demos over-automate instead of fitting into the actual reviewer workflow.
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Open Demo
2026 prototype
Revenue Cycle Copilot
AKASA-style healthcare operations demo for coding review, CDI clarification, and denial-risk intervention inside a revenue cycle work queue.
- Next.js
- TypeScript
- Zustand
- Revenue Cycle UX
- Healthcare AI
- Coding + CDI
Solution
Built a dense internal copilot with an encounter queue, chart review workspace, transparent AI rationale, editable physician queries, and measurable before/after impact so a reviewer can stay in control while moving faster.
Impact
Shows how AI can fit into revenue cycle work as an auditable assistant: explaining why it suggested a code change, surfacing payer-rule risk, and letting the human accept, reject, or edit each recommendation.
Revenue Cycle Copilot is a product demo for a healthcare operations AI workflow, not a marketing site.
The point is to show good judgment in the shape of the work:
- queue triage
- documentation review
- coding and CDI intervention
- denial-risk visibility
- measurable operational lift
What the demo gets right
- the AI suggestions are visible and reviewable
- the rationale is tied to note evidence and payer rules
- the human reviewer stays in control
- the impact is expressed in time, accuracy, and denial risk rather than vague “efficiency”
Product point of view
The best healthcare AI products usually do not feel magical.
They feel legible.
That means:
- showing what the model saw
- tying recommendations back to real documentation
- giving the operator a clean accept / reject / edit flow
- and making the ROI concrete enough that an operations leader can believe it
Demo Mirror
Live Preview
Mini preview of the actual demo. Use the launch button for full-screen interaction.
