Conte, Marisa

Abstract Objectives Embedded pragmatic clinical trials (ePCTs) are conducted as part of routine clinical care and therefore use data collected from real-world data sources, such as electronic health record systems and administrative claims. A common approach for using these types of data across all phases of trial conduct is to create a computable phenotype (an explicitly defined data query data, including specified data types, data codes, and logical parameters) to capture patients with a clinical condition, exposure, symptom, characteristic, treatment, or outcome of interest. Materials and Methods The Electronic Health Record (EHR) Core Working Group of the NIH Pragmatic Trials Collaboratory captured the experiences of investigators in developing, adapting, and applying computable phenotype definitions in their studies. Results Four case studies describe different approaches to developing and using computable phenotypes in pragmatic trials. Discussion We recommend: (1) developing computable phenotypes as part of a team with multiple areas of expertise; (2) appropriate validation; (3) dissemination of salient details regarding phenotype creation; and (4) capturing and reporting any modifications made to phenotypes during the conduct of the trial. Conclusion A range of issues and decisions influence how computable phenotypes are developed and used in pragmatic clinical trials. Multidisciplinary teams that understand the context of the data, including the reason the data are collected and potential sources of bias, are best suited for the development and validation of phenotypes for ePCTs.

AbstractIntroductionComputable biomedical knowledge artifacts (CBKs) are digital objects conveying biomedical knowledge in machine‐interpretable structures. As more CBKs are produced and their complexity increases, the value obtained from sharing CBKs grows. Mobilizing CBKs and sharing them widely can only be achieved if the CBKs are findable, accessible, interoperable, reusable, and trustable (FAIR+T). To help mobilize CBKs, we describe our efforts to outline metadata categories to make CBKs FAIR+T.MethodsWe examined the literature regarding metadata with the potential to make digital artifacts FAIR+T. We also examined metadata available online today for actual CBKs of 12 different types. With iterative refinement, we came to a consensus on key categories of metadata that, when taken together, can make CBKs FAIR+T. We use subject‐predicate‐object triples to more clearly differentiate metadata categories.ResultsWe defined 13 categories of CBK metadata most relevant to making CBKs FAIR+T. Eleven of these categories (type, domain, purpose, identification, location, CBK‐to‐CBK relationships, technical, authorization and rights management, provenance, evidential basis, and evidence from use metadata) are evident today where CBKs are stored online. Two additional categories (preservation and integrity metadata) were not evident in our examples. We provide a research agenda to guide further study and development of these and other metadata categories.ConclusionA wide variety of metadata elements in various categories is needed to make CBKs FAIR+T. More work is needed to develop a common framework for CBK metadata that can make CBKs FAIR+T for all stakeholders.