Authorization Decision Support

The engine ingests unstructured clinical data from multiple sources—physician notes, lab results, and imaging reports—and uses clinical narrative summarization to create a unified, chronologically coherent patient history. This eliminates the manual task of hunting through disparate EHR screens.

• Entity Extraction: Identifies key clinical concepts like diagnoses, procedures, and medications.
• Temporal Normalization: Orders clinical events on a timeline for disease progression analysis.
• Source Reconciliation: Resolves conflicting information from different documents.

The system performs a direct comparison between the synthesized clinical evidence and the specific criteria defined in the payer's medical policy. Using Medical Policy NLP, it parses complex coverage logic from PDFs and bulletins into machine-readable rules.

• Criteria Decomposition: Breaks down policy statements into discrete, testable logical conditions.
• Concept Normalization: Maps extracted terms to standard ontologies like SNOMED CT and RxNorm for accurate matching.
• Gap Identification: Flags missing documentation required for policy compliance before the reviewer sees the case.

A predictive authorization scoring model assigns a real-time probability score to each pending request, forecasting the likelihood of approval, denial, or escalation to a peer-to-peer review. This score is derived from historical payer behavior and the clinical context of the current case.

• Denial Risk Stratification: Categorizes cases as low, medium, or high risk of denial.
• Queue Optimization: Routes high-probability approvals for fast-tracking and flags high-risk cases for senior review.
• Outcome Drivers: Highlights the specific clinical factors most influencing the predicted score.

The system presents a human-in-the-loop review interface that surfaces the AI's recommendation, the supporting evidence, and the relevant policy citations in a single, unified view. This is designed to minimize cognitive load and accelerate adjudication.

• Confidence Thresholding: Automatically approves high-confidence cases and routes only ambiguous ones for manual review.
• Evidence Highlighting: Visually anchors the specific sentences in the source documents that support the recommendation.
• Rationale Generation: Provides a draft determination letter explaining the clinical reasoning for the reviewer to validate.

The decision support engine is embedded within a broader authorization workflow orchestration layer that coordinates automated and human tasks. It dynamically routes requests based on AI confidence scores, reviewer specialization, and service-level agreement deadlines.

• Intelligent Triage: Sorts incoming requests by urgency and revenue impact.
• Automated Status Tracking: Provides real-time visibility into the lifecycle of every request.
• Payer Response Parsing: Automatically extracts the final determination and rationale from unstructured payer response letters or faxes to close the feedback loop.

The foundational process of using natural language processing to identify and pull relevant clinical data points from unstructured medical records. This step is critical for feeding the decision support system with the raw patient context needed for accurate review.

• Parses narrative physician notes, radiology reports, and lab results
• Identifies key concepts like diagnoses, medications, and prior treatments
• Transforms free-text into structured data for downstream policy matching

An NLP technique that compares extracted patient-specific clinical data against a payer's formal medical policy documents. This engine determines if coverage criteria are met, providing the core logic for the recommended determination.

• Parses complex policy bulletins and PDFs into machine-readable rules
• Maps clinical concepts to SNOMED CT and ICD-10-CM codes
• Flags specific criteria that are satisfied, unmet, or require further documentation