Knowledge Representation for Integrating Clinical and Administrative Data in Claims Processing

Abstract

This paper addresses the increasingly complex challenge of integrating clinical and administrative data in health insurance claims processing through advanced knowledge representation techniques. Driven by rising healthcare costs and the growing prevalence of electronic health records, there is an urgent need to establish robust, scalable, and semantically aware frameworks for linking heterogeneous data sources. By introducing formalisms that combine logical inference, ontological modeling, and various algebraic methods for handling large-scale datasets, this work seeks to elucidate key mechanisms for bridging the semantic gap between clinical and administrative terminologies. In doing so, it explores the intricacies of reconciling high-level abstractions, such as diagnoses and procedure codes, with granular data related to patient care, clinical observations, and associated costs. The discussion elaborates on representations that effectively capture domain constraints, contextual relationships among data elements, and cross-system references to internationally recognized coding standards. Moreover, formal logic statements and advanced linear algebraic approaches are introduced to illustrate how data alignment can be implemented at scale. This paper also examines potential implementation barriers, such as privacy concerns, legacy system interoperability, and organizational resistance to semantic integration. Ultimately, by proposing novel frameworks and theoretical underpinnings, this work illustrates the possibilities of leveraging knowledge representation to enable seamless, consistent, and efficient analysis of diverse healthcare data sources. The outcome is a more structured, logically consistent environment for accurate claims processing and deeper clinical insights, paving the way for improvements in both patient outcomes and cost management.