OHDSI’s central mission is to improve health by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care. We work towards that goal in the areas of data standards, methodological research, open-source analytics development, and clinical applications.
Our workgroups present opportunities for all community members to find a home for their talents and passions, and make meaningful contributions. We are always looking for new collaborators. Learn more about these workgroups by checking out this page.
The ATLAS workgroup will provide a forum for the OHDSI community of developers that are interested in improving the open-source software applications ATLAS & WebAPI. These tools aim to provide capabilities to perform phenotype evaluation and design standardized analytics to execute on the OMOP Common Data Model.
The Clinical Trials workgroup objective is to facilitate the integration of clinical trial data, formatted according to the CDISC SDTM standard, into the OMOP framework.
The OHDSI CDM Survey Sub-Working Group develops standardized frameworks, vocabulary, and tools to enable consistent, interoperable representation of survey and assessment instrument data in the OMOP CDM — reducing duplication of effort and accelerating patient-reported and standardized assessment data in observational health research.
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The CDM workgroup exists to maintain and improve the use of the OMOP Common Data Model to make it the premier observational health data model in the world. We ensure the integrity and usability of the OMOP CDM in relation to other working groups by providing guidance on data standardization best practices.
To understand how dentistry can leverage observational research to improve oral health outcomes and further investigate the links between oral health and systemic disease.
To create an inviting venue for OHDSI community members early in their careers or engagement with OHDSI to navigate its resources, ask questions, present their research, find mentorship through networking, and seek insight on their career trajectories.
education:  To educate our field on how to generate clinical evidence with open science and reproducible research methods. We will empower the global health data community by establishing structured learning pathways, publishing foundational open-source knowledge like the Book of OHDSI, and maintaining a searchable, sustainable repository of educational artifacts. We foster collaboration across academia and industry by building a higher-education teaching consortium and providing a public catalog of global educational offerings to expand access to the field of observational health data science.
The Electronic Animal Record Workgroup exists to adopt the OMOP Common Data Model for electronic animal records to facilitate:
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The purpose of the Eye Care and Vision Research Workgroup is to advance the development and implementation of data standards in ophthalmology, optometry, and the vision sciences, and to support studies using observational ophthalmic data for generating insights to improve health and vision outcomes.
To advance healthcare research and improve patient outcomes through the innovative application of generative AI and foundational models.
The goal of the OHDSI GIS WG is to enable studies of place-related data in conjunction with longitudinal patient-level data.
Enable the OHDSI community to perform observational research following OHDSI best practices for characterization, population-level estimation, and patient-level prediction by providing a cohesive set of open-source analytic software.
Support healthcare systems on their OHDSI journey.
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The medical imaging working group turns pixels into phenotypes. Medical imaging segmentation can generate incredibly rich features that describe anatomical and physiological response to disease and to treatment. The imaging working group has developed the medical imaging extension to OMOP including the semantic integration of the DICOM medical imaging vocabulary standard. Our goal is to create evidence networks that incorporate medical images into their observational research for evidence generation from real world data that can readily be translated into clinical practice.
Empower real-world evidence generation through collaborative innovation in statistical and computational methods.
Promote the use of textual data in electronic health records (EHRs) for observational studies under the OHDSI umbrella
The OMOP Oncology workgroup aims to provide a foundation for representing cancer data at the levels of granularity and abstraction required to support observational cancer research.
Discuss and perform research into best practices for developing prediction models using observational healthcare data. We also maintain multiple R packages that enable prediction model development for data in the OMOP CDM format.
Our purpose is to develop tools and standards for pregnancy and reproductive health research to foster collaborative studies within the OHDSI network, and advance research in this field generally.
To improve the quality and reliability of the evidence generated from observational data, by advancing the science of phenotype development and evaluation
Expand the vocabulary for representing neuropsychiatric scales and scores to enable OHDSI studies that capture the detailed phenomology of neurologic, psychiatric and neuropsychological conditions.
Use data representing the phenomena in OHDSI studies that improves understanding and care for patients with these conditions. Provide feedback and assistance to community members who want to conduct OHDSI studies on populations with these conditions.
The goal of the OHDSI Rare Disease Working Group is to advance the understanding and treatment of rare diseases by leveraging real-world data, uniting multidisciplinary experts, and developing innovative methodologies to improve patient outcomes and inform clinical decision-making.
Promote better rehabilitation care by leveraging the OHDSI collaborative to enable large scale observational rehabilitation research
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To collaboratively generate the observational health evidence needed to better understand the role of surgical and perioperative interventions in medical care.
The goal of Themis is to provide conventions on how source data should be standardized to the OMOP CDM to support the OHDSI community to generate the evidence that promotes better health decisions and better care. When there is ambiguity on how data should be inserted into the CDM, Themis will examine the issue, create a convention and document it.
We follow the FAIR principles: Findable, Accessible, Interoperable and Reusable. FAIRR – reproducible
Themis makes decisions for the good of the whole community. We must compromise. We can always revisit and modify the convention. Don’t let perfect be the enemy of great. And interoperability between different OMOP CDMs is great!
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The goal of the Transplant WG is to enable observational research in solid organ transplant.
Develop a standard vocabulary for vaccines that supports the study of vaccine uptake, vaccine effectiveness, and safety outcomes within a global network of observational health databases.
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