Understanding Medical Claims Processing Systems

US hospitals faced $262 billion in claim denials and underpayments last year, with 65% of denials not resubmitted despite 90% being recoverable with timely follow-up. The rising denial rates over the past decade remain unresolved by merely increasing denial management capacity, as the root issue lies in upstream medical claims processing systems that provide erroneous data to adjudication engines. Standard OCR technology, which processes claims documents by converting pixels to text, fails to accurately interpret the structured, grid-like forms such as CMS-1500 and UB-04, leading to character-level misreads and coding errors that result in denials. These failures are compounded by the inconsistent formats of Explanation of Benefits (EOBs) and prior authorization forms, which standard OCR cannot handle effectively without custom configuration. LlamaParse offers a solution with agentic OCR, which uses layout-aware computer vision to understand document structures before extraction, improving extraction accuracy and reducing manual intervention costs. By fixing data extraction errors at intake, healthcare revenue cycles can improve straight-through processing (STP) rates, ultimately reducing the reliance on denial management teams and enhancing financial performance.