A comparison of four schemes for codification of problem lists

Area of Science:

• Medical Informatics
• Health Information Systems
• Clinical Documentation

Background:

• Accurate representation of patient problem lists is crucial for effective healthcare delivery and research.
• Existing medical coding schemes vary in their ability to capture clinical complexity.
• Evaluating coding scheme completeness is essential for optimizing health information systems.

Purpose of the Study:

• To assess the completeness of four major coding schemes in representing patient problem lists.
• To compare the performance of Unified Medical Language System (UMLS), Systematized Nomenclature of Medicine (SNOMED), Read coding system, and International Classification of Diseases (ICD-9-CM).

Main Methods:

• Collected 400 patient problems from primary care records in Omaha and Seattle.
• Matched problems against descriptions in UMLS (4th ed.), SNOMED International, Read (v2), and ICD-9-CM.
• Five medical faculty reviewers rated the matches using a five-point Likert scale for satisfaction.

Main Results:

• Unified Medical Language System (UMLS) and Systematized Nomenclature of Medicine (SNOMED) showed significantly better performance.
• Read coding and International Classification of Diseases (ICD-9-CM) exhibited lower completeness rates.
• No single coding scheme was found to be entirely comprehensive.

Conclusions:

• UMLS and SNOMED are more effective than Read coding and ICD-9-CM for capturing clinical problem list content.
• UMLS and SNOMED offer distinct, potentially complementary advantages for systems developers.
• Further development is needed to achieve comprehensive coverage across all coding schemes.