A Systematic Approach to Observational Research Using Multiple Existing Healthcare Databases

The Observational Medical Outcomes Partnership (OMOP) is a public-private partnership designed to help improve the monitoring of drugs. The partnership began in Q4-2008 and is conducting a two-year research initiative to determine whether it is feasible and useful to analyze existing healthcare databases to identify and evaluate safety and benefit issues of drugs already on the market. OMOP draws on the expertise and resources of the pharmaceutical industry, academic institutions, non-profit organizations, the Food and Drug Administration, and other federal agencies. It is funded and managed through the Foundation for the National Institutes of Health. In addition to sponsoring research, OMOP will create data models, experimental protocols, and database evaluation tools that will be placed in the public domain to encourage research by a broad community of investigators. One of OMOP’s goals is to define processes that can be used to assess the feasibility and utility of using observational data to identify and evaluate associations between drugs and health-related conditions. To facilitate its methodological research, the partnership will evaluate the performance of various analytical methods for identifying drug-outcome associations across multiple disparate data sources (administrative claims and electronic health records). OMOP is developing and testing a program structure and governance model, a common semantic data model and analytical methods that will enable a distributed collaboration of research partners to analyze their own healthcare data and share the results of their analysis. The common semantic data model will enable disparate sources of healthcare data to be standardized to common formats and medical concepts, thus enabling the same analytical programs to be used by all research partners and the comparison of analytical results. This presentation will provide an overview of the OMOP program, models and analytical methods. It will also present the lessons learned to date at the time of the presentation.