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Package: DDLGeneratr
Type: Package
Title: OMOP CDM DDL Generator
Version: 0.1.0
Author: Clair Blacketer
Maintainer: Clair Blacketer <mblacke@its.jnj.com>
Description: This packages uses the SqlRender package to convert a DDL script written in OHDSql to the other supported dialects.
License: Apache License 2.0
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
Imports:
SqlRender,
rmarkdown,
stringr,
readxl,
dplyr
NeedsCompilation: no
RoxygenNote: 6.1.1

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# Generated by roxygen2: do not edit by hand
export(downloadCurrentDdl)
export(parseWiki)
export(writeConstraints)
export(writeDDL)
export(writeIndex)
export(writePrimaryKeys)

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<a class="navbar-brand" href="index.html"><div><img src="ohdsi16x16.png"></img> OMOP Common Data Model </div></a>
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<p>##TODO for CDM # File bug reports</p>
<p>If you’ve found a bug you can help out by filing a bug report. Each package has an issue tracker where you can create a new issue. Try and be as specific as possible. What error message occurs? Can you provide a simple example to reproduce the problem? What versions of the various packages are you running?</p>
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<h1>Write help files</h1>
<p>If you’re a bit more experienced with the Methods Library and are looking to improve your open source development skills, the next step up is to contribute a pull request to a Methods Library package. The most important thing to know is that Methods Library packages use <a href="https://github.com/klutometis/roxygen">roxygen2</a>: this means that documentation is found in the R code close to the source of each function.</p>
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<p>An important part of validating the methods in the library is unit testing. A unit test is a small program that tests a specific function in the library. For example, there are <a href="https://github.com/OHDSI/CohortMethod/blob/master/tests/testthat/test-psFunctions.R#L5">several unit tests</a> that make sure the propensity score matching works correctly. We can never have enough unit tests, and writing unit tests is an excellent way to learn how the Methods Library packages work under the hood.</p>
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<p>We are always looking for new methods and packages to add to the Methods Libary. However, in order to qualify for inclusion a package must meet the following requirements:</p>
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<h1 class="title toc-ignore">Data Model Conventions</h1>
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<p>There are a number of implicit and explicit conventions that have been adopted in the CDM. Developers of methods that run against the CDM need to understand these conventions.</p>
<div id="general-conventions-of-schemas" class="section level2">
<h2>General conventions of schemas</h2>
<p>New to CDM v6.0 is the concept of schemas. This allows for more separation between read-only and writeable tables. The clinical data, event, and vocabulary tables are in the ‘CDM’ schema and tables that need to be manipulated by web-based tools or end users have moved to the ‘Results’ schema. Currently the only two tables in the ‘Results’ schema are COHORT and COHORT_DEFINITON, though likely more will be added over the course of v6.0 point releases.</p>
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<div id="general-conventions-of-data-tables" class="section level2">
<h2>General conventions of data tables</h2>
<p>The CDM is platform-independent. Data types are defined generically using ANSI SQL data types (VARCHAR, INTEGER, FLOAT, DATE, DATETIME, CLOB). Precision is provided only for VARCHAR. It reflects the minimal required string length and can be expanded within a CDM instantiation. The CDM does not prescribe the date and datetime format. Standard queries against CDM may vary for local instantiations and date/datetime configurations.</p>
<p>In most cases, the first field in each table ends in ’_ID’, containing a record identifier that can be used as a foreign key in another table.</p>
</div>
<div id="general-conventions-of-fields" class="section level2">
<h2>General conventions of fields</h2>
<p>Variable names across all tables follow one convention:</p>
<table>
<colgroup>
<col width="29%" />
<col width="70%" />
</colgroup>
<thead>
<tr class="header">
<th>Notation</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><entity>_SOURCE_VALUE</td>
<td>Verbatim information from the source data, typically used in ETL to map to CONCEPT_ID, and not to be used by any standard analytics. For example, CONDITION_SOURCE_VALUE = ‘787.02’ was the ICD-9 code captured as a diagnosis from the administrative claim.</td>
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<td><entity>_ID</td>
<td>Unique identifiers for key entities, which can serve as foreign keys to establish relationships across entities. For example, PERSON_ID uniquely identifies each individual. VISIT_OCCURRENCE_ID uniquely identifies a PERSON encounter at a point of care.</td>
</tr>
<tr class="odd">
<td><entity>_CONCEPT_ID</td>
<td>Foreign key into the Standardized Vocabularies (i.e. the standard_concept attribute for the corresponding term is true), which serves as the primary basis for all standardized analytics. For example, CONDITION_CONCEPT_ID = <a href="http://athena.ohdsi.org/search-terms/terms/31967">31967</a> contains the reference value for the SNOMED concept of ‘Nausea’</td>
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<td><entity>_SOURCE_CONCEPT_ID</td>
<td>Foreign key into the Standardized Vocabularies representing the concept and terminology used in the source data, when applicable. For example, CONDITION_SOURCE_CONCEPT_ID = <a href="http://athena.ohdsi.org/search-terms/terms/45431665">45431665</a> denotes the concept of ‘Nausea’ in the Read terminology; the analogous CONDITION_CONCEPT_ID might be 31967, since SNOMED-CT is the Standardized Vocabulary for most clinical diagnoses and findings.</td>
</tr>
<tr class="odd">
<td><entity>_TYPE_CONCEPT_ID</td>
<td>Delineates the origin of the source information, standardized within the Standardized Vocabularies. For example, DRUG_TYPE_CONCEPT_ID can allow analysts to discriminate between ‘Pharmacy dispensing’ and ‘Prescription written’</td>
</tr>
</tbody>
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<div id="representation-of-content-through-concepts" class="section level2">
<h2>Representation of content through Concepts</h2>
<p>In CDM data tables the meaning of the content of each record is represented using Concepts. Concepts are stored with their CONCEPT_ID as foreign keys to the CONCEPT table in the Standardized Vocabularies, which contains Concepts necessary to describe the healthcare experience of a patient. If a Standard Concept does not exist or cannot be identified, the Concept with the CONCEPT_ID 0 is used, representing a non-existing or un-mappable concept.</p>
<p>Records in the CONCEPT table contain all the detailed information about it (name, domain, class etc.). Concepts, Concept Relationships and other information relating to Concepts is contained in the tables of the Standardized Vocabularies.</p>
</div>
<div id="difference-between-concept-ids-and-source-values" class="section level2">
<h2>Difference between Concept IDs and Source Values</h2>
<p>Many tables contain equivalent information multiple times: As a Source Value, a Source Concept and as a Standard Concept.</p>
<ul>
<li>Source Values contain the codes from public code systems such as ICD-9-CM, NDC, CPT-4 etc. or locally controlled vocabularies (such as F for female and M for male) copied from the source data. Source Values are stored in the *_SOURCE_VALUE fields in the data tables.</li>
<li>Concepts are CDM-specific entities that represent the meaning of a clinical fact. Most concepts are based on code systems used in healthcare (called Source Concepts), while others were created de-novo (CONCEPT_CODE = ‘OMOP generated’). Concepts have unique IDs across all domains.</li>
<li>Source Concepts are the concepts that represent the code used in the source. Source Concepts are only used for common healthcare code systems, not for OMOP-generated Concepts. Source Concepts are stored in the *_SOURCE_CONCEPT_ID field in the data tables.</li>
<li>Standard Concepts are those concepts that are used to define the unique meaning of a clinical entity. For each entity there is one Standard Concept. Standard Concepts are typically drawn from existing public vocabulary sources. Concepts that have the equivalent meaning to a Standard Concept are mapped to the Standard Concept. Standard Concepts are referred to in the <entity>_CONCEPT_ID field of the data tables.</li>
</ul>
<p>Source Values are only provided for convenience and quality assurance (QA) purposes. Source Values and Source Concepts are optional, while Standard Concepts are mandatory. Source Values may contain information that is only meaningful in the context of a specific data source.</p>
</div>
<div id="difference-between-general-concepts-and-type-concepts" class="section level2">
<h2>Difference between general Concepts and Type Concepts</h2>
<p>Type Concepts (ending in _TYPE_CONCEPT_ID) and general Concepts (ending in _CONCEPT_ID) are part of many tables. The former are special Concepts with the purpose of indicating where the data are derived from in the source. For example, the Type Concept field can be used to distinguish a DRUG_EXPOSURE record that is derived from a pharmacy-dispensing claim from one indicative of a prescription written in an electronic health record (EHR).</p>
</div>
<div id="time-span-of-available-data" class="section level2">
<h2>Time span of available data</h2>
<p>Data tables for clinical data contain a datetime stamp (ending in _DATETIME, _START_DATETIME or _END_DATETIME), indicating when that clinical event occurred. As a rule, no record can be outside of a valid OBSERVATION_PERIOD time period. Clinical information that relates to events that happened prior to the first OBSERVATION_PERIOD will be captured as a record in the OBSERVATION table as ‘Medical history’ (CONCEPT_ID = 43054928), with the OBSERVATION_DATETIME set to the first OBSERVATION_PERIOD_START_DATE of that patient, and the VALUE_AS_CONCEPT_ID set to the corresponding CONCEPT_ID for the condition/drug/procedure that occurred in the past. No data occurring after the last OBSERVATION_PERIOD_END_DATE can be valid records in the CDM. * When mapping source data to the CDM, if time is unknown the default time of 00:00:00 should be chosen. If a time of 00:00:00 is given in the source data it should be shifted to 00:00:01 (<a href="https://github.com/OHDSI/Themis/issues/10">THEMIS issue #10</a>).</p>
</div>
<div id="content-of-each-table" class="section level2">
<h2>Content of each table</h2>
<p>For the tables of the main domains of the CDM it is imperative that concepts used are strictly limited to the domain. For example, the CONDITION_OCCURRENCE table contains only information about conditions (diagnoses, signs, symptoms), but no information about procedures. Not all source coding schemes adhere to such rules. For example, ICD-9-CM codes, which contain mostly diagnoses of human disease, also contain information about the status of patients having received a procedure. The ICD-9-CM code V20.3 ‘Newborn health supervision’ defines a continuous procedure and is therefore stored in the PROCEDURE_OCCURRENCE table.</p>
</div>
<div id="differentiating-between-source-values-source-concept-ids-and-standard-concept-ids" class="section level2">
<h2>Differentiating between Source Values, Source Concept Ids, and Standard Concept Ids</h2>
<p>Each table contains fields for Source Values, Source Concept Ids, and Standard Concept Ids.</p>
<ul>
<li>Source Values are fields to maintain the verbatim information from the source database, stored as unstructured text, and are generally not to be used by any standardized analytics. There is no standardization for these fields and these columns can be used as the local CDM builders see fit. A typical example would be an ICD-9 code without the decimal from an administrative claim as condition_source_value = ‘78702’ which is how it appeared in the source (<a href="https://github.com/OHDSI/Themis/issues/15">THEMIS issue #15</a>).</li>
<li>Source Concept Ids provide a repeatable representation of the source concept, when the source data are drawn from a commonly-used, internationally-recognized vocabulary that has been distributed with the OMOP Common Data Model. Specific use cases where source vocabulary-specific analytics are required can be accommodated by the use of the *_SOURCE_CONCEPT_ID fields, but these are generally not applicable across disparate data sources. The standard *_CONCEPT_ID fields are <strong>strongly suggested</strong> to be used in all standardized analytics, as specific vocabularies have been established within each data domain to facilitate standardization of both structure and content within the OMOP Common Data Model.</li>
</ul>
<p>The following provide conventions for processing source data using these three fields in each domain:</p>
<p>When processing data where the source value is either free text or a reference to a coding scheme that is not contained within the Standardized Vocabularies:</p>
<ul>
<li>Map all Source Values to the corresponding Standard CONCEPT_IDs. Store the CONCEPT_IDs in the TARGET_CONCEPT_ID field of the SOURCE_TO_CONCEPT_MAP table.
<ul>
<li>If a CONCEPT_ID is not available for the source code, the TARGET_CONCEPT_ID field is set to 0.</li>
</ul></li>
</ul>
<p>When processing your data where Source Value is a reference to a coding scheme contained within the Standardized Vocabularies:</p>
<ul>
<li>Find all CONCEPT_IDs in the Source Vocabulary that correspond to your Source Values. Store the result in the SOURCE_CONCEPT_ID field.
<ul>
<li>If the source code follows the same formatting as the distributed vocabulary, the mapping can be directly obtained from the CONCEPT table using the CONCEPT_CODE field.</li>
<li>If the source code uses alternative formatting (ex. format has removed decimal point from ICD-9 codes), you will need to perform the formatting transformation within the ETL. In this case, you may wish to store the mappings from original codes to SOURCE_CONCEPT_IDs in the SOURCE_TO_CONCEPT_MAP table.</li>
<li>If the source code is not found in a vocabulary, the SOURCE_CONCEPT_ID field is set to 0</li>
</ul></li>
<li>Use the CONCEPT_RELATIONSHIP table to identify the Standard CONCEPT_ID that corresponds to the SOURCE_CONCEPT_ID in the domain.
<ul>
<li>Each SOURCE_CONCEPT_ID can have 1 or more Standard CONCEPT_IDs mapped to it. Each Standard CONCEPT_ID belongs to only one primary domain but when a source CONCEPT_ID maps to multiple Standard CONCEPT_IDs, it is possible for that SOURCE_CONCEPT_ID to result in records being produced across multiple domains. For example, ICD-10-CM code Z34.00 ‘Encounter for supervision of normal first pregnancy, unspecified trimester’ will be mapped to the SNOMED concept ‘Routine antenatal care’ in the procedure domain and the concept in the condition domain ‘Primagravida’. It is also possible for one SOURCE_CONCEPT_ID to map to multiple Standard CONCEPT_IDs within the same domain. For example, ICD-9-CM code 070.43 ‘Hepatitis E with hepatic coma’ maps to the SNOMED concept for ‘Acute hepatitis E’ and a second SNOMED concept for ‘Hepatic coma’, in which case multiple CONDITION_OCCURRENCE records will be generated for the one source value record.</li>
<li>If the SOURCE_CONCEPT_ID is not mappable to any Standard CONCEPT_ID, the <entity>_CONCEPT_ID field is set to 0.</li>
</ul></li>
<li>Write the data record into the table(s) corresponding to the domain of the Standard CONCEPT_ID(s).
<ul>
<li>If the Source Value has a SOURCE_CONCEPT_ID but the SOURCE_CONCEPT_ID is not mapped to a Standard CONCEPT_ID, then the domain for the data record, and hence it’s table location, is determined by the DOMAIN_ID field of the CONCEPT record the SOURCE_CONCEPT_ID refers to. The Standard <entity>_CONCEPT_ID is set to 0.</li>
<li>If the Source Value cannot be mapped to a SOURCE_CONCEPT_ID or Standard CONCEPT_ID, then direct the data record to the most appropriate CDM domain based on your local knowledge of the intent of the source data and associated value. For example, if the un-mappable Source Value came from a ‘diagnosis’ table then, in the absence of other information, you may choose to record that fact in the CONDITION_OCCURRENCE table.</li>
</ul></li>
</ul>
<p>Each Standard CONCEPT_ID field has a set of allowable CONCEPT_ID values. The allowable values are defined by the domain of the concepts. For example, there is a domain concept of ‘Gender’, for which there are only two allowable standard concepts of practical use (8507 - ‘Male’, 8532- ‘Female’) and one allowable generic concept to represent a standard notion of ‘no information’ (concept_id = 0). This ‘no information’ concept should be used when there is no mapping to a standard concept available or if there is no information available for that field. The exceptions are MEASUREMENT.VALUE_AS_CONCEPT_ID, OBSERVATION.VALUE_AS_CONCEPT_ID, MEASUREMENT.UNIT_CONCEPT_ID, OBSERVATION.UNIT_CONCEPT_ID, MEASUREMENT.OPERATOR_CONCEPT_ID, and OBSERVATION.MODIFIER_CONCEPT_ID, which can be NULL if the data do not contain the information (<a href="https://github.com/OHDSI/Themis/issues/11">THEMIS issue #11</a>).</p>
<p>There is no constraint on allowed CONCEPT_IDs within the SOURCE_CONCEPT_ID fields.</p>
</div>
<div id="custom-source_to_concept_maps" class="section level2">
<h2>Custom SOURCE_TO_CONCEPT_MAPs</h2>
<p>When the source data uses coding systems that are not currently in the Standardized Vocabularies (e.g. ICPC codes for diagnoses), the convention is to store the mapping of such source codes to Standard Concepts in the SOURCE_TO_CONCEPT_MAP table. The codes used in the data source can be recorded in the SOURCE_VALUE fields, but no SOURCE_CONCEPT_ID will be available.</p>
<p>Custom source codes are not allowed to map to Standard Concepts that are marked as invalid.</p>
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<h1 class="title toc-ignore">OMOP CDM Frequently Asked Questions</h1>
</div>
<p><strong>1. I understand that the common data model (CDM) is a way of organizing disparate data sources into the same relational database design, but how can it be effective since many databases use different coding schemes?</strong></p>
<p>During the extract, transform, load (ETL) process of converting a data source into the OMOP common data model, we standardize the structure (e.g. tables, fields, data types), conventions (e.g. rules that govern how source data should be represented), and content (e.g. what common vocabularies are used to speak the same language across clinical domains). The common data model preserves all source data, including the original source vocabulary codes, but adds the standardized vocabularies to allow for network research across the entire OHDSI research community.</p>
<p><strong>2. How does my data get transformed into the common data model?</strong></p>
<p>You or someone in your organization will need to create a process to build your CDM. Don’t worry though, you are not alone! The open nature of the community means that much of the code that other participants have written to transform their own data is available for you to use. If you have a data license for a large administrative claims database like IBM MarketScan® or Optum’s Clinformatics® Extended Data Mart, chances are that someone has already done the legwork. Here is one example of a full builder freely available on <a href="https://github.com/OHDSI/ETL-CDMBuilder">github</a> that has been written for a variety of data sources.</p>
<p>The <a href="http://forums.ohdsi.org/">community forums</a> are also a great place to ask questions if you are stuck or need guidance on how to represent your data in the common data model. Members are usually very responsive!</p>
<p><strong>3. Are any tables or fields optional?</strong></p>
<p>It is expected that all tables will be present in a CDM though it is not a requirement that they are all populated. The two mandatory tables are:</p>
<ul>
<li><a href="https://github.com/OHDSI/CommonDataModel/wiki/person">Person</a>: Contains records that uniquely identify each patient in the source data who is at-risk to have clinical observations recorded within the source systems.</li>
<li><a href="https://github.com/OHDSI/CommonDataModel/wiki/observation_period">Observation_period</a>: Contains records which uniquely define the spans of time for which a Person is at-risk to have clinical events recorded within the source systems.</li>
</ul>
<p>It is then up to you which tables to populate, though the core event tables are generally agreed upon to be <a href="https://github.com/OHDSI/CommonDataModel/wiki/CONDITION_OCCURRENCE">Condition_occurrence</a>, <a href="https://github.com/OHDSI/CommonDataModel/wiki/PROCEDURE_OCCURRENCE">Procedure_occurrence</a>, <a href="https://github.com/OHDSI/CommonDataModel/wiki/DRUG_EXPOSURE">Drug_exposure</a>, <a href="https://github.com/OHDSI/CommonDataModel/wiki/MEASUREMENT">Measurement</a>, and <a href="https://github.com/OHDSI/CommonDataModel/wiki/OBSERVATION">Observation</a>. Each table has certain required fields, a full list of which can be found on the Common Data Model <a href="https://github.com/OHDSI/CommonDataModel/wiki/">wiki page</a>.</p>
<p><strong>4. Does the data model include any derived information? Which tables or values are derived?</strong></p>
<p>The common data model stores verbatim data from the source across various clinical domains, such as records for conditions, drugs, procedures, and measurements. In addition, to assist the analyst, the common data model also provides some derived tables, based on commonly used analytic procedures. For example, the <a href="https://github.com/OHDSI/CommonDataModel/wiki/CONDITION_ERA">Condition_era</a> table is derived from the <a href="https://github.com/OHDSI/CommonDataModel/wiki/CONDITION_OCCURENCE">Condition_occurrence</a> table and both the <a href="https://github.com/OHDSI/CommonDataModel/wiki/DRUG_ERA">Drug_era</a> and <a href="https://github.com/OHDSI/CommonDataModel/wiki/DOSE_ERA">Dose_era</a> tables are derived from the <a href="https://github.com/OHDSI/CommonDataModel/wiki/DRUG_EXPOSURE">Drug_exposure</a> table. An era is defined as a span of time when a patient is assumed to have a given condition or exposure to a particular active ingredient. Members of the community have written code to create these tables and it is out on the <a href="https://github.com/OHDSI/CommonDataModel/tree/master/CodeExcerpts/DerivedTables">github</a> if you choose to use it in your CDM build. It is important to reinforce, the analyst has the opportunity, but not the obligation, to use any of the derived tables and all of the source data is still available for direct use if the analysis calls for different assumptions.</p>
<p><strong>5. How is age captured in the model?</strong></p>
<p>Year_of_birth, month_of_birth, day_of_birth and birth_datetime are all fields in the Person table designed to capture some form of date of birth. While only year_of_birth is required, these fields allow for maximum flexibility over a wide range of data sources.</p>
<p><strong>6. How are gender, race, and ethnicity captured in the model? Are they coded using values a human reader can understand?</strong></p>
<p>Standard Concepts are used to denote all clinical entities throughout the OMOP common data model, including gender, race, and ethnicity. Source values are mapped to Standard Concepts during the extract, transform, load (ETL) process of converting a database to the OMOP Common Data Model. These are then stored in the Gender_concept_id, Race_concept_id and Ethnicity_concept_id fields in the Person table. Because the standard concepts span across all clinical domains, and in keeping with Cimino’s ‘Desiderata for Controlled Medical Vocabularies in the Twenty-First Century’, the identifiers are unique, persistent nonsematic identifiers. Gender, for example, is stored as either 8532 (female) or 8507 (male) in gender_concept_id while the original value from the source is stored in gender_source_value (M, male, F, etc)..</p>
<p>**7. Are there conditions/procedures/drugs or other domains that should be masked or hidden in the CDM?</p>
<p>The masking of information related to a person is dependent on the organization’s privacy policies and may vary by data asset (<a href="https://github.com/OHDSI/Themis/issues/21">THEMIS issue #21</a>).</p>
<p><strong>7. How is time-varying patient information such as location of residence addressed in the model?</strong></p>
<p>The OMOP common data model has been pragmatically defined based on the desired analytic use cases of the community, as well as the available types of data that community members have access to. Prior to CDM v6.0, each person record had associated demographic attributes which are assumed to be constant for the patient throughout the course of their periods of observation, like location and primary care provider. With the release of CDM v6.0, the Location_History table is now available to track the movements of people, care sites, and providers over time. Only the most recent location_id should be stored in the Person table to eliminate duplication, while the person’s movements are stored in Location_History.</p>
<p>Something like marital status is a little different as it is considered to be an observation rather than a demographic attribute. This means that it is housed in the Observation table rather than the Person table, giving the opportunity to store each change in status as a unique record.</p>
<p>If someone in the community had a use case for time-varying location of residence and also had source data that contains this information, we’d welcome participation in the CDM workgroup to evolve the model further.</p>
<p><strong>8. How does the model denote the time period during which a Person’s information is valid?</strong></p>
<p>The OMOP Common Data Model uses something called observation periods (stored in the <a href="https://github.com/OHDSI/CommonDataModel/wiki/observation_period">Observation_period</a> table) as a way to define the time span during which a patient is at-risk to have a clinical event recorded. In administrative claims databases, for example, these observation periods are often analogous to the notion of ‘enrollment’.</p>
<p><strong>9. How does the model capture start and stop dates for insurance coverage? What if a person’s coverage changes?</strong></p>
<p>The <a href="https://github.com/OHDSI/CommonDataModel/wiki/payer_plan_period">Payer_plan_period</a> table captures details of the period of time that a Person is continuously enrolled under a specific health Plan benefit structure from a given Payer. Payer plan periods, as opposed to observation periods, can overlap so as to denote the time when a Person is enrolled in multiple plans at the same time such as Medicare Part A and Medicare Part D.</p>
<p><strong>10. What if I have EHR data? How would I create observation periods?</strong></p>
<p>An observation period is considered as the time at which a patient is at-risk to have a clinical event recorded in the source system. Determining the appropriate observation period for each source data can vary, depending on what information the source contains. If a source does not provide information about a patient’s entry or exit from a system, then reasonable heuristics need to be developed and applied within the ETL.</p>
<div id="vocabulary-mapping" class="section level2">
<h2>Vocabulary Mapping</h2>
<p><strong>11. Do I have to map my source codes to Standard Concepts myself? Are there vocabulary mappings that already exist for me to leverage?</strong></p>
<p>If your data use any of the 55 source vocabularies that are currently supported, the mappings have been done for you. The full list is available from the open-source <a href="http://athena.ohdsi.org/search-terms/terms">ATHENA</a> tool under the download tab (see below). You can choose to download the ten <a href="https://github.com/OHDSI/CommonDataModel/wiki/Standardized-Vocabularies">vocabulary tables</a> from there as well – you will need a copy in your environment if you plan on building a CDM.</p>
<p><img src="images/Athena_download_box.png" /></p>
<p>The <a href="http://athena.ohdsi.org/search-terms/terms">ATHENA</a> tool also allows you to explore the vocabulary before downloading it if you are curious about the mappings or if you have a specific code in mind and would like to know which standard concept it is associated with; just click on the search tab and type in a keyword to begin searching.</p>
<p><strong>12. If I want to apply the mappings myself, can I do so? Are they transparent to all users?</strong></p>
<p>Yes, all mappings are available in the <a href="https://github.com/OHDSI/CommonDataModel/wiki/CONCEPT_RELATIONSHIP">Concept_relationship</a> table (which can be downloaded from <a href="http://athena.ohdsi.org/search-terms/terms">ATHENA</a>). Each value in a supported source terminology is assigned a Concept_id (which is considered non-standard). Each Source_concept_id will have a mapping to a Standard_concept_id. For example:</p>
<p><img src="images/Sepsis_to_SNOMED.png" /></p>
<p>In this case the standard SNOMED concept 201826 for type 2 diabetes mellitus would be stored in the Condition_occurrence table as the Condition_concept_id and the ICD10CM concept 1567956 for type 2 diabetes mellitus would be stored as the Condition_source_concept_id.</p>
<p><strong>13. Can RXNorm codes be stored in the model? Can I store multiple levels if I so choose? What if one collaborator uses a different level of RXNorm than I use when transforming their database?</strong></p>
<p>In the OMOP Common Data Model RXNorm is considered the standard vocabulary for representing drug exposures. One of the great things about the Standardized Vocabulary is that the hierarchical nature of RXNorm is preserved to enable efficient querying. It is agreed upon best practice to store the lowest level RXNorm available and then use the Vocabulary to explore any pertinent relationships. Drug ingredients are the highest-level ancestors so a query for the descendants of an ingredient should turn up all drug products (Clinical Drug or Branded Drug) containing that ingredient. A query designed in this way will find drugs of interest in any CDM regardless of the level of RXNorm used.</p>
<p><strong>14. What if the vocabulary has a mapping I don’t agree with? Can it be changed?</strong></p>
<p>Yes, that is the beauty of the community! If you find a mapping in the vocabulary that doesn’t seem to belong or that you think could be better, feel free to write a note on the <a href="https://forums.ohdsi.org/">forums</a> or on the <a href="https://github.com/OHDSI/Vocabulary-v5.0/issues">vocabulary github</a>. If the community agrees with your assessment it will be addressed in the next vocabulary version.</p>
<p><strong>15. What if I have source codes that are specific to my site? How would these be mapped?</strong></p>
<p>In the OMOP Vocabulary there is an empty table called the Source_to_concept_map. It is a simple table structure that allows you to establish mapping(s) for each source code with a standard concept in the OMOP Vocabulary (TARGET_CONCEPT_ID). This work can be facilitated by the OHDSI tool <a href="https://github.com/OHDSI/Usagi">Usagi</a> (pictured below) which searches for text similarity between your source code descriptions and the OMOP Vocabulary and exports mappings in a SOURCE_TO_CONCEPT_MAP table structure. Example Source_to_concept_map files can be found <a href="https://github.com/OHDSI/ETL-CDMBuilder/tree/master/man/VOCABULARY_ADDITIONS">here</a>. These generated Source_to_concept_map files are then loaded into the OMOP Vocabulary’s empty Source_to_concept_map prior to processing the native data into the CDM so that the CDM builder can use them in a build.</p>
<p><img src="images/Usagi.png" /></p>
<p>If an source code is not supported by the OMOP Vocabulary, one can create a new records in the CONCEPT table, however the CONCEPT_IDs should start &gt;2000000000 so that it is easy to tell between the OMOP Vocabulary concepts and the site specific concepts. Once those concepts exist CONCEPT_RELATIONSHIPS can be generated to assign them to a standard terminologies, USAGI can facilitate this process as well (<a href="https://github.com/OHDSI/Themis/issues/22">THEMIS issue #22</a>).</p>
<p><strong>16. How are one-to-many mappings applied?</strong></p>
<p>If one source code maps to two Standard Concepts then two rows are stored in the corresponding clinical event table.</p>
<p><strong>17. What if I want to keep my original data as well as the mapped values? Is there a way for me to do that?</strong></p>
<p>Yes! Source values and Source Concepts are fully maintained within the OMOP Common Data Model. A Source Concept represents the code in the source data. Each Source Concept is mapped to one or more Standard Concepts during the ETL process and both are stored in the corresponding clinical event table. If no mapping is available, the Standard Concept with the concept_id = 0 is written into the *_concept_id field (Condition_concept_id, Procedure_concept_id, etc.) so as to preserve the record from the native data.</p>
</div>
<div id="common-data-model-versioning" class="section level2">
<h2>Common Data Model Versioning</h2>
<p><strong>18. Who decides when and how to change the data model?</strong></p>
<p>The community! There is a <a href="https://docs.google.com/document/d/144e_fc7dyuinfJfbYW5MsJeSijVSzsNE7GMY6KRX10g/edit?usp=sharing">working group</a> designed around updating the model and everything is done by consensus. Members submit proposed changes to the <a href="https://github.com/OHDSI/CommonDataModel">github</a> in the form of <a href="https://github.com/OHDSI/CommonDataModel/issues">issues</a> and the group meets once a month to discuss and vote on the changes. Any ratified proposals are then added to the queue for a future version of the Common Data Model.</p>
<p><strong>19. Are changes to the model backwards compatible?</strong></p>
<p>Generally point version changes (5.1 -&gt; 5.2) are backwards compatible and major version changes (4.0 -&gt; 5.0) may not be. All updates to the model are listed in the release notes for each version and anything that could potentially affect backwards compatibility is clearly labeled.</p>
<p><strong>20. How frequently does the model change?</strong></p>
<p>The current schedule is for major versions to be released every year and point versions to be release every quarter though that is subject to the needs of the community.</p>
<p><strong>21. What is the dissemination plan for changes?</strong></p>
<p>Changes are first listed in the release notes on the <a href="https://github.com/OHDSI/CommonDataModel/">github</a> and in the <a href="https://github.com/OHDSI/CommonDataModel/wiki">common data model wiki</a>. New versions are also announced on the weekly community calls and on the <a href="https://forums.ohdsi.org">community forums</a>.</p>
</div>
<div id="ohdsi-tools" class="section level2">
<h2>OHDSI Tools</h2>
<p><strong>22. What are the currently available analytic tools?</strong></p>
<p>While there are a variety of tools freely available from the community, these are the most widely used:</p>
<ul>
<li><a href="http://www.github.com/ohdsi/achilles">ACHILLES</a> – a stand-alone tool for database characterization</li>
<li><a href="http://www.ohdsi.org/web/atlas/#/home">ATLAS</a> - an integrated platform for vocabulary exploration, cohort definition, case review, clinical characterization, incidence estimation, population-level effect estimation design, and patient-level prediction design (<a href="http://www.github.com/ohdsi/atlas">link to github</a>)</li>
<li><a href="https://github.com/OHDSI/ArachneUI">ARACHNE</a> – a tool to facilitate distributed network analyses</li>
<li><a href="https://github.com/OHDSI/whiterabbit">WhiteRabbit</a> - an application that can be used to analyse the structure and contents of a database as preparation for designing an ETL</li>
<li><a href="https://github.com/OHDSI/whiterabbit">RabbitInAHat</a> - an application for interactive design of an ETL to the OMOP Common Data Model with the help of the the scan report generated by White Rabbit</li>
<li><a href="https://github.com/OHDSI/usagi">Usagi</a> - an application to help create mappings between coding systems and the Vocabulary standard concepts.</li>
</ul>
<p><strong>23. Who is responsible for updating the tools to account for data model changes, bugs, and errors?</strong></p>
<p>The community! All the tools are open source meaning that anyone can submit an issue they have found, offer suggestions, and write code to fix the problem.</p>
<p><strong>24. Do the current tools allow a user to define a treatment gap (persistence window) of any value when creating treatment episodes?</strong></p>
<p>Yes – the ATLAS tool allows you to specify a persistence window between drug exposures when defining a cohort (see image below).</p>
<p><img src="images/ATLAS_Persistence_Window.PNG" /></p>
<p><strong>25. Can the current tools identify medication use during pregnancy?</strong></p>
<p>Yes, you can identify pregnancy markers from various clinical domains, including conditions and procedures, for example ‘live birth’, and then define temporal logic to look for drug exposure records in some interval prior to the pregnancy end. In addition, members of the community have built an advanced logic to define pregnancy episodes with all pregnancy outcomes represented, which can be useful for this type of research.</p>
<p><strong>26. Do the current tools execute against the mapped values or source values?</strong></p>
<p>The tools can execute against both source and mapped values, though mapped values are strongly encouraged. Since one of the aims of OHDSI is to create a distributed data network across the world on which to run research studies, the use of source values fails to take advantage of the benefits of the Common Data Model.</p>
</div>
<div id="network-research-studies" class="section level2">
<h2>Network Research Studies</h2>
<p><strong>27. Who can generate requests?</strong></p>
<p>Anyone in the community! Any question that gains enough interest and participation can be a network research study.</p>
<p><strong>28. Who will develop the queries to distribute to the network?</strong></p>
<p>Typically a principal investigator leads the development of a protocol. The PI may also lead the development of the analysis procedure corresponding to the protocol. If the PI does not have the technical skills required to write the analysis procedure that implements the protocol, someone in the community can help them put it together.</p>
<p><strong>29. What language are the queries written in?</strong></p>
<p>Queries are written in R and SQL. The <a href="https://github.com/OHDSI/sqlrender">SqlRender</a> package can translate any query written in a templated SQL Server-like dialect to any of the supported RDBMS environments, including Postgresql, Oracle, Redshift, Parallel Data Warehouse, Hadoop Impala, Google BigQuery, and Netezza.</p>
<p><strong>30. How do the queries get to the data partners and how are they run once there?</strong></p>
<p>OHDSI runs as a distributed data network. All analyses are publicly available and can be downloaded to run at each site. The packages can be run locally and, at the data partner’s discretion, aggregate results can be shared with the study coordinator.</p>
<p>Data partners can also make use of one of OHDSI’s open-source tools called <a href="https://github.com/OHDSI/arachne">ARACHNE</a>, a tool to facilitate distributed network analytics against the OMOP CDM.</p>
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<h1 class="title toc-ignore"><div>
<img src="ohdsi40x40.png"></img> OMOP Common Data Model
</div></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/events/2019-ohdsi-symposium/">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>
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