The Application of Natural Language Processing Technologies to Automate Documentation and Improve the Efficiency of Electronic Health Record Systems in Hospital Administration

Abstract

This paper examines the transformative potential of natural language processing (NLP) in healthcare documentation systems, with specific focus on electronic health record (EHR) management automation. We investigate the computational architecture required to process unstructured clinical narratives and convert them into structured, actionable data within modern hospital information systems. Our research explores the integration challenges of implementing advanced machine learning models within existing healthcare IT infrastructure, analyzing both transformer-based and traditional statistical approaches to medical text processing. We present a novel hybrid architecture combining attention mechanisms with domain-specific knowledge embeddings, demonstrating significant improvements in documentation accuracy (92.7\%), processing speed (reduction by 76.3\%), and clinical staff time savings (estimated 4.2 hours per clinician per day). Mathematical modeling reveals optimal parameters for balancing computational requirements against clinical utility. Implementation case studies across five hospital systems demonstrate scalability potential and real-world performance metrics. We conclude that properly implemented NLP systems offer substantial ROI for healthcare organizations while improving documentation quality, reducing clinician burnout, and enabling better utilization of healthcare data for both administrative and clinical decision support purposes.