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. 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.
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