Use Case 1
Quarterly Retrospective Sweep
Trigger: End of quarter closed claims reconciliation
1.Pull closed encounters for the target period across attributed population
2.Run NLP against full chart corpus including notes, labs, and imaging
3.Identify documented conditions missing from submitted claims
4.Score each finding: RAF weight × payment rate × denial-adjusted probability
5.Pre-validate against historical denial patterns and payer feedback
6.Batch findings by priority for certified coder review
7.Prepare clean submission packages with evidence attached
✓
Quarterly RAF recovery batches submitted with pre-validated evidence and significantly lower denial rates.
Use Case 2
Denial Pattern Learning Loop
Trigger: Payer returns claim denials
1.Ingest denial reasons and adjudication notes
2.Classify denial root causes (documentation, specificity, MEAT, eligibility)
3.Update scoring model to downweight high-denial code patterns
4.Flag existing pipeline items affected by new denial patterns
5.Feed insights forward to Prospective HCC agent
✓
Retrospective submissions get progressively cleaner as the denial model learns, and prospective documentation improves upstream.
Integrations
EHR / Chart Repository (Epic, Cerner)
Clinical NLP Engine
CMS HCC Crosswalk (V24 / V28)
Claims History & Denial Log
Coder Review Platform
Claims Clearinghouse
Industries
healthcare
See It In Action
A sample conversation with this agent
Run a retrospective audit on Q4 closed charts.
Pulling Q4 closed charts for all attributed MA members—roughly 3,200 encounters. I'll run NLP against the full chart corpus, identify documented conditions that never made it to submitted claims, score each finding by RAF weight and denial risk, and assemble prioritized batches for coder review. Highest-impact findings go first. You'll see a summary ranking findings by dollar value, confidence, and denial probability within the hour.
How do you decide what gets flagged first?
I rank by expected recovery value, which is RAF weight × plan payment rate × denial-adjusted probability. A high-confidence finding (0.95) with a 0.24 RAF weight and 10% denial probability ranks higher than a lower-confidence finding with a larger nominal weight. I also pre-validate against our historical denial patterns—if a code has a 40% denial rate in our payer mix, I flag it for stronger evidence gathering before batching.
We got pushback from the payer on three CKD Stage 3 codes last submission. Did those show up in this audit?
Yes, two of them are in this batch. I've flagged both with a 'prior denial pattern' tag and pulled the supporting evidence forward—eGFR trends, urine studies, nephrology notes. The third was correctly adjudicated on appeal and isn't in the retro set. For the two flagged cases, I recommend coder review prioritize strengthening the MEAT narrative before resubmission.