Alexander K. Goel

Background: Pathology reports contain critical information necessary for the management of cancer patient care. Efforts to structure pathology cancer reports by the College of American Pathologists and the International Collaboration on Cancer Reporting (ICCR) have been successful in standardizing pathology reports. Likewise, standards development organizations have advanced methods to improve data computability and exchange, by enabling interoperability of pathology cancer reports. Objective: This study aimed to provide a tractable method to render pathology cancer reports computable and interoperable using published cancer reporting protocols, SNOMED Clinical Terms (SNOMED CT) and Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR). Methods: The ICCR colorectal cancer (CRC) reporting dataset (version 1.0) was evaluated by terminologists and pathologists. SNOMED CT concepts were bound to the data elements. The dataset was then converted into a FHIR structured data capture (SDC) questionnaire using the United States National Library of Medicine tooling and rendered into a FHIR-conformant message for data exchange. Results: The ICCR CRC dataset contained 216 data elements; 207 data elements were bound to SNOMED CT and incorporated into a FHIR SDC construct. The 9 uncoded data elements were ambiguous and could not be reliably encoded. The resultant FHIR SDC form fully represented the ICCR CRC dataset and rendered these data in an R4 JSON format for data exchange. Conclusions: This study demonstrates a tractable and extensible approach to making cancer pathology reports fully computable and interoperable that can be broadly adopted. ICCR datasets are supported internationally and supported by multiple national pathology societies. These datasets can be fully represented using SNOMED CT to render data elements computable and semantically faithful to their intended meaning. The use of the FHIR SDC construct enables widespread and standardized data exchange of clinical information. While challenges remain, including FHIR adoption and the need to maintain current clinical content and standard terminology, this approach provides a clear pathway toward implementation.

BACKGROUND Pathology reports contain critical information necessary to manage cancer patient care. Efforts to structure pathology cancer reports by the College of American Pathologists and the International Collaboration on Cancer Reporting (ICCR) have been successful in standardizing pathology reports. Likewise, methods to improve data computability and exchange by standards development organizations have progressed to make pathology cancer reports interoperable. OBJECTIVE This study aims to provide a tractable method to render pathology cancer reports computable and interoperable using published cancer reporting protocols, SNOMED Clinical Terms (SNOMED CT) and Heath Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR). METHODS The ICCR colorectal cancer (CRC) reporting dataset version 1.0 was evaluated by terminologists and pathologists. SNOMED CT concepts were bound to the data elements. The dataset was then converted into a FHIR structured data capture (SDC) questionnaire using the United States National Library of Medicine (US NLM) tooling and rendered into a FHIR conformant message for data exchange. RESULTS The ICCR CRC dataset contained 216 data elements, 207 data elements could be bound to SNOMED CT and incorporated into a FHIR SDC construct. The 9 uncoded data elements were ambiguous and could not be reliably encoded. The resultant FHIR SDC form fully represented the ICCR CRC dataset and rendered these data in a R4 JSON format data exchange. CONCLUSIONS This project demonstrates a tractable and extensible approach to make cancer pathology reports fully computable and interoperable that can be broadly adopted. ICCR datasets are supported internationally and supported by multiple national societies of pathology. These datasets are fully represented using SNOMED CT to render data elements computable and semantically faithful to their intended meaning. The use of the FHIR SDC construct enables widespread, common data exchange of clinical information. While challenges of FHIR adoption as well as maintaining currency of clinical content and standard terminology remain, the presented approach provides a clear pathway towards implementation.

Cancer Care Ontario (CCO) is the clinical advisor to the Ontario Ministry of Health and Long-Term Care for the funding and delivery of cancer services. Data contained in radiology reports are inaccessible for analysis without significant manual cost and effort. Synoptic reporting includes highly structured reporting and discrete data capture, which could unlock these data for clinical and evaluative purposes. To assess the feasibility of implementing synoptic radiology reporting, a trial implementation was conducted at one hospital within CCO’s Lung Cancer Screening Pilot for People at High Risk. This project determined that it is feasible to capture synoptic data with some barriers. Radiologists require increased awareness when reporting cases with a large number of nodules due to lack of automation within the system. These challenges may be mitigated by implementation of some report automation. Domains such as pathology and public health reporting have addressed some of these challenges with standardized reports based on interoperable standards, and radiology could borrow techniques from these domains to assist in implementing synoptic reporting. Data extraction from the reports could also be significantly automated to improve the process and reduce the workload in collecting the data. RadLex codes aided the difficult data extraction process, by helping label potential ambiguity with common terms and machine-readable identifiers.