Leveraging Natural Language Processing for Automated Extraction of Patient Information from Unstructured Clinical Notes in Electronic Health Records

Abstract

Unstructured clinical notes embedded within Electronic Health Records (EHRs) hold critical insights for patient care and decision-making. However, the narrative nature of these notes limits the ease of data retrieval, integration, and real-time analytics. In this paper, we explore the application of Natural Language Processing (NLP) techniques for the automated extraction of structured patient information from unstructured clinical notes. Framed in the 2022 context, where EHR adoption and AI tools have matured, this paper evaluates NLP pipelines involving Named Entity Recognition (NER), clinical ontologies (UMLS, SNOMED CT), and machine learning models.We present both a conceptual architecture and a proof-of-concept system trained on MIMIC-III datasets. The NLP pipeline uses hybrid rule-based and deep learning components to extract diagnoses, medication events, and temporal relationships. Evaluations show a notable improvement in precision and recall compared to previous heuristic systems. This automation holds promise in clinical decision support, population health research, and administrative documentation