MEDICAL REPORT PARSING AND STANDARDIZATION

Abstract

Efficient secondary use of healthcare data demands reliable extraction and structuring of information from heterogeneous medical reports. This research presents an AI-driven system that automates parsing and standardization of clinical narratives across various formats, integrating optical character recognition and natural language processing methods. The proposed pipeline systematically identifies patient details, diagnoses, laboratory results, and medications, harmonizing extracted elements into a standardized tabular format suitable for analytics and electronic health record integration. Evaluation on real-world reports demonstrates high accuracy and strong interoperability, underscoring the utility of automated frameworks for scalable, consistent medical data management.