Once a database has been converted to the OMOP CDM, evidence can be generated using standardized analytics tools. It would also support collaborative research across data sources both within and outside the United States, in addition to being manageable for data owners and useful for data users. The CDM can accommodate both administrative claims and EHR, allowing users to generate evidence from a wide variety of sources. Each has been collected for a different purpose, resulting in different logical organizations and physical formats, and the terminologies used to describe the medicinal products and clinical conditions vary from source to source. Electronic Medical Records (EMR) are aimed at supporting clinical practice at the point of care, while administrative claims data are built for the insurance reimbursement processes. Observational databases differ in both purpose and design. The concept behind this approach is to transform data contained within those databases into a common format (data model) as well as a common representation (terminologies, vocabularies, coding schemes), and then perform systematic analyses using a library of standard analytic routines that have been written based on the common format. The OMOP Common Data Model allows for the systematic analysis of disparate observational databases. What is the OMOP Common Data Model (CDM)? Most importantly, we have an active community that has done many data conversions (often called ETLs) with members who are eager to help you with your CDM conversion and maintenance. We provide resources to convert a wide variety of datasets into the CDM, as well as a plethora of tools to take advantage of your data once it is in CDM format. We at OHDSI are deeply involved in the evolution and adoption of a Common Data Model known as the OMOP Common Data Model. And despite the growing use of standard terminologies in healthcare, the same concept (e.g., blood glucose) may be represented in a variety of ways from one setting to the next. These data may be stored in different formats using different database systems and information models. Data are collected for different purposes, such as provider reimbursement, clinical research, and direct patient care. Healthcare data can vary greatly from one organization to the next. Thanks also to Carleton University and Queens University, which continue to contribute markup in-kind, in addition to any MarkIt! funds received.Data standardization is the critical process of bringing data into a common format that allows for collaborative research, large-scale analytics, and sharing of sophisticated tools and methodologies. OCUL would like to congratulate the successful applicants, and to thank all the institutions that have participated in the program to date: Carleton University, the University of Guelph, McMaster University, the University of Ottawa, Queens University, Ryerson University, and the University of Toronto. In response to a call for applications circulated in March 2013, OCUL has selected the following institutions to participate in the program from May 2013 to April 2014: The program supports the hiring of students to mark up statistical datasets, using the DDI metadata standard, for inclusion in. OCUL is pleased to announce the 2013-14 round of funding for MarkIt!, the student markup program.
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