Natural Language Processing with AI for Unstructured Claims Data

Abstract

Despite decades of investment in electronic claims  processing, a substantial proportion of healthcare insurance claims  still depend on unstructured clinical artifacts such as physician  progress notes, discharge summaries, operative reports, pathology  narratives, and scanned medical records. These free-text sources  often contain the most critical evidence for medical necessity,  diagnosis–procedure alignment, and compliance with coverage  policies, yet they remain poorly exploited by traditional rule-based  adjudication systems that are optimized for structured codes andtabular data. This paper examines the role of artificial intelligence  driven Natural Language Processing (NLP) in transforming  unstructured claims data into actionable signals for automated and  semi-automated adjudication. We analyze how contemporary NLP  models ranging from clinical named entity recognition and medical  concept normalization to transformer-based contextual embeddings  enable the extraction of diagnoses, procedures, temporal events,  and provider intent from heterogeneous clinical narratives.  Particular emphasis is placed on challenges unique to the medical  domain, including terminological ambiguity, negation, context  sensitivity, clinical abbreviations, and cross-document inference.  The study further explores how NLP-derived features can be  reconciled with structured claims standards such as X12 EDI andHL7 FHIR, enabling hybrid adjudication pipelines that combine  narrative intelligence with coded data. By positioning NLP as a  semantic bridge between clinical documentation and claims  infrastructure, this work highlights its potential to reduce manual  review rates, accelerate decision timelines, and improve  adjudication accuracy while preserving regulatory compliance and 
auditability.