OMOP Community

Workgroups

Come be a part of the Community Work Group. We meet on the 3rd Thursday of the month to discuss various topics about the common data model, vocabularies, statistical methods and implementation. Meeting starts at 11:00 am ET and lasts about one hour. Phone number 866.740.1260 and Access Code: 8281207

If you would like to be a part of the either group, please contact ewelebob@reaganudall.org to receive the logistics for the meetings. The slides and audio of past meetings can be downloaded below.

Data Management Meeting - Vocabulary overview (01-Aug-2013)

Statistical Group Meeting - P-value calibration (08-Aug-2013)

Data Management Meeting - Drug Dose / Dose Era (05-Sep-2013)

Statistical Group Meeting - Predicting Health Outcomes From Health Histories (10-Oct-2013)

Community Meeting - Exploring OMOP 2011-2012 experiments - Susan Gruber (16-Oct-2013)

Data Management Meeting - Examination of CDM Vocabulary - Hugh Kawabata (05-Dec-2013)

Open Further Framework - University of Utah (20-Mar-2014)

Collaborations

Auburn University and HP Labs
Developing a structured process for measuring and interpreting health outcomes of interest in the OMOP common data model

The first two years of OMOP research yielded significant progress towards structured use of existing observational data sources for active surveillance. OMOP established distributed and centralized data access mechanisms with claims and EHR data transformed to a common data model. In 2011, one of OMOP's priorities include refinement of some of the strategies previously developed. In particular, definitions for measurement of health outcomes of interest (HOI) in observational data are a critical element to bolster confidence in output of the active surveillance process.

Our research will investigate 3-4 of the OMOP HOIs (and method data management strategies), starting with acute liver injury, and their impact on method performance. The overall goal of this work is to minimize false positive cases through better measurement of the HOI. Our process includes refining HOI definitions through clinical review of cases, creation of training datasets through expert classification of true cases, predictive modeling to further refine HOI definitions, and comparison of methodological performance based on probability thresholds.

DARTNet Institute
Implementing the OMOP CDM and Vocabularies

The DARTNet Institute is working with multiple clinical and academic partners that are and have been supported by numerous grants from the Agency for Healthcare Research and Quality, the National Institutes of Health, the Centers for Disease Control and other funders for the purposes of improving primary care practice and supporting comparative effectiveness research. The DARTNet Institute investigators are using the OMOP common data model and licensed vocabularies as the starting point for creating a common data model among DARTNet Institute clinical partners.

Principal Investigator: Wilson D. Pace, MD, FAAFP, University of Colorado Denver

Department of Veterans Affairs PBM Center for Medication Safety (VAMedSAFE)
Continuation of Evaluation of OMOP Methods on VA Data

The final report of the VA Center for Medication Safety (VAMedSAFE) for the initial OMOP research was submitted in January 2011. Continuing to collaborate with OMOP, the VAMedSAFE performed additional analyses to further evaluate the performance of the OMOP methods on the VA data.

The following methods were implemented: disproportionality analysis (DP), multi-set case control estimation (MSCCE), high-dimensional propensity score (HDPS), univariate self-controlled case series (USCC), observational screening (OS), maximum sequential probability ratio test (maxSPRT), conditional sequential sampling procedure (CSSP), case-control surveillance (CCS), case-crossover (CCO), high throughput safety-screening (HSIU), and incident user design HOI method (IUD-HOI).

SCAlable National Network for Effectiveness Research (SCANNER)
Implementing the OMOP CDM and Vocabularies

Researchers at the University of California, San Diego School of Medicine, led by Lucila Ohno-Machado, MD, PhD, founding chief of the Division of Biomedical Informatics, are creating the The SCAlable National Network for Effectiveness Research (SCANNER), which aims to make it easier to integrate data from widely dispersed health care systems. SCANNER will create certified computational systems and architecture necessary to securely exchange health information so that the same data can be used for comparative effectiveness research. To accomplish this, SCANNER is using the OMOP common data model version 4.0. SCANNER is supported by the Agency for Healthcare Research and Quality (AHRQ) through the American Recovery & Reinvestment Act of 2009, Grant R01 HS19913-01.

Principal Investigator: Lucila Ohno-Machado, MD, PhD, UCSD Professor of Medicine, Founding Chief of the Division of Biomedical Informatics, Associate Dean for Informatics and Technology

Scalable Architecture for Federated Translational Inquiries Network (SAFTINet)
Implementing the OMOP CDM and Vocabularies<

The overall goals of SAFTINet are to enhance the capacity and capability of a safety net-focused distributed research network to conduct prospective comparative effectiveness research via a multi-setting, multi-state organization. SAFTINet will both leverage and extend the established governance and technologic capabilities of the Distributed Ambulatory Research in Therapeutics Network (DARTNet) to allow more flexible options for participants and improved grid technology. SAFTINet is developing an infrastructure using the TRIAD grid and OMOP CDM. This project will allow researchers, health policy experts, payers, and clinicians to better understand the impact of a wide variety of health care interventions on health outcomes for minority, underserved and socioeconomically disadvantage populations by supporting CER.

Principal Investigator: Lisa M. Schilling, M.D., M.S.P.H., Associate Professor, University of Colorado School of Medicine

Prior Collaborations

  • Computer Sciences Corporation
  • Eli Lilly and Company
  • Humana, Inc., University of Miami-Humana Health Services Research Center
  • i3 Drug Safety
  • Indiana University-Regenstrief Institute
  • Harvard Pilgrim Health Care Institute
  • Merck Research Laboratories, Biostatistics and Research Data Systems
  • Partners HealthCare System, Research Computing for Partners HealthCare System
  • ProSanos Corporation (now United BioSource Corporation)
  • Risk Benefit Statistics LLC, Robert L. Obenchain, PhD
  • RTI International
  • SDI Health
  • United BioSource Corporation
  • University of North Carolina at Chapel Hill and SAS Institute, Inc.
  • University of Utah
  • University of Wisconsin-Madison, Department of Biostatistics & Medical Informatics