Semantic Search in Electronic Health Records: A Case Study Using SemEHR System

Abstract

Electronic Health Records (EHRs) contain a blend of structured fields and unstructured clinical notes that together capture the complete story of a patient’s health. While structured data can be processed easily, the rich medical insights hidden in free-text notes often remain inaccessible to traditional keyword-based search methods. These limitations arise from variations in medical terminology, frequent use of abbreviations, and contextual expressions such as negation and temporality. This paper examines how semantic search, an approach that interprets the meaning and context of medical concepts can enhance information retrieval in healthcare settings. Using SemEHR, an open-source semantic search and analytics system deployed in multiple UK hospitals, as a case study, the work demonstrates how clinical notes can be transformed into ontology-linked concepts, temporally structured patient views, and clinically meaningful summaries. The evaluation of SemEHR across real hospital datasets shows substantial improvement in retrieval accuracy and efficiency compared to traditional search approaches. These findings highlight the potential of semantic search to support clinical research, patient identification, and medical decision-making by making unstructured EHR data more accessible, interpretable, and actionable.