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Development and evaluation of a computerized admission diagnoses encoding system

M L Gundersen1, P J Haug, T A Pryor

  • 1Department of Medical Informatics, L DS Hospital, Salt Lake City, Utah 84143, USA.

Computers and Biomedical Research, an International Journal
|October 1, 1996
PubMed
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This study developed a natural language understanding system to encode free-text hospital admission diagnoses. The system achieved 76% accuracy, matching manual methods when successful, and shows potential for automating coding.

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Health Information Systems

Background:

  • Hospital information systems capture patient data via free-text and precoded schemes.
  • Free-text data presents challenges for review and analysis in healthcare settings.
  • Natural Language Understanding (NLU) offers a solution for extracting information from unstructured clinical text.

Purpose of the Study:

  • To develop and evaluate an NLU system for encoding free-text hospital admission diagnoses.
  • To assess the system's performance against manual coding methods.
  • To explore the potential for automating the clinical coding process.

Main Methods:

  • The system integrates semantic and syntactic linguistic analysis techniques.
  • It was designed to identify, extract, and encode information from free-text admission diagnoses.

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  • Performance was evaluated based on encoding accuracy and efficiency.

    • Main Results:

    • The system demonstrated reasonable overall performance, accurately encoding approximately 76% of admission diagnoses.
    • When encodings were generated, accuracy was comparable to current manual coding methods.
    • Inefficiencies were noted, with the system failing to generate encodings in about 15% of cases.

    Conclusions:

    • The developed NLU system shows promise for partially automating the coding of free-text admission diagnoses.
    • Further modifications are needed to improve system efficiency and reduce instances where encodings cannot be generated.
    • The findings support the utility of NLU in enhancing the usability of unstructured clinical data.