Autonomous medical coding is a hot space, but it has a hard constraint most demos gloss over: in coding, a wrong output can mean False Claims Act liability, and some payers require human attestation on AI-generated claims regardless of model accuracy. So "remove the human" is the wrong design goal. The right one is "make attestation take five seconds." Here's the architecture.
1. Output codes WITH evidence, not just codes
The agent returns each CPT/ICD-10 suggestion bundled with the source spans (note text, clinical context) that justify it. Attestation is only fast if the reviewer doesn't have to go hunting for the "why."
2. Attestation is a required state transition, not an afterthought
A suggested code is not a billable code. It moves to "billable" only through an explicit human-attest action — a real state machine, so nothing reaches a claim un-signed.
3. Confidence routing
High-confidence, low-risk codes get batch-attested; low-confidence or high-risk (e.g. anything that changes reimbursement tier) is flagged for individual review. Throughput without abdicating judgment.
4. Append-only audit trail as the source of truth
Log the suggestion, the evidence, the attesting user, and the timestamp — immutably and exportably. "Why this code?" becomes a query. This is what turns an auditor conversation from archaeology into a lookup.
5. Don't silo it — share the control plane
Coding is one agent among eligibility, prior-auth, claims, and denials. One MCP-style control plane means clean data flows front-to-back and every agent inherits the same governance and audit model.
The reusable idea: in regulated domains, attestation + evidence + an immutable log beats raw autonomy, because the bottleneck was never the model's speed — it was defensibility. We ship this pattern as one of several governed healthcare AI agents in IntelliBooks Studio — more at intellibooks.ai/overview.
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