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

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Implementing a SNOMED CT-coded health problem list automated by NLP tools significantly improved clinical coding accuracy and efficiency. This enhances data reuse for healthcare research and management.

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Area of Science:

  • Medical Informatics
  • Health Data Management
  • Clinical Terminology

Background:

  • Accurate and efficient clinical coding is crucial for healthcare data utilization.
  • Manual coding processes can lead to delays and data inconsistencies.
  • Standardized terminologies like SNOMED CT are essential for interoperability.

Purpose of the Study:

  • To describe the implementation of a SNOMED CT-coded health problem list at Hospital Clínic de Barcelona.
  • To automate health problem coding using Natural Language Processing (NLP) tools.
  • To enhance the accuracy, efficiency, and reusability of clinical data for research and management.

Main Methods:

  • Selected SNOMED CT as the reference terminology.
  • Created a local Health Problems Catalogue (HPC) subset.
  • Integrated an NLP tool into the clinical workstation for automated coding.
  • Utilized a system architecture with dedicated servers for coding and review.

Main Results:

  • Over 118,000 health problems were recorded between April and October 2024.
  • 74.2% of health problems were coded in real-time using the NLP tool.
  • Coding delays were significantly reduced, and the data warehouse was enriched.

Conclusions:

  • Implementing a SNOMED CT-coded health problem list with NLP enhances coding accuracy and clinician efficiency.
  • The system facilitates real-time research and supports data-driven healthcare decision-making.
  • Improved clinical understanding and evidence-based recommendations are key outcomes.