Evaluating the Minimal Common Oncology Data Elements Suitability in Enhancing Clinical Observational Research.

Purpose: This article explored how suitable the minimal Common Oncology Data Elements (mCODE) standard is for the real-world evidence research of cancer patient characterization, disease characterization, treatment patterns, and treatment outcomes.

Methods: We identified research questions for each category, broke them down to clinical information elements, and mapped them to the mCODE model. Gaps were further categorized as model deficiencies, clarifying when the mCODE element availability was explicitly specified as an element, derived through external calculation, or implied as part of its support for Fast Healthcare Interoperability Resources.

Results: In our study, 20 research questions were categorized in the following areas: patient characteristics, disease characteristics, treatment patterns, and health outcomes. The mCODE model fully supports patient characterization but shows significant gaps in disease characteristics, treatment patterns, and health outcomes, particularly in areas like treatment regimens and therapy outcomes. Our analysis underscores the need to enhance the mCODE model to better support observational research.

Conclusion: We conclude that mCODE is partially suitable for observational research. Although mCODE shows promise for research purposes in patient and disease characterization, it currently lacks data elements needed to fully support identifying treatment patterns and health outcomes essential for comprehensive observational real-world evidence research.