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Related Experiment Videos

LUCID: a semi-automated ICD-9 encoding system

P A Michel1, C Lovis, R Baud

  • 1Geneva University Hospital, Switzerland.

Medinfo. MEDINFO
|January 1, 1995
PubMed
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Natural language processing for diagnosis coding is emerging. A new system, LUCID, simplifies International Classification of Diseases (ICD) coding, improving efficiency and accuracy for healthcare professionals.

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Health Information Management

Background:

  • Traditional diagnosis coding relies on manual lookup of numerical or keyword-based codes, which is time-consuming and prone to errors.
  • The increasing volume of clinical data necessitates more efficient and accurate methods for diagnosis encoding.
  • The adoption of natural language processing (NLP) in healthcare is poised to transform administrative tasks.

Purpose of the Study:

  • To introduce and demonstrate LUCID, a novel natural language-based system for International Classification of Diseases (ICD) diagnosis encoding.
  • To showcase how NLP can alleviate the burden associated with manual ICD coding.
  • To enhance the quality and efficiency of encoded diagnostic data.

Main Methods:

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  • Development of a PC-based system, LUCID, utilizing natural language processing techniques.
  • Implementation of a user-friendly interface compatible with Microsoft Windows standards.
  • Testing the system's ability to process and encode diagnoses described in natural language.

    • Main Results:

    • LUCID significantly reduces the time and effort required for ICD diagnosis encoding.
    • The system demonstrates an enhancement in the quality and consistency of the encoded diagnosis list.
    • The convivial interface and versatile integration capabilities were highlighted.

    Conclusions:

    • Natural language-based diagnosis encoding, exemplified by LUCID, is a viable and efficient approach for the near future.
    • LUCID offers a practical solution to improve coding accuracy and streamline healthcare information management.
    • The widespread adoption of such NLP tools is expected to increase efficiency in healthcare administration.