AI-Driven Intelligent Document Processing for Insurance and Prior Authorization: OCR-Based Data Extraction, Enterprise Validation, and Automated Downstream Processing
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P151Keywords:
Intelligent Document Processing, Optical Character Recognition, Document AI, Prior Authorization, Insurance Automation, Enterprise Validation, FHIR, Human-In-The-Loop, Workflow Orchestration, Healthcare InteroperabilityAbstract
Insurance and healthcare prior-authorization operations continue to receive clinically and administratively relevant information through heterogeneous documents, including scanned forms, physician notes, referral packets, diagnostic attachments, and payer correspondence. Optical character recognition (OCR) can convert pixels into text, but reliable automation requires a broader mechanism that determines document type, extracts semantically typed entities, normalizes values, verifies them against enterprise records and domain constraints, and controls downstream actions under uncertainty. This paper presents a reference architecture for AI-driven intelligent document processing (IDP) that separates perceptual confidence from operational validity. The proposed pipeline combines document classification, OCR or visual-document understanding, entity extraction, terminology normalization, multilayer validation, risk-aware routing, and idempotent downstream orchestration. A formal decision model is defined in which each extracted entity is evaluated by recognition confidence, field criticality, syntactic validity, cross-field consistency, and enterprise-reference agreement before it can trigger an automated action. Because no proprietary insurance corpus or production measurements were supplied for this study, the paper deliberately avoids invented performance results. Instead, the framework is evaluated through requirements traceability to FHIR-based prior-authorization standards, failure-mode analysis, scenario walkthroughs, and a reproducible empirical protocol specifying OCR, extraction, validation, automation, latency, and safety metrics. The resulting design provides a technically testable path from document recognition to controlled enterprise automation while preserving human review for ambiguous or high-risk cases.
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