Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer.

PURPOSE: The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships. METHODS AND MATERIALS: The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs. The Big Data Science Committee adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community. RESULTS: We developed the Operational Ontology for Oncology (O3), which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, and the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to 4 constituencies: device manufacturers, centers of clinical care, researchers, and professional societies. CONCLUSIONS: O3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used to create large, representative, findable, accessible, interoperable, and reusable data sets to support the scientific objectives of grant programs. The construction of comprehensive "real-world" data sets and application of advanced analytical techniques, including artificial intelligence, holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative data sets.

Computable Guidelines and Clinical Decision Support for Cervical Cancer Screening and Management to Improve Outcomes and Health Equity.

Cervical cancer is highly preventable when precancerous lesions are detected early and appropriately managed. However, the complexity of and frequent updates to existing evidence-based clinical guidelines make it challenging for clinicians to stay abreast of the latest recommendations. In addition, limited availability and accessibility to information technology (IT) decision supports make it difficult for groups who are medically underserved to receive screening or receive the appropriate follow-up care. The Centers for Disease Control and Prevention (CDC), Division of Cancer Prevention and Control (DCPC), is leading a multiyear initiative to develop computer-interpretable ("computable") version of already existing evidence-based guidelines to support clinician awareness and adoption of the most up-to-date cervical cancer screening and management guidelines. DCPC is collaborating with the MITRE Corporation, leading scientists from the National Cancer Institute, and other CDC subject matter experts to translate existing narrative guidelines into computable format and develop clinical decision support tools for integration into health IT systems such as electronic health records with the ultimate goal of improving patient outcomes and decreasing disparities in cervical cancer outcomes among populations that are medically underserved. This initiative meets the challenges and opportunities highlighted by the President's Cancer Panel and the President's Cancer Moonshot 2.0 to nearly eliminate cervical cancer.