From Admission to Discharge: Leveraging NLP for Upstream Primary Coding with SNOMED CT.

Abstract

This study aims to describe implementing a SNOMED CT-coded health problem (HP) list at Hospital Clínic de Barcelona. The project focuses on enhancing the accuracy and efficiency of clinical coding by automating the process from patient admission, while simultaneously enabling the reuse of coded data for research and management purposes. SNOMED CT was selected as the reference terminology for recording HPs. A subset of terms (our Health Problems Catalogue -HPC-) was created to meet local needs. An NLP tool was integrated into the clinical workstation to assist in primary coding HPs from natural language inputs. The system architecture included four servers (Coder, Reviewer, Manager, and Terminology Server) supporting real-time coding and review processes. Clinical and operational data from April to October 2024 were analyzed to evaluate the system's performance. Between April 9 and October 4, 2024, a total of 118,534 HPs were recorded. Of these, 74.2% were coded in real-time using the NLP tool, 23.3% were coded by documentation specialists, and 2.5% remained uncoded. The system significantly reduced coding delays and enriched the institutional data warehouse, facilitating real-time research and management activities. Implementing a SNOMED CT-coded HP list supported by NLP and terminology services improved coding accuracy and clinician efficiency. This system enhances clinical understanding, enables evidence-based recommendations, and supports data-driven decision-making in healthcare management and research. Clinical Trial Number Not applicable.