Governed AI for denial management — inside the revenue cycle operating system.
DenialOS applies governed AI across the revenue cycle: denial classification, evidence synthesis, appeal drafting, deadline computation, payer pattern analysis and recovery triage. Every AI output is flagged for human review before it moves anything forward. No black-box decisions, no AI acting alone.
Denial Classification
AI classifies denial types from EOBs and 835s, maps them to payer-specific rules, and routes each case to the right queue.
Evidence Synthesis
AI synthesizes clinical records, EOBs, and denial letters into structured appeal context, flagging missing evidence for review.
Appeal Draft Generation
AI drafts appeal letters from the synthesized evidence, denial codes, and payer rules. Human review required before submission.
Autonomy Controls
Hospital administrators configure what AI can draft, flag, and recommend. Human review gates are adjustable.
Deadline Governance
Canonical filing windows are computed by payer and plan, and the work queue orders itself by deadline risk.
Pattern Detection
AI analyzes observed outcomes by payer, code, facility, and provider to surface recurring denial patterns.
Every AI action is governed. Every decision is human.
DenialOS AI is built for regulated clinical and billing workflows. Drafts are suggestions, not instructions. AI surfaces information; humans make decisions. Every action is logged, auditable, and reversible.
AI outputs are drafts, not actions. Every AI recommendation requires human approval before submission.
Every AI draft, classification, and recommendation is logged with timestamp, user, and case context.
Hospital administrators control what AI can do, flag, and recommend per role and workflow.
AI-synthesized clinical content is reviewed by qualified staff before inclusion in appeals.