Automated Clinical Review

The foundational capability to ingest unstructured medical records—such as physician notes, lab reports, and scanned PDFs—and identify relevant clinical data points. This process uses medical named entity recognition to pinpoint diagnoses, procedures, and medications, transforming narrative text into structured, computable data. It eliminates manual chart review, reducing the time to locate supporting evidence from hours to seconds.

An NLP engine that compares the extracted, structured clinical data against a payer's formal medical policy documents. It uses clinical concept normalization to map terms to standard ontologies like SNOMED CT and RxNorm, then applies a rule-based authorization engine to verify if coverage criteria are met. This ensures a consistent, objective evaluation against the latest policy version, flagging only exceptions for human review.

An automated process that proactively identifies missing or insufficient documentation before a request is submitted. The system compares the clinical evidence provided against the specific requirements of a payer's policy to pinpoint gaps. This feature shifts the workflow from a reactive denial to a proactive correction, enabling providers to assemble a complete and defensible request the first time, dramatically reducing administrative rework.

A machine learning model that assigns a probability score to a pending authorization request, predicting the likelihood of approval, denial, or the need for a peer-to-peer review. By analyzing historical claims data, clinical context, and payer behavior, the system enables authorization queue prioritization. High-probability approvals can be fast-tracked, while high-risk denials are escalated for immediate intervention, optimizing revenue cycle outcomes.

A purpose-built user experience for clinical reviewers to efficiently audit and correct AI outputs. The interface presents a synthesized summary of evidence, the relevant policy criteria, and the system's recommended determination. It is driven by model confidence thresholding, routing only low-confidence or complex exceptions to a human. This design ensures clinical oversight is focused where it adds the most value, maximizing reviewer throughput.

The coordination layer that manages the end-to-end lifecycle of a request. It routes tasks based on AI confidence scores, queue priorities, and staff availability. The system handles automated approvals, pends requests requiring more information, and seamlessly escalates complex cases for manual review. This orchestration ensures a deterministic, auditable process from submission to final payer adjudication, integrating with payer portal automation for status updates.

The algorithmic core of automated clinical review. This process evaluates a proposed service against payer-defined clinical criteria to confirm it is appropriate for the patient's condition. The engine cross-references structured clinical data with policy rules to generate a binary or scored outcome.

• Compares patient-specific data to evidence-based guidelines
• Outputs an approve, deny, or pend-for-review recommendation
• Reduces the need for manual clinical judgment on routine cases

An NLP technique that compares extracted patient data against a payer's formal medical policy documents. This step determines if coverage criteria are met by aligning clinical facts with policy logic, often using semantic similarity and ontology mapping.

• Parses complex policy language into machine-readable rules
• Identifies gaps between submitted evidence and policy requirements
• Enables consistent, defensible decision-making at scale

An AI-powered system that provides clinical reviewers with a synthesized summary of relevant evidence, policy criteria, and a recommended determination. This accelerates manual review for complex cases that fall outside automatic approval parameters.

• Presents a unified view of patient history and policy logic
• Highlights specific criteria that are met or unmet
• Serves as the bridge between full automation and human oversight

The process of mapping extracted clinical terms to a standard terminology like SNOMED CT or RxNorm. This enables consistent, computable matching against payer policies by ensuring that 'high blood pressure' and 'essential hypertension' are recognized as the same concept.

• Eliminates semantic ambiguity in clinical language
• Enables reliable rule execution across diverse documentation styles
• Critical for accurate policy-to-patient matching

The coordination layer that routes authorization requests based on AI confidence scores, queue priorities, and staff availability. This system decides which cases are auto-adjudicated, which go to human review, and in what order.

• Dynamically assigns tasks to balance workload and urgency
• Tracks the full lifecycle from submission to final determination
• Integrates automated and human decision points seamlessly