NLP-Based Predictive Analytics Framework for Early Detection of Medical Coding Errors Using Transformer-Based Clinical Text Analysis

Abstract

Accurate medical coding is essential for healthcare reimbursement, regulatory compliance, and accreditation, serving as the foundation for quality clinical documentation. Current post-coding review approaches for error detection are resource-intensive and reactive, failing to alert coders about documentation discrepancies before code assignment occurs. An NLP-based predictive analytics framework has been developed to identify medical coding errors by comparing clinical narratives to assigned diagnosis and procedure codes. The framework employs specialized transformer models to extract clinical details from unstructured documentation and assess alignment between written clinical information and assigned codes. Transformer-based architectures enable semantic understanding of clinical text and generation of code representations for both ICD-10-CM and CPT nomenclature. Structured features—including encounter type, provider specialty, and documentation length—complement neural embeddings for comprehensive encounter characterization. Supervised machine learning classifiers predict coding error risk across diverse medical specialties. Cost-sensitive learning approaches address the inherent class imbalance in medical coding datasets, prioritizing minority class (error) detection. The framework demonstrates substantial performance improvements compared to rule-based validation systems across multiple clinical domains. NLP-based predictive systems for coding error detection offer healthcare organizations the opportunity to shift from retrospective audit models to proactive risk identification, enabling more efficient resource allocation, enhanced compliance outcomes, and improved revenue cycle accuracy.