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Related Experiment Videos

Combining laboratory data sets from multiple institutions using the logical observation identifier names and codes

D M Baorto1, J J Cimino, C A Parvin

  • 1Department of Pathology, Washington University School of Medicine, Washington University, St. Louis, MO 63110, USA. baorto@pathbox.wustl.edu

International Journal of Medical Informatics
|September 28, 1998
PubMed
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Standardizing laboratory test names with Logical Observation Identifier Names and Codes (LOINC) is crucial for data sharing. However, institutional coding differences, particularly in the "Kind of Property" field, frequently cause data mismatches, highlighting the need for quality coding standards.

Area of Science:

  • Medical Informatics
  • Health Data Standards
  • Laboratory Medicine

Background:

  • Automated data pooling for multi-institutional research and patient care requires standardized laboratory test names and codes.
  • The Logical Observation Identifier Names and Codes (LOINC) database provides a convention for test naming.
  • Previous pilot studies identified complexities in the LOINC coding process.

Purpose of the Study:

  • To expand on a pilot study by re-evaluating the use of LOINC for laboratory data exchange between two medical centers.
  • To assess the impact of an updated LOINC database version on test coding and data pooling.
  • To compare laboratory utilization by examining aggregate data derived from LOINC-coded tests.

Main Methods:

  • Two medical centers extracted raw laboratory data from their local information systems for a defined patient population.

Related Experiment Videos

  • Local laboratory tests were translated into LOINC codes using an updated LOINC database version.
  • Aggregate data were generated for comparison of laboratory utilization and to identify data matching failures.

    • Main Results:

    • Coding local tests into LOINC proved complex, with the "Kind of Property" field being particularly challenging.
    • Differences in local coding choices by institutions resulted in nonmatches in electronically pooled data.
    • Seventy-five percent of test matching failures between institutions were attributed to variations in local coding decisions.

    Conclusions:

    • LOINC has the potential to reduce manual data inspection for pooling laboratory data from diverse sources.
    • The system may offer built-in flexibility for matching stringency by selecting subsets of LOINC fields.
    • A standardized, quality coding procedure is essential, and specific examples require careful attention during LOINC mapping.