Information Extraction from Provider Notes for Streamlined Medical Billing Using Weak Supervision

Abstract

Information extraction from provider notes has emerged as an instrumental approach to optimizing medical billing processes by capturing relevant clinical and administrative details to reduce claim denials and billing inefficiencies. The presence of domain-specific language, abbreviations, and frequent variations in provider documentation calls for specialized strategies that can automatically identify, categorize, and validate key clinical entities. Traditional methods in supervised learning rely on extensive manually annotated datasets to capture complex linguistic nuances, resulting in time-consuming and resource-intensive data preparation stages. By contrast, weak supervision offers the potential to harness automatically generated labeling functions, expert knowledge bases, and rule-based heuristics to train models in a more cost-effective manner. This paper discusses a framework that integrates weak supervision with advanced natural language processing techniques, aiming to adaptively handle unstructured and semi-structured medical text for robust entity recognition and accurate downstream billing code assignment. The approach involves logic-based constraints for label reconciliation and probabilistic inference to account for label noise. Through this strategy, refined entity resolution is achieved, thus streamlining the billing pipeline by enabling automatic validation of provider notes and real-time alerts for coding inconsistencies. Empirical results indicate that combining weak supervision with context-sensitive embeddings can significantly reduce the burden on human annotators while preserving high levels of precision and recall in capturing relevant clinical descriptors. The ensuing discussion delves into the mathematical formulation, linguistic representation, and real-world impact of these methodologies.