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Automated Mapping of Observation Codes Using Extensional Definitions

Affiliations of the authors: University of Utah (KAZ) and Intermountain Health Care (SMH), Salt Lake City, Utah.

Correspondence and reprints: Kenneth A. Zollo, MD, University of Utah, Department of Medical Informatics, 50 North Medical Drive, AB193 SOM, Salt Lake City, UT 84132; e-mail: <kenneth.zollo{at}hsc.utah.edu>.

Objective: To create "extensional definitions" of laboratory codes from derived characteristics of coded values in a clinical database and then use these definitions in the automated mapping of codes between disparate facilities.

Design: Repository data for two laboratory facilities in the Intermountain Health Care system were analyzed to create extensional definitions for the local codes of each facility. These definitions were then matched using automated matching software to create mappings between the shared local codes. The results were compared with the mappings of the vocabulary developers.

Measurements: The number of correct matches and the size of the match group were recorded. A match was considered correct if the corresponding codes from each facility were included in the group. The group size was defined as the total number of codes in the match group (e.g., a one-to-one mapping is a group size of two).

Results: Of the matches generated by the automated matching software, 81 percent were correct. The average group size was 2.4. There were a total of 328 possible matches in the data set, and 75 percent of these were correctly identified.

Conclusions: Extensional definitions for local codes created from repository data can be utilized to automatically map codes from disparate systems. This approach, if generalized to other systems, can reduce the effort required to map one system to another while increasing mapping consistency.

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