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This summary is machine-generated.

This study presents a method to make cancer pathology reports computable and interoperable using SNOMED Clinical Terms (SNOMED CT) and Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR). The approach successfully structured International Collaboration on Cancer Reporting (ICCR) colorectal cancer data for standardized exchange.

Keywords:
SNOMED clinical termsfast healthcare interoperability resourcessemantic interoperabilitystructured data capturesynoptic pathology reportsyntactic interoperability

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Area of Science:

  • Medical Informatics
  • Computational Pathology
  • Health Data Standards

Background:

  • Pathology reports are crucial for cancer patient care.
  • Standardization efforts by College of American Pathologists and International Collaboration on Cancer Reporting (ICCR) have improved pathology report structure.
  • Standards development organizations have advanced methods for data computability and exchange, enhancing pathology report interoperability.

Purpose of the Study:

  • To develop a method for making pathology cancer reports computable and interoperable.
  • To utilize published cancer reporting protocols, SNOMED Clinical Terms (SNOMED CT), and Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR).

Main Methods:

  • The ICCR colorectal cancer (CRC) reporting dataset was evaluated by terminologists and pathologists.
  • SNOMED CT concepts were mapped to data elements within the ICCR CRC dataset.
  • The dataset was converted into a FHIR structured data capture (SDC) questionnaire using National Library of Medicine tooling.
  • The questionnaire was rendered into a FHIR-conformant message for data exchange.

Main Results:

  • The ICCR CRC dataset comprised 216 data elements; 207 were successfully mapped to SNOMED CT and integrated into a FHIR SDC construct.
  • Nine data elements were ambiguous and could not be reliably encoded.
  • The resulting FHIR SDC form accurately represented the ICCR CRC dataset and provided data in an R4 JSON format for exchange.

Conclusions:

  • A tractable and extensible approach was demonstrated for making cancer pathology reports fully computable and interoperable.
  • International Collaboration on Cancer Reporting (ICCR) datasets can be fully represented using SNOMED CT for semantic accuracy and computability.
  • The use of FHIR SDC facilitates standardized and widespread clinical data exchange, offering a clear implementation pathway despite adoption challenges.