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Conversion problems concerning automated mapping from ICD-10 to ICD-9

S Schulz1, A Zaiss, R Brunner

  • 1Department of Medical Informatics, University of Freiburg, Germany. stschulz@uni-freiburg.de

Methods of Information in Medicine
|October 27, 1998
PubMed
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To address challenges in comparing diagnoses coded using both ICD-9 and ICD-10, a conversion table was developed. This tool facilitates mapping between the two systems, improving data comparability in healthcare.

Area of Science:

  • Medical Informatics
  • Health Informatics
  • Clinical Coding

Background:

  • The parallel use of International Classification of Diseases, Ninth Revision (ICD-9) and International Classification of Diseases, Tenth Revision (ICD-10) hinders diagnostic comparability.
  • Accurate and consistent health data is crucial for research, public health surveillance, and healthcare management.

Purpose of the Study:

  • To develop a symmetric, interactive conversion table for mapping between ICD-9 and ICD-10 codes.
  • To evaluate the accuracy and efficiency of automated mapping between the two classification systems.

Main Methods:

  • A vector space text-retrieval method was employed to construct a symmetric conversion table.
  • The method focused on three- and four-character classes of the tabular list for mapping.

Related Experiment Videos

  • Automated mapping tables were created for multi-valued relations and challenging cases.

    • Main Results:

    • Unambiguous mapping was achieved from ICD-9 to ICD-10 in 64% of classes and from ICD-10 to ICD-9 in 87%.
    • A significant portion (13%) presented multi-valued relations requiring further automated mapping.
    • Automated mapping was impossible in 1.6% of cases due to newly defined concepts and structural differences.

    Conclusions:

    • The developed conversion table significantly enhances the comparability of diagnoses coded in ICD-9 and ICD-10.
    • Future revisions of the International Classification of Diseases (ICD) should prioritize compatibility between classification systems.
    • The findings support improved data consistency for clinical research and health informatics applications.