Your AI drafts the appeal. A physician should validate it before the payer sees it.
Clinovian is not competing with your AI tool. We are the clinical quality-control layer that makes your output defensible. Independent physician review of AI-drafted appeal arguments — catching hallucinated claims, weak criteria logic, missing comorbidities, and overstatement — before submission.
Clinical claims that don't exist in the record
AI-drafted appeals assert facts — lab values, vital signs, clinical events — that are plausible but not documented. The payer reviewer checks the record and the appeal loses credibility.
Criteria cited without clinical mapping
"Per InterQual criteria, the patient clearly met inpatient threshold" is a conclusion without evidence. Payer reviewers are trained to dismiss this.
Directionally correct, clinically overstated
The AI captures the general argument but amplifies it beyond what the record supports. Under close payer scrutiny, the overstatement becomes a vulnerability.
A pre-submission checkpoint, not a parallel workflow.
Per-Batch QA
Send batches of AI-drafted appeals for physician review. Batch scope, service levels, and volume pricing are confirmed at intake based on batch size and case mix. Best for: testing the QA layer or irregular volume.
Monthly QA Retainer
Fixed monthly allocation for ongoing QA coverage. Priority turnaround. Monthly pattern report on recurring AI failure modes — feeds back into your model improvement cycle. Best for: production-scale AI appeal operations.
"Physician-validated AI appeals" is a market differentiator your competitors don't have. As the AI-RCM market matures, providers and payers will increasingly demand clinical quality assurance on AI-generated content. Building the QA layer now positions your product ahead of that curve.
The monthly pattern report also creates a feedback loop: Clinovian identifies the categories where your AI consistently underperforms, giving your engineering and clinical teams targeted improvement signals.
Send a sample batch. See what physician QA catches before the payer does.
Begin with one de-identified draft and a description of the supporting-record workflow for an initial fit assessment. Paid QA engagements may then cover 5–10 AI-drafted appeals, with annotations returned within the agreed turnaround.