Intelligent Document Processing

Ingests and normalizes diverse clinical formats including faxes, scanned images, PDFs, and digital C-CDA documents into a unified processing pipeline. This capability combines Optical Character Recognition (OCR) for rasterized text with direct text extraction for born-digital files. The system handles common real-world degradation like low-resolution scans, skewed pages, and handwritten annotations, ensuring that data trapped in legacy formats becomes accessible for automated prior authorization workflows.

Deploys healthcare-specific NLP models to identify and classify key clinical concepts from unstructured narrative text. This goes beyond simple keyword matching to recognize entities such as:

• Diagnoses (e.g., Type 2 Diabetes Mellitus)
• Procedures (e.g., MRI Lumbar Spine)
• Medications (e.g., Metformin 500mg BID)
• Lab Values (e.g., HbA1c of 7.2%) The system understands clinical context, distinguishing between a current medication and a historical one mentioned in a family history section.

Constructs a semantic graph linking extracted entities based on their clinical relationships within a document. This capability resolves negation (patient denies chest pain), uncertainty (possible pneumonia), and temporality (history of hypertension vs. current episode). It accurately associates a specific medication with its dosage, route, and frequency, and links a diagnosis to the physician who documented it. This structured relational data is critical for automated medical necessity validation against payer policies.

Transforms extracted free-text clinical terms into standardized, computable codes from authoritative ontologies. This process maps physician shorthand and synonyms to precise identifiers:

• SNOMED CT for clinical findings and procedures
• ICD-10-CM for diagnoses
• RxNorm for medications
• LOINC for lab tests Normalization is the bridge that allows an AI-extracted concept like 'high blood sugar' to be matched deterministically against a payer's policy rule written for 'E11.65'.

Automatically categorizes incoming clinical documents into specific types—such as operative reports, progress notes, discharge summaries, and lab results—using a combination of layout analysis and NLP. This intelligent triage ensures that a radiology report is routed to the imaging evidence extraction pipeline, while a medication list is sent to the drug reconciliation module. Accurate classification is the critical first step that enables targeted, high-precision data extraction downstream.

Serializes the extracted, normalized, and validated clinical data into interoperable formats ready for downstream system consumption. Primary output is FHIR R4 resources (e.g., Condition, MedicationRequest, Observation), enabling seamless integration with EHRs and payer APIs. The system also supports legacy formats like HL7 v2 and structured JSON. This capability closes the loop, transforming unstructured document chaos into clean, actionable data for prior authorization automation and clinical analytics.