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--- documentation:vocabulary:background [2016/06/18 16:08] – cgreich
+++ documentation:vocabulary:background [2024/09/03 07:41] (current) – jcruz
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 ===== Background and Motivation =====
-The availability of very large-scale healthcare databases in electronic form has opened the possibility to generate systematic and large-scale evidence and insights about the application of healthcare to patients. This discipline is called Observational Outcome Research, and it uses longitudinal patient level clinical data in order to describe and understand the pathogenesis of disease and the effect of other clinical events as well as treatment interventions on the progression of the disease. This research constitutes secondary use of the data, which is being collected usually for purposes other than research: administrative data such as insurance reimbursement claims and Electronic Health or Medical Record (EHR, EMR).
+Thank you for visiting the wiki! This page has moved to https://github.com/OHDSI/Vocabulary-v5.0/wiki/Introduction#background-and-motivation.
-Because of the collection purpose for primary use, the format and representation of the data follows that primary use. It also introduces artifacts and bias into the data. In addition, all source datasets differ from each other in format and content representation. Since healthcare systems differ between countries, the problem becomes even harder for research carried out internationally. All this makes robust, reproducible and automated research a significant challenge.
-The solution is the standardization of the data and a standardization of the representation. This allows methods and tools to operate on data of disparate origin, freeing the analyst from having to dissect the idiosyncrasies of a particular dataset and manipulating the data to make it fit for research. It also allows to develop analytical methods on one dataset, and applying it an any other dataset in CDM format.
-The OMOP CDM and Standardized Vocabularies provide such a framework for systematic research. It consists of the following components and mechanisms:
-  - **Standardization:** Multiple Vocabularies used in observational data consolidated into a common format. This relieves the researchers from having to understand and handle multiple different formats and life cycle conventions of the Vocabularies.
-  - **Unique Standard Concepts**: For each Clinical Entity, there is only one Concepts representing it, called the Standard Concept. Other equivalent or similar Concepts are designated non-Standard and mapped to the Standard ones.
-  - **Domains:** Each Concept is assigned a Domain, and it is done so correctly. This also defines in which CDM table a clinical entity should be placed into or looked up in at query time.
-  - **Comprehensive coverage:** All meaningful entities in a Domain have a Standard Concept. This ensures that any activity or observation relevant to a patient can be represented in the data.
-  - **Hierarchy:** Within a Domain all Concepts are organized in a hierarchical structure. This allows to researcher to query for all Concepts (e.g. drug products) that are hierarchically subsumed under a higher level Concept (e.g. a drug class).
-  - **Relationships** between Concepts within and acros Vocabularies and **Mappings** from non-Standard to Standard Concepts.
-It is important to note that these critera are followed strictly for the purpose of observational research. In that regard the Standardized Vocabularies differ from large collections with equivalence mapping of concepts such as the [[http://www.nlm.nih.gov/research/umls|UMLS]], which supports indexing and searching the entire biomedical literature. UMLS resources have been used heavily as a basis for constructing many of the Standardized Vocabulary components, but significant additional efforts have been made to the make the framework for for purpose:
-  * Additional Vocabularies were created, mostly for metadata purposes.
-  * Mappings and relationships were added to achieve comprehensive coverage. If equivalence cannot be achieved, "uphill" relationships from more granular non-standard to higher level Standard Concepts are created.
-  * A comprehensive domain structure was established and each Concepts was assigned a Domain (or combination of Domains).
-  * A hierarchical tree within Domains was built representing classifications used in medical science and clinical practice.
-However, significant work needs to be done to achieve all the criteria in all of the Domains. Currently, we can achieve the following compliance for the most complex ones:
-^Domain^Standardization^Unique Concepts^Reliable Domains^Comprehensive Coverage^Hierarchy^Mapping^
-|Drug|  x  |  x  |  x  |In US. Other countries in process|  x  |  x  |
-|Condition|  x  |  x  |  x  |  x  |  x  |  x  |
-|Procedure|  x  |Heavy overlapping|  x  |  x  | |
-|Measurement|  x  |  somewhat  |  mostly  |  x  |  minimal  | |
-|Device| | |  mostly  | | | |
-|Unit|  x  |  x  |  x  |  x  | | |