AUTOMATED MEDICAL CODING | Clinical Notes to Submitted Claims
AI coding from clinical notes, covering E/M CPT assignment, insurance verification, and claim submission end to end
BACKGROUND
A multi-location medical practice delivers care across a growing network of providers and sites. Every encounter produces clinical documentation that must be translated into the appropriate E/M level and CPT code, checked against insurance eligibility, and prepared for submission. Certified coders and revenue-cycle staff carry that work, connecting the clinical record to the systems that get the practice paid.
THE OPPORTUNITY
Manual coding queues delayed charge entry, and eligibility and coding errors surfaced as denials weeks after the visit. Faster, more consistent coding would shorten charge lag, capture more of the work actually performed, and let revenue-cycle capacity grow without proportional hiring as the practice added providers and locations.
OUR APPROACH
Ajaia built the pipeline in five phases, with coding accuracy validated against the practice's own encounters before any claim was submitted automatically.
We walked the flow from encounter documentation through coding, eligibility verification, and submission, marking every point where an encounter waited on a person. That produced a baseline of charge lag days, uncoded encounter volume, and denial reasons, which set the targets the build had to beat and made the eventual improvement measurable rather than anecdotal.
The system reads the clinical note and assigns the E/M level and CPT code under AMA guidelines. The logic is documented and applied identically to every encounter, so coding is consistent and defensible regardless of which coder would have picked up the chart. Each assigned code links back to the specific documentation supporting it, which is what makes the output auditable rather than just fast.
E/M assignment carries revenue exposure in one direction and audit exposure in the other, so this phase gated the rollout.
Certified coder benchmarking. System-assigned codes were scored against a reference set coded independently by the practice's certified coders. Exact-match rate, undercoding, and overcoding were tracked separately, because leaving revenue behind and creating audit risk are different failures that require different fixes.
Documentation support testing. Every code had to be supported by specific language in the note. Any assignment the system could not tie to documentation was treated as a defect, not a near miss.
E/M distribution monitoring. We tracked the level distribution across encounters against the practice's historical pattern and specialty benchmarks, so improved capture could be distinguished from level inflation. This is the same analysis a payer runs, which is why we run it first.
Confidence-based routing. Encounters below a defined confidence threshold route to a certified coder instead of submitting automatically. The threshold was set with the practice and tightened only as accuracy held.
Regression testing. A standing suite of reference encounters runs on every logic change, so coding behavior cannot drift silently between releases.
Eligibility is checked as part of the pipeline, before the claim is built. Coverage problems that used to return as denials weeks after the visit now surface while there is still time to correct them, with HIPAA-aligned PHI handling throughout.
Each coded, verified encounter moves straight to submission with no batching step and no queue. The pipeline ran in parallel with the practice's manual process first, coding live encounters alongside human coders without submitting, so accuracy could be proven against real volume at no financial risk. Automated submission then opened by visit type and location, expanding as the numbers held rather than all at once.
THE SOLUTION
An RCM engine that turns each clinical note into a coded, verified, submitted claim, with certified coders reviewing exceptions instead of leveling routine visits.
RESULTS
Charge capture rose 25% because encounters no longer age out or slip through uncoded. Revenue rose 13% as that captured work converted to payment on the payer's clock, supported by cleaner claims that go out verified the first time rather than returning for rework. The gain came from coding work that was already performed and documented, not from shifting encounters to higher levels.
Routine claim submission is now fully automated. Nothing waits for a batch run and nothing sits in a queue, while low-confidence encounters route to a certified coder by design rather than by exception.
The role of the coding team changed rather than shrank. Certified coders now audit output and resolve the encounters that need judgment, which is where their certification and clinical knowledge actually pay off, instead of leveling routine visits one chart at a time.
For a growing group, that decouples patient volume from billing headcount. New providers add encounters to an automated pipeline, and the pipeline absorbs them without proportional hiring.
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