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AI coding with evidence: helping coders work faster without losing control

Why every AI-suggested diagnosis code on BharathiExchange comes with the exact sentence it was drawn from — and why that matters for accuracy and audit.

1 min readBharathiExchange Team

This is an illustrative scenario showing how the platform is designed to work.

Clinical coding turns doctors' notes into standard codes used for records, claims and statistics. It's skilled work — and it is often a bottleneck.

Suggest, don't decide

For each discharge summary, the AI proposes a ranked list of codes. Each suggestion shows:

  • the code and its description
  • a confidence score
  • the highlighted text in the note that supports it

The coder accepts, edits or rejects each suggestion. Nothing reaches a claim or a record without that review.

Why evidence matters

  • Accuracy: coders can see at a glance when a suggestion is based on a negated finding ("no evidence of pneumonia").
  • Audit: reviewers and payers can trace every code back to the source text.
  • Learning: accepted and rejected suggestions feed back into model monitoring.

Explore all our AI solutions.

AIClinical NLPCoding