Secure document intake
Approved scanning, OCR, malware checks, identity, access, encryption, retention, and minimum-necessary data handling.
AIGAUR LABS / HEALTHCARE PROTOTYPE
Review documents. Prepare coding suggestions.
A working interface concept for de-identified intake, evidence extraction, code suggestions, and qualified human approval. The public prototype uses synthetic text and never sends it to a server.
Discuss a healthcare workflow
WORKING PROTOTYPE
See the review loop, not a black-box answer.
Yes, a production system can scan a paper or uploaded document and prepare code suggestions. It needs secure OCR, structured extraction, current licensed code sets, evidence links, payer and setting rules, qualified review, and a complete audit record.
This public prototype demonstrates the interface and review logic with synthetic examples. It is not connected to an EHR, clearinghouse, payer, encoder, or protected health information environment.
01 / SOURCE
Prepare the record
Use a local sample file
.txt is read in your browser. Image and PDF OCR is shown as a production integration boundary.
02 / HUMAN REVIEW
Evidence-backed suggestions
Ready for a synthetic review
Choose a sample or enter de-identified text, then prepare the coding review.
Review record0 marked reviewed0 rejected
Production systems should store the source, extracted evidence, code-set version, model and rule versions, reviewer, changes, and final authorized action.
PRODUCTION ARCHITECTURE
What the real system would add.
Current coding knowledge
Licensed and official code sets, version dates, payer and facility rules, and evidence attached to every suggestion.
Qualified review
Coder queues, edit and reject paths, escalation, final authorization, and measured agreement with human decisions.
Traceable integration
Controlled write-back to approved systems with source, version, reviewer, timestamp, and downstream response.
Questions worth asking.
Does this prototype process real patient information?
No. It is designed for synthetic or properly de-identified demonstration text. The public page does not send the entered text to a server.
Can an AI system guarantee the exact billing code?
No. Complete documentation, code-set versions, payer rules, care setting, sequencing, modifiers, medical necessity, and qualified review can change the final result.
Can AI Gaur build a private version?
Yes. A private pilot can be designed around the organization’s approved infrastructure, licensed sources, security review, coder workflow, and measurable acceptance criteria.
Does the prototype include CPT coding?
No. This public example is limited to a small synthetic ICD-10-CM demonstration. A production design must address licensing and the organization’s approved coding resources.
OFFICIAL REFERENCES