Lessons Learned

The mapping of the Laboratory Information System (LIS) test catalogue to the pan-Canadian LOINC Observation Code Database (pCLOCD) requires extensive preparation prior to beginning the actual mapping. The pCLOCD is published in Excel or Access format, it is expected that mapping projects have access to a mapping tool (LOINC Mapping – Planning Stage). Mapping principles are required to guide mappers in their selections, to ensure maps are done consistently and to provide mapping criteria which supports the preparation of the LIS Test Catalogue (LOINC Mapping Mapping: Managing LIS Test Catalogues).


  • Has the basic workflow that follows the data from its source (LIS) to its final destination (final system) been mapped out?
  • Are the requirements and data expectations of the final system well defined?
  • Have the methods and versions of data transmission been identified?
  • Is the mapping relationship between the source terms and the chosen standard identified? Is a User Interface terminology required?
  • Has source data been grouped or subdivided and terms defined (see LOINC Mapping – Managing LIS Test Catalogues)?
  • Key considerations for Mapping Principles include defining principles to cover:
    • Granularity required
    • Matching criteria
    • Managing mapping documentation
    • Lab supporting documents and mapping process documents
    • Resources and resource support
    • Communication between resources
    • Governance
    • Quality assurance processes
    • Change management and maintenance processes
    • Operational processes

Next Steps:

  • Gather key resources to develop mapping principles and guidelines. Resources should include:
    • LIS Subject Matter Experts or Administrators who are familiar with the LIS content
    • Clinical Subject Matter experts that can provide guidance on test interpretations
    • Mapping resources with specific LOINC expertise
    • Resources who are familiar with JLIS architecture and expected use
  • Mapping principles are best developed with an in-depth knowledge of the source data, the standards that will be used and the expected use of the final standardized data.

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