Cross-terminology mapping challenges: A demonstration using medication terminological systems

  • Authors:

  • Himali Saitwal;David Qing;Stephen Jones;Elmer V. Bernstam;Christopher G. Chute;Todd R. Johnson

  • Affiliations:

  • The University of Texas School of Biomedical Informatics at Houston, 7000 Fannin Suite 600, Houston, TX 77030, USA;The University of Texas School of Biomedical Informatics at Houston, 7000 Fannin Suite 600, Houston, TX 77030, USA;The University of Texas School of Biomedical Informatics at Houston, 7000 Fannin Suite 600, Houston, TX 77030, USA and Department of Surgery, The Methodist Hospital Research Institute, 6550 Fannin ...;The University of Texas School of Biomedical Informatics at Houston, 7000 Fannin Suite 600, Houston, TX 77030, USA and Department of Internal Medicine, The University of Texas, Health Science Cent ...;Division of Biomedical Statistics and Informatics, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA;The University of Texas School of Biomedical Informatics at Houston, 7000 Fannin Suite 600, Houston, TX 77030, USA and Division of Biomedical Informatics, Department of Biostatistics, College of P ...

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2012

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Abstract

Standardized terminological systems for biomedical information have provided considerable benefits to biomedical applications and research. However, practical use of this information often requires mapping across terminological systems-a complex and time-consuming process. This paper demonstrates the complexity and challenges of mapping across terminological systems in the context of medication information. It provides a review of medication terminological systems and their linkages, then describes a case study in which we mapped proprietary medication codes from an electronic health record to SNOMED CT and the UMLS Metathesaurus. The goal was to create a polyhierarchical classification system for querying an i2b2 clinical data warehouse. We found that three methods were required to accurately map the majority of actively prescribed medications. Only 62.5% of source medication codes could be mapped automatically. The remaining codes were mapped using a combination of semi-automated string comparison with expert selection, and a completely manual approach. Compound drugs were especially difficult to map: only 7.5% could be mapped using the automatic method. General challenges to mapping across terminological systems include (1) the availability of up-to-date information to assess the suitability of a given terminological system for a particular use case, and to assess the quality and completeness of cross-terminology links; (2) the difficulty of correctly using complex, rapidly evolving, modern terminologies; (3) the time and effort required to complete and evaluate the mapping; (4) the need to address differences in granularity between the source and target terminologies; and (5) the need to continuously update the mapping as terminological systems evolve.