Payer Rules Engine

Translates narrative medical policies into computable if-then-else logic. This eliminates ambiguity by hard-coding coverage criteria—such as frequency limits, age restrictions, and diagnosis-to-procedure pairings—into executable rules. Unlike probabilistic AI, a rules engine provides a binary, auditable outcome for every condition, ensuring 100% consistency with the payer's formal coverage determination guidelines.

Ingests and harmonizes disparate clinical guidelines from sources like MCG Health, InterQual, and custom payer policies. The engine maps proprietary criteria codes and narrative text to a unified internal data model. This allows a single authorization request to be simultaneously evaluated against multiple overlapping policies, instantly identifying the most specific applicable rule set for the patient's clinical context.

Manages the lifecycle of medical policies with strict version control. When a payer updates a coverage guideline—such as changing the required conservative therapy period from 6 weeks to 12 weeks—the engine activates the new rule version at a specific timestamp. This ensures adjudication is always based on the policy effective at the date of service, maintaining a complete audit trail for compliance.

Accepts structured and unstructured inputs to fire rules. The engine consumes FHIR bundles, X12 278 transactions, and structured clinical data extracted via NLP. It can evaluate a rule requiring a specific HbA1c value from a lab result, a CPT code from the claim, and a medication frequency from a structured drug list simultaneously, correlating data across multiple sources in a single decision cycle.

Handles scenarios where multiple rules apply or contradict. The engine uses a priority hierarchy—plan-specific rules override general medical policy, and state mandates override plan rules. It also manages temporary overrides, such as a medical director's exception for a non-formulary drug, logging the rationale and automatically expiring the override after a defined duration.

Records a complete, immutable trace of every rule evaluated during an authorization. For each decision, the engine logs the rule ID, version, input data elements, and boolean outcome. This granular logging supports regulatory audits, provider dispute resolution, and internal analytics, providing a transparent 'show your work' trail that explains exactly why a request was approved or denied.

The NLP-driven process of ingesting unstructured payer policy documents (PDFs, bulletins) and converting them into machine-readable clinical logic. This is the critical upstream feed for a rules engine.

• Parses complex inclusion/exclusion criteria
• Structures frequency limits and age restrictions
• Maps clinical terminology to SNOMED CT and ICD-10-CM codes
• Enables rapid policy updates without manual coding

A complementary system that verifies the accuracy and completeness of AI-extracted clinical data before it enters the adjudication pipeline. It applies both deterministic and probabilistic logic.

• Validates laterality and anatomical consistency
• Checks for temporal contradictions in dates
• Confirms dose-to-diagnosis alignment for medications
• Flags missing required data elements for the payer rules engine

The deterministic execution layer that applies predefined payer-specific rules to automatically approve, deny, or pend an authorization request. It consumes structured clinical data and policy logic.

• Executes if-then-else clinical algorithms
• Applies administrative checks (eligibility, network status)
• Generates determination letters with specific rationale
• Routes complex cases to human clinical review

The automated evaluation confirming a proposed service aligns with evidence-based guidelines and payer-specific criteria. The rules engine operationalizes this by matching patient data against policy.

• Validates diagnosis-to-procedure pairing
• Checks conservative therapy prerequisites
• Applies frequency and duration limits
• Ensures the service is not experimental or investigational

The coordination layer that manages the end-to-end lifecycle, routing requests based on the rules engine's output. It balances automated determinations with human-in-the-loop review.

• Routes auto-approved cases directly to notification
• Queues pended cases for clinical reviewer audit
• Prioritizes by urgency and revenue impact
• Tracks service level agreement compliance for turnaround times

A machine learning model that assigns a probability score to a pending request, predicting the likelihood of approval. It augments the deterministic rules engine with statistical insight.

• Trained on historical adjudication data
• Identifies patterns the rules engine may miss
• Flags high-risk denials for pre-submission intervention
• Feeds confidence scores into workflow prioritization