Integrating Natural Language Processing into Electronic Health Records for Automated Clinical Decision Support

Abstract

This study explores the integration of Natural Language Processing (NLP) into Electronic Health Records (EHRs) to enhance Clinical Decision Support Systems (CDSS). Despite the vast amount of data in EHRs, much of the unstructured data, such as clinical notes, remains underutilized. NLP can extract meaningful information from this unstructured text, bridge the gap between data availability and actionable clinical knowledge. We developed an NLP pipeline using the MIMIC-III dataset, to achieve high performance in entity recognition and relation extraction. The integrated CDSS demonstrated improved accuracy in key clinical tasks, such as early sepsis detection and in-hospital mortality prediction. The system maintained real-time responsiveness, which generated patient-specific recommendations within 3.5 seconds. This study highlights the potential of NLP to transform CDSS by leveraging unstructured clinical data, ultimately improving patient outcomes and healthcare efficiency.

Keywords: Natural Language Processing, Electronic Health Records, Clinical Decision Support Systems, Healthcare Informatics