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Jennifer Markarian is manufacturing editor of Pharmaceutical Technology.
The Observational Medical Outcomes Partnership (OMOP), which is managed by the Foundation, desires feedback on proposed research priorities and tasks.
The Observational Medical Outcomes Partnership (OMOP), a public–private partnership managed by the nonprofit Foundation for the National Institutes of Health and chaired by FDA, conducts methodological research on the use of observational healthcare data in studying the effects of medical products. The OMOP executive board is seeking public feedback on the priorities, infrastructure tasks, and research tasks it has proposed for 2013–2016. Comments are being collected via the OMOP website through Sept. 15, 2012.
“Receiving public feedback regarding OMOP’s work is vital to our success,” said OMOP executive board member, Ronald Krall, MD, in an Aug. 27, 2012 press release. “We want to make sure we hear every idea about how to use healthcare data to improve the safety and effectiveness of medical products.”
OMOP was formed in 2008, and the group’s research since then has affirmed its belief that the science of observational research requires an empirical and reproducible approach to methodology and systematic application. OMOP’s research has generated empirical evidence about the ability of observational analysis methods to identify true risks of medical products and discriminate from false findings. Experiments were designed to understand how an estimated effect from an observational study relates to the true relationship between medical product exposure and adverse events.
Proposed priorities for upcoming research, described in the OMOP Call for Comments, include creating a foundation for the common representation of healthcare data, developing empirical evidence to support study of the association between medical interventions and adverse outcomes, developing empirical evidence to aid detection of unsuspected associations between medical events and interventions, and creating a framework for evidence-based decision making.