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