Paul A. Seegers

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.

Molecular Diagnostics (MDx) is integral to the personalization of cancer treatment, offering both prognostic and predictive insights that inform therapeutic decision-making. However, the rapid advancement of MDx in oncology comes with significant challenges for the development, implementation, and adherence to clinical guidelines. A systematic approach is required to monitor clinical practice, including the utilization of MDx and the evaluation of patient outcomes. Such an approach can facilitate evidence-based updates to clinical guidelines, enhance adherence, and increase transparency in healthcare decision-making. This study aimed to develop a proof-of-principle practice monitor for MDx using two showcases, non-small cell lung cancer (NSCLC) and endometrial cancer (EC). Adopting a socio-technical framework, stakeholders were engaged to ensure alignment of the monitor’s content with clinical needs. Critical clinical questions were identified, and computational decision algorithms were developed based on current guidelines and expert consensus. Nationwide real-world data registries were analyzed to assess the availability, quality, and completeness of data in relation to the clinical questions and decision algorithms. Variables included in decision algorithms were largely present for NSCLC and EC, confirming the feasibility of nationwide monitoring. While some clinical questions could be fully or partially addressed with the available data, many could not. This was due to fragmented data, inconsistent coding practices, and the use of non-standardized free-text entries. Accordingly, this initiative addresses key challenges related to data standardization, computational representation of guidelines, and variability in clinical reporting. With strong national databases and infrastructure already in place, successful implementation now hinges on focused multidisciplinary and multi-organizational collaboration and a learning community to sustain ongoing progress. This approach assessed the feasibility and challenges of continuous evaluation of MDx practice in a proof-of-principle practice monitor, as an important step towards a Learning Health System.

The lack of standardized reporting in placental pathology limits research scalability and clinical application despite consensus-based criteria like the Amsterdam Criteria. This study presents the development and implementation of a comprehensive Standardized Structured Reporting (SSR) tool for placental pathology. Utilizing the LogicNets© platform, the tool incorporates the Amsterdam Criteria, Freedman placental phenotype classification system, and consensus recommendations on placental examination. Development involved collaboration with placental pathology experts, literature review, and iterative feedback from pathologists to address reporting inconsistencies and ensure practicality. The SSR tool integrates evidence-based standards for examining and phenotyping the placenta, including structured gross and microscopic analyses. It introduces control mechanisms for severity grading and phenotype assignment, enabling precise assessment of placental lesions, even in multiple births. Organized into sections—Demographics, Macroscopy, Microscopy, and Group and Phenotype—it efficiently records and analyzes data. Additional functionalities include automated calculations of gross placental features, built-in controls, and versatile documentation of microscopic observations. The tool is designed for seamless integration into clinical workflows. Implementing the SSR tool could enhance placental pathology reporting quality, completeness, and consistency, facilitating large-scale analyses. Discrete data capture enables robust research, potentially improving the clinical utility and understanding of placenta-mediated diseases. However, further validation is required before widespread adoption. Embracing SSR could standardize placental examination, improve clinical interpretation, and advance research in placental pathology for enhanced utility in both research and clinical care.

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.

The adoption of Standardized Structured Reporting (SSR) in pathology offers significant potential to improve data consistency, completeness, and interoperability. This study combines quantitative data from an online survey of Belgian pathologists with qualitative insights from focus group interviews to identify key factors influencing SSR implementation. Survey results demonstrate strong support for SSR, particularly in enhancing report uniformity, completeness, and efficiency, especially in multidisciplinary teams and for secondary data use. Despite these advantages, participants identified several challenges, including the integration of SSR with existing IT systems, financial constraints, resistance to change, and legal and regulatory barriers. Focus group interviews reinforced these findings, emphasizing the need for collaboration, technological innovation, and governmental support. Effective strategies for overcoming these barriers include securing funding, providing comprehensive training, improving IT infrastructure, and advocating for supportive legal frameworks. A hybrid approach, balancing centralized standardization with local customization and drawing on international experiences, seems to offer the most promise for successfully integrating SSR into pathology practice.