Cascaded Intelligence for High-Speed Medical Document Processing
DOI:
https://doi.org/10.55578/amsr.2607.011Keywords:
Information Extraction, Document AI, Cascaded Intelligence, Revenue Cycle Management, Financial Automation, Hybrid Neural ModelsAbstract
This paper presents a robust, high-throughput architectural framework for automated information extraction from Explanation of Benefits (EOB) documents. In the medical billing industry, the extreme variability of document formats poses a significant challenge for traditional automation. Our research demonstrates that conventional approaches, such as heuristic-based systems and prompt-based Large Language Model (LLM) extraction, fail to achieve the precision required for automated financial posting due to spatial misalignments and neural hallucinations. To address these limitations, we propose a hybrid cascaded pipeline that integrates specialized models for page splitting, layout classification, and field-specific normalization. Furthermore, we implement a multi-layer financial verification engine to ensure arithmetic integrity. The system is deployed in a production environment, processing over 300,000 documents per month, achieving a 95.8% first-pass resolution rate (FPRR) and reducing charge lag by 81%.
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Copyright (c) 2026 Ato Kasymov, Fedor Krasnov (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.