Enhancing Clinical Data Capture: Developing a Natural Language Processing Pipeline for Converting Free Text Admission Notes to Structured EHR Data

Abstract

Automating the extraction of essential patient information from clinical texts, such as admission notes, can significantly enhance the entry of this data into Electronic Health Records (EHR), thereby enhancing workflow efficiency and supporting improved patient care and healthcare management. To address this issue, we introduce a Natural Language Processing (NLP) pipeline designed to (i) automatically extract patient data via Named Entity Recognition (NER), (ii) normalize the extracted data to correspond to codes in official medical ontologies, and (iii) coerce the data into EHR format using Health Level 7’s (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. By adhering to these widely used standardized formats, the pipeline output can be immediately integrated into the Hospital Information System (HIS). To achieve this, we propose a newly labeled dataset comprising 255 notes from unlabelled datasets published by the Text Retrieval Conference’s (TREC) Clinical Trials tracks. Finally, we utilize SapBERT for the normalization of extracted entities and employ the FHIR standard as a basis to generate Electronic Health Records (EHRs).