Developing an Expert System for Healthcare Claims Validation Using Knowledge Representation Techniques

Abstract

Healthcare claims validation is a complex process requiring the alignment of patient information, diagnostic codes, procedure codes, policy rules, and regulatory guidelines. The growing volume of healthcare data, coupled with frequent policy revisions, underscores the need for a robust and adaptive mechanism to ensure consistent and accurate claims validation. Expert systems, underpinned by sophisticated knowledge representation techniques, offer a viable approach for automating this task. By encoding domain knowledge from medical experts, billing specialists, and regulatory documents into logical constructs, these systems can systematically evaluate claim legitimacy. Such an approach not only minimizes human error and administrative overhead, but also promotes transparency by capturing detailed reasoning trails. This paper explores the theoretical underpinnings and practical development of an expert system that employs propositional and first-order logic, structured rule-based frameworks, and semantic networks to validate healthcare claims with high precision. Emphasis is placed on constructing reliable inference mechanisms to handle uncertain or incomplete data, ensuring that claims are cross-checked against the latest medical policies and evolving insurance guidelines. The system’s architecture integrates novel linear algebraic methods for detecting inconsistencies among large sets of claims, enabling the automatic flagging of outliers. Through a comprehensive evaluation on multiple datasets, the proposed expert system demonstrates improved efficiency, enhanced consistency, and measurable reductions in processing time, thereby contributing to streamlined healthcare administration and better resource allocation across the medical sector.