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

Charles S. Mayo, Mary U. Feng, Kristy K. Brock, Randi Kudner, Peter Balter, Jeffrey C. Buchsbaum, Amanda Caissie, Elizabeth Covington, Emily C. Daugherty, Andre L. Dekker, Clifton D. Fuller, Anneka L. Hallstrom, David S. Hong, Julian C. Hong, Sophia C. Kamran, Eva Katsoulakis, John Kildea, Andra V. Krauze, Jon J. Kruse, Tod McNuttMichelle Mierzwa, Amy Moreno, Jatinder R. Palta, Richard Popple, Thomas G. Purdie, Susan Richardson, Gregory C. Sharp, Shiraishi Satomi, Lawrence R. Tarbox, Aradhana M. Venkatesan, Alon Witztum, Kelly E. Woods, Yuan Yao, Keyvan Farahani, Sanjay Aneja, Peter E. Gabriel, Lubomire Hadjiiski, Dan Ruan, Jeffrey H. Siewerdsen, Steven Bratt, Michelle Casagni, Su Chen, John C. Christodouleas, Anthony DiDonato, James Hayman, Rishhab Kapoor, Saul Kravitz, Sharon Sebastian, Martin Von Siebenthal, Walter Bosch, Coen Hurkmans, Sue S. Yom, Ying Xiao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)533-550
Number of pages18
JournalInternational Journal of Radiation Oncology Biology Physics
Volume117
Issue number3
DOIs
StatePublished - Nov 1 2023

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