ArticleJournal of medical systems2025
From Admission to Discharge: Leveraging NLP for Upstream Primary Coding with SNOMED CT.
Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Clinical evaluation of MiADE: a natural language processing system for assisting structured diagnosis recording at the point of care.BMJ health & care informatics · 2026Article
- Beware the Little Foxes that Spoil the Vines: Small Inconsistencies in Clinical Data Can Distort Machine Learning Findings.Fortune journal of health sciences · 2025Article
- Telemedicine and the European Health Data Space: a new paradigm for healthcare in the EU.Frontiers in digital health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.