Medical Policy NLP

Automated parsing of diverse policy formats including PDFs, HTML, and scanned faxes into machine-readable text. This capability uses intelligent document processing to handle multi-column layouts, tables, and embedded clinical criteria that traditional OCR fails to capture. The system normalizes document structure while preserving the hierarchical relationships between coverage rules, exceptions, and evidentiary requirements.

Identification and structuring of medical necessity criteria from narrative policy language. The NLP engine extracts discrete clinical conditions including:

• Diagnosis codes (ICD-10-CM) and their relationships
• Procedure requirements (CPT/HCPCS) with modifiers
• Frequency and duration limits for services
• Prior therapy requirements and step therapy protocols This transforms unstructured policy text into computable, queryable rules for automated authorization engines.

Mapping extracted clinical terms to standardized ontologies including SNOMED CT, RxNorm, LOINC, and ICD-10-CM. The system resolves synonymous expressions—for example, normalizing 'high blood pressure,' 'HTN,' and 'elevated BP' to a single SNOMED concept. This semantic normalization enables consistent, cross-policy comparison and accurate matching against patient-specific clinical data extracted from EHRs.

Reconstruction of complex boolean logic and conditional dependencies embedded in policy documents. The NLP engine identifies logical operators (AND, OR, NOT), temporal constraints, and nested conditional statements that define coverage eligibility. Output is a structured decision tree or rules engine format that can be directly consumed by automated authorization systems, preserving the exact clinical intent of the original policy language.

Automated diff analysis between successive versions of a payer's medical policy. The system identifies:

• Added, removed, or modified clinical criteria
• Changed frequency limits or documentation requirements
• Updated code sets and referenced guidelines This capability enables payers and providers to immediately assess the operational impact of policy updates on pending and future authorization requests.

Extraction of specific documentation requirements that must accompany an authorization request. The NLP identifies required clinical evidence types such as:

• Imaging reports (MRI, CT, X-ray)
• Laboratory values with threshold ranges
• Clinical notes documenting specific findings
• Prior treatment history and outcomes This structured output feeds directly into automated attachment generation and clinical evidence extraction pipelines.