About Us

Important:

These original OMOP resources are no longer supported. You can find the latest developments by visiting the OHDSI website.



History

During it's years as an active project, OMOP has demonstrated the feasibility of establishing a common infrastructure which can accommodate observational data of different types (both claims and EHRs) from sources around the world, and successfully developed and executed large-scale statistical analyses capable of enabling active drug safety surveillance across prescription medications.


Background

America’s drug-approval process has set the global standard for rigorous safety and effectiveness review, but even with clinical trials and other safeguards, it is impossible to fully understand the impact of any particular medical intervention until it is widely used. Once on the market, drugs are further studied by pharmacoepidemiologists and other researchers, who work diligently to identify safety issues and potential unanticipated benefits. Many important discoveries have been made, but researchers were always hampered by a reliance on voluntary reporting as the primary source of data. As a result, there is growing interest to use other sources of data generated within the healthcare setting. Unlike clinical trials, the use of observational data and methods to monitor medical product safety is challenged by the lack of accepted research methods and practices, leading to the well-known phenomenon of multiple studies of the same intervention yielding vastly different results.


OMOP’s Challenge

In 2007, recognizing that the increased use of electronic health records (EHR) and availability of other large sets of marketplace health data provided new learning opportunities, Congress directed the FDA to create a new drug surveillance program to more aggressively identify potential safety issues. The FDA launched several initiatives to achieve that goal, including the well-known Sentinel program to create a nationwide data network.

In partnership with PhRMA and the FDA, the Foundation for the National Institutes of Health (FNIH) launched the Observational Medical Outcomes Partnership (OMOP), a public-private partnership. This interdisciplinary research group has tackled a surprisingly difficult task that is critical to the research community’s broader aims: identifying the most reliable methods for analyzing huge volumes of data drawn from heterogeneous sources.

Employing a variety of approaches from the fields of epidemiology, statistics, computer science and elsewhere, OMOP seeks to answer a critical challenge: what can medical researchers learn from assessing these new health databases, could a single approach be applied to multiple diseases, and could their findings be proven? Success would mean the opportunity for the medical research community to do more studies in less time, using fewer resources and achieving more consistent results. In the end, it would mean a better system for monitoring drugs, devices and procedures so that the healthcare community can reliably identify risks and opportunities to improve patient care.


OMOP’s Work

Officially launched in late 2008 as a two-year pilot, OMOP worked to design experiments testing a variety of analytical methodologies in a range of data types to look for drug impacts that are already well-known. The initial findings were inconclusive, and the team found more challenges than answers.

From this initial base, the project moved forward with additional research, and found greater success. Over the course of 2011 and 2012, research yielded greater confidence that particular methods used with particular types of data can reliably identify correlations between individual medical interventions and specific health outcomes. While there is still work to be done, the findings suggest meaningful progress toward the ultimate goal.


OMOP’s Future

Having achieved its mission as envisioned by the founding members of the partnership, the OMOP Partnership concluded at FNIH in June. The OMOP research investigators have formed a new collaborative, the Observational Health Data Sciences and Informatics OHDSI to continue its mission of developing tools and evidence to support the appropriate analysis of observational data.