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A study to extract contextual factors affecting decision-making in the clinical setting from the literature.

Common Data Models (CDMs) are essential tools for data harmonization, which can lead to significant improvements in healthcare. CDMs harmonize data from disparate sources and eases collaborations across institutions which lead to generation of larger standardized data repositories across different entities. This Scoping Review (Sc-R) on methods used in the development of CDMs for healthcare aims to obtain a broad overview of approaches that are used in developing CDMs, i.e., Common Data Elements (CDEs) or Common Data Sets (CDS) for different disease domains on an international level. To get an overview of the state-of-the-art literature databases, namely PubMed, Web of Science, Science Direct, and Scopus are searched for five-year publications, starting from 2017, with associated keywords. The included articles will be evaluated methodically and a list of different types of methods will be created. The methods will then be categorized into groups.

In this review, we evaluate the current potential of the OMOP to be applicable in cancer prediction and how comprehensively the genomic vocabulary extension of OMOP can serve current needs within the field.

Introduction: Clinical decision support systems (CDSS) have great potential for personalized medicine [ref:1], [ref:2], [ref:3], [ref:4], [ref:5]. However, CDSS are often insufficiently accepted and used, mainly because no user-centered development process [for full text, please go to the a.m. URL]