Inspiration

Medical billing is one of the most error-prone and manual processes in healthcare. Mistakes like upcoding and unbundling can lead to claim denials, compliance issues, and significant administrative overhead.

The idea was to explore whether AI agents could automate the entire billing workflow — from reading medical documents to validating billing rules and submitting claims — with minimal human involvement.

What it does

Zero-Interface Medical Biller is an autonomous medical billing system powered by Amazon Nova models. It reads scanned prescriptions, extracts billing information, validates the claim against NCCI compliance rules, detects violations such as upcoding or unbundling, corrects the billing codes, and automatically submits the claim to an insurance portal. If the system encounters complex or low-confidence cases, they are routed to a human review queue to ensure safety and accuracy.

How we built it

The system is built as an end-to-end AI pipeline using the Amazon Nova model family. We leveraged Kiro to build it and went through several stages of requirement and design iterations.

The pipeline is orchestrated in Python, with rule validation, structured claim generation, and an escalation queue implemented for human review when necessary.

Challenges we ran into

One of the biggest challenges was reliably extracting structured billing data from scanned prescriptions, since medical documents can vary widely in format and quality.

Accomplishments that we're proud of

We successfully built a fully autonomous billing pipeline that goes from scanned medical documents to submitted insurance claims without human interaction.

What we learned

This project showed how combining multimodal AI, rule reasoning, and agentic automation can solve complex real-world workflows.

What's next for Zero-Interface Medical Biller

The long-term vision is to build AI agents that can autonomously manage complex healthcare administrative workflows while keeping humans in the loop for critical decisions.

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