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<a class="navbar-brand" href="index.html"><div><img src="ohdsi16x16.png"></img> OMOP Common Data Model </div></a>
</div>
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<a href="index.html">
<span class="fa fa-house"></span>
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<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
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Background
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<li>
<a href="background.html">Model Background</a>
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<a href="cdmRefreshProcess.html">CDM Refresh Process</a>
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<a href="vocabulary.html">How the Vocabulary is Built</a>
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Conventions
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<a href="dataModelConventions.html">General Conventions</a>
</li>
<li>
<a href="ehrObsPeriods.html">Observation Periods for EHR Data</a>
</li>
<li>
<a href="cdmPrivacy.html">Patient Privacy and OMOP</a>
</li>
<li>
<a href="customConcepts.html">Custom Concepts</a>
</li>
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<a href="cdm54Changes.html">Changes from CDM v5.3</a>
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<a href="cdm54erd.html">Entity Relationships</a>
</li>
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<a href="cdm54ToolingSupport.html">Detailed tooling support per CDM field</a>
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CDM Proposals
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<a href="https://github.com/OHDSI/CommonDataModel/issues/252">Region_concept_id</a>
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<div id="header">
<h1 class="title toc-ignore"><img src="ohdsi40x40.png"></img> OMOP CDM
Background</h1>
</div>
<div id="background" class="section level2">
<h2>Background</h2>
<p>The Observational Medical Outcomes Partnership (OMOP) was a
public-private partnership established to inform the appropriate use of
observational healthcare databases for studying the effects of medical
products. Over the course of the 5-year project and through its
community of researchers from industry, government, and academia, OMOP
successfully achieved its aims to:</p>
<ul>
<li>Conduct methodological research to empirically evaluate the
performance of various analytical methods on their ability to identify
true associations and avoid false findings</li>
<li>Develop tools and capabilities for transforming, characterizing, and
analysing disparate data sources across the health care delivery
spectrum</li>
<li>Establish a shared resource so that the broader research community
can collaboratively advance the science</li>
</ul>
<p>The results of OMOP’s research has been widely published and
presented at scientific conferences, including <a
href="https://www.ohdsi.org/2021-ohdsi-global-symposium-info/">annual
symposia</a>.</p>
<p>The OMOP Legacy continues…</p>
<p>The community is actively using the OMOP Common Data Model for their
various research purposes. Those tools will continue to be maintained
and supported, and information about this work is available in the <a
href="http://www.github.com/ohdsi">public domain</a>.</p>
<p>The OMOP Common Data Model will continue to be an open-source
community standard for observational healthcare data. The model
specifications and associated work products will be placed in the public
domain, and the entire research community is encouraged to use these
tools to support everybody’s own research activities.</p>
</div>
<div id="the-role-of-the-common-data-model" class="section level2">
<h2>The Role of the Common Data Model</h2>
<p>No single observational data source provides a comprehensive view of
the clinical data a patient accumulates while receiving healthcare, and
therefore none can be sufficient to meet all expected outcome analysis
needs. This explains the need for assessing and analyzing multiple data
sources concurrently using a common data standard. This standard is
provided by the OMOP Common Data Model (CDM).</p>
<p>The CDM is designed to support the conduct of research to identify
and evaluate associations between interventions (drug exposure,
procedures, healthcare policy changes etc.) and outcomes caused by these
interventions (condition occurrences, procedures, drug exposure etc.).
Outcomes can be efficacious (benefit) or adverse (safety risk). Often
times, specific patient cohorts (e.g., those taking a certain drug or
suffering from a certain disease) may be defined for treatments or
outcomes, using clinical events (diagnoses, observations, procedures,
etc.) that occur in predefined temporal relationships to each other. The
CDM, combined with its standardized content (via the Standardized
Vocabularies), will ensure that research methods can be systematically
applied to produce meaningfully comparable and reproducible results.</p>
</div>
<div id="design-principles" class="section level2">
<h2>Design Principles</h2>
<p>The CDM is designed to include all observational health data elements
(experiences of the patient receiving health care) that are relevant for
analytic use cases to support the generation of reliable scientific
evidence about disease natural history, healthcare delivery, effects of
medical interventions, the identification of demographic information,
health care interventions and outcomes.</p>
<p>Therefore, the CDM is designed to store observational data to allow
for research, under the following principles:</p>
<ul>
<li><strong>Suitability for purpose:</strong> The CDM aims to provide
data organized in a way optimal for analysis, rather than for the
purpose of addressing the operational needs of health care providers or
payers.</li>
<li><strong>Data protection:</strong> All data that might jeopardize the
identity and protection of patients, such as names, precise birthdays
etc. are limited. Exceptions are possible where the research expressly
requires more detailed information, such as precise birth dates for the
study of infants.</li>
<li><strong>Design of domains:</strong> The domains are modeled in a
person-centric relational data model, where for each record the identity
of the person and a date is captured as a minimum.</li>
<li><strong>Rationale for domains:</strong> Domains are identified and
separately defined in an entity-relationship model if they have an
analysis use case and the domain has specific attributes that are not
otherwise applicable. All other data can be preserved as an observation
in an entity-attribute-value structure.</li>
<li><strong>Standardized Vocabularies:</strong> To standardize the
content of those records, the CDM relies on the Standardized
Vocabularies containing all necessary and appropriate corresponding
standard healthcare concepts.</li>
<li><strong>Reuse of existing vocabularies:</strong> If possible, these
concepts are leveraged from national or industry standardization or
vocabulary definition organizations or initiatives, such as the National
Library of Medicine, the Department of Veterans’ Affairs, the Center of
Disease Control and Prevention, etc.</li>
<li><strong>Maintaining source codes:</strong> Even though all codes are
mapped to the Standardized Vocabularies, the model also stores the
original source code to ensure no information is lost.</li>
<li><strong>Technology neutrality:</strong> The CDM does not require a
specific technology. It can be realized in any relational database, such
as Oracle, SQL Server etc., or as SAS analytical datasets.</li>
<li><strong>Scalability:</strong> The CDM is optimized for data
processing and computational analysis to accommodate data sources that
vary in size, including databases with up to hundreds of millions of
persons and billions of clinical observations.</li>
<li><strong>Backwards compatibility:</strong> All changes from previous
CDMs are clearly delineated in the <a
href="https://github.com/OHDSI/CommonDataModel">github repository</a>.
Older versions of the CDM can be easily created from the CDMv5, and no
information is lost that was present previously.</li>
</ul>
</div>
</div>
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