Common Data Models for clinical data

In the previous chapter, we described a potential journey that creates a clinical dataset. The common data models play a huge role in storing and also exchanging data (see “Study conduct and data collection” in the previous chapter), thus we want to briefly dive into the most important standardisation systems that were already mentioned in the introduction:  

  • CDISC, which is essential for submitting clinical trials to regulatory authorities

  • OMOP as sophisticated approach to store observational data 

  • FHIR, which focuses more on data exchange. 


The Clinical Data Interchange Standards Consortium (CDISC) is a standard developing organisation (SDO) which started as a volunteer group in 1997 with the goal of improving medical research and related areas of healthcare by working on the interoperability of data, resulting in a number of standards. Some of the CDISC standards are mandatory for submission to clinical regulators. Furthermore, CDISC is also cooperating with Health Seven International ( ), abbreviated to HL7® . However, the long and rich history of CDISC may explain its complex landscape.  

CDISC Operational Data Model (ODM)

The CDISC Operational Data model (ODM) is a standard designed for the exchange and archiving of translational research data and its associated metadata as well as administrative data, reference data and audit information. The ODM standard is used these days in many more ways than originally intended. Usage varies from Electronic data capture to planning, data collection and data analysis and archival. The implementation of the operational model is XML based (ODM-XML), which also facilitates the data exchange between different foundational standards as I was extended after its invention in 1999 for different purposes. ODM-XML is vendor-neutral and platform-independent format.

Figure 5: The CDISC clinical foundational standards consist of 1) Model for Planning (PRM), 2) Model for Data Collection (CDASH), 3) Model for Tabulation of Study Data (SDTM) and 4) Analysis Data Model (ADaM). These foundational standards are components of the CDISC Operational Data Model (ODM). The data exchange is facilitated through five extended markup languages (XML) which are 1) Study Trial Design (SDM), 2) Operational Data Model (ODM), 3) Dataset Metadata (Define), 4) Dataset Data (Dataset) and 5) Clinical Trial Registry (CTR). Adapted from: and

CDISC Foundational standards

The major “Foundational standards” are the building blocks of CDISC, as illustrated in Figure XXX. They consist of protocols for the clinical research processes of planning (PRM - Protocol Representation Model), collecting data (CDASH), organising study data (SDTM) and analysis of data models (ADaM). In addition, there is a standard for tabulation of animal (non or pre-clinical) studies (SEND). In the following paragraphs we briefly describe the foundational standards, before we discuss the CDISC’s data exchange mechanism and the other important CDISC building blocks – the controlled terminology and so-called therapeutic area-specific extensions.


The Protocol Representation Model (PRM) is a way to “translate” the human readable research protocol into machine readable representation. Thus it focuses on study characteristics such as study design or eligibility criteria and notably also information that is necessary for submission to Clinical Trial Registries. PRM is aligned with the Biomedical Research Integrated Domain Group (BRIDG) Model (see section “Shared model of clinical study design and outcome”). 


Clinical Data Acquisition Standards Harmonisation (CDASH) is focused on the collection of data across studies with a focus on traceability between data collection and data submission to the regulatory agencies. Using CDASH during clinical trial data collection supports the creation of SDTM data for submission later on in the clinical data process. Or in other words, while the use of CDASH is not mandatory, it makes sense to stick to CDISC defined data elements from the start because it allows easier conversion to SDTM. 


The Study Data Tabulation Model (SDTM) is a standard method for structuring data about collecting, managing, analysing and reporting clinical study results. The format is table based, where each row represents an observation for a uniquely identifiable clinical study subject, e.g. by patientIdentifier, date, time, etc. Besides this identifying information, the row also contains one data point associated with the observation. This data format leads to large files with potentially many blank columns, though it does remain internally consistent. The headers in SDTM are standardised as they have fixed (variable) names and overlap to a certain extent with CDASH.

Different “domains” are represented in SDTM, for example the demographics domain (DM) or the subject visits domain (SV). Usually the data for each domain are stored in different files - the sum of these files represents the whole dataset of the clinical study. 


The Analysis data model (ADaM) is a subset of derived data from a SDTM dataset for statistical and scientific analysis. For example, Age of the patient is a derived variable and is calculated from the Date of Birth and Date of Informed Consent. Some ADaM variables are also specific/relevant to certain Indications (e.g. such as Progression Free Survival (PFS), Time To Progression (TTP)). The principles and standards specified in ADaM provide a clear lineage from data collection to data analyses.  

Data Exchange

CDISC also provides standards for data exchange formats to enable the clinical research process, namely ODM-XML, which connects PRM, CDASH and SDTM.Define-XML and Dataset XML are ODM-XML extensions to connect the foundational standard SDTM to ADaM. Define-XML is also used to support the submission of clinical trials data to regulatory agencies, using a tabular dataset structure. Dataset XML complements Define XML by supporting the exchange of datasets based on Define-XML metadata.  

CDISC controlled vocabulary and extensions 

Controlled Terminology is the set of codelists and valid values used with data items within CDISC-defined datasets. Controlled Terminology provides the values required for submission to FDA and PMDA in CDISC-compliant datasets. Controlled Terminology does not tell you WHAT to collect; it tells you IF you collected a particular data item, how you should submit it in your electronic dataset. For example, if various lab tests were performed (e.g. WBC, glucose, albumin) CDISC provides vocabularies on how these tests should be reported, including the units of the measurements. 

CDISC, in collaboration with the National Cancer Institute's Enterprise Vocabulary Services (EVS), supports the Controlled Terminology needs of CDISC Foundational and Therapeutic Area Standards.


We can look at this at two levels. The first one is the FAIRness of the CDISC standards and specifications themselves. The second one is the FAIRness of CDISC coded data, i.e. clinical trial information recorded through an implementation of the CDISC standards stack.

FAIRness of CDISC standards:

With the release of the CDISC Library, authorised users and vetted member organisations can get access the various CDISC specifications and their versions via a REST-API, the documentation of which is available here:

Figure 6: the CDISC Library API Open API Specification compliant documentation. Note that accessing the full documentation requires an API key from the CDISC organisation.

The most interesting feature of the service is that it complies with the Open-API v3 specifications, which means that it allows software agents to rely and standardised annotations and API calls are self describing identifying parameters and response types.

The CDISC service thus available ranks fairly highly in terms of FAIR maturity since it implements community standards for describing API. Responses from the CDISC Library REST endpoint are either json, xml, csv or excel format.

Figure 7: Overview of the CDISC Library which can be found at

FAIRness of CDISC coded clinical trial data:

The different CDISC standards and exchange data formats are mostly table-based and are defining a grammar for structuring information in tables. They lack explicit semantics, and thus are not anchored to overarching ontologies/semantic models. This, in turns, may limit interoperability of CDISC coded clinical trial data with other data or clinical trial data coded using a different system, and thus FAIR maturity. To address this issue, a project called CDISC 360 was started. It tries to bring linked data and semantics to the CDISC standards. While this is an ambitious and promising effort, this is still very much work in progress

Some of the challenges with these datasets include updating internal systems and providing guidance when SDTM versions change. Although there exists some standards (such as CDASH) for capturing the data, the standards do not cover all data types and extensions are made by the sponsors within the CDISC framework. Furthermore, capturing metadata about sponsor extensions is another challenge in itself.

Further reading on CDISC


Observational Health Data Sciences and Informatics (OHDSI, pronounced “Odyssey”) is an open-science community. The focus of OHDSI is on observational data, including real-world data (RWD) and real world evidence (RWE) generation. Its goal is improving health by empowering the community to collaboratively generate evidence that promotes better health decisions and better care.  To enable this, the community established itself as a standard developing organisation (SDO) and started building information exchange specifications. 

OMOP Common Data Model (OMOP CDM)

Under the OHDSI umbrella, the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) was developed. The OMOP CDM is person-centric, as observational data capture what happens to a patient while receiving healthcare. Thus, all events are linked directly to the patient to allow a longitudinal view of the given healthcare and its outcome. This patient history, especially when summarised for a part of the population, can later be exploited for insights and for delivering improved health decisions and better care as stated in OHDSI’s mission statement. 

Transforming data from different healthcare systems to converge to the OMOP CDM enhances interoperability and enables data analysis on combined data (meta-analysis).

The OMOP CDM uses standardised tables as its building blocks. There are tables defined for different areas, e.g. clinical data, vocabularies, metadata  and health system tables. An overview of these tables can be found here and more details can be found here.

Besides the structure of the data in the described tables, OMOP CDM also uses standardised vocabularies to align the actual content - the meaning of the data (see next chapter). However, as noted with CDISC standards before, this suffers from the same issue of implicit semantics, where the meaning of relation between fields is unclear to software agents.

OHDSI standardised vocabularies 

The common data model would not be able to fulfil its promise to boost interoperability without using agreed, standardised vocabularies underpinning it. However, even for controlled vocabularies, competing or conflicting standards exist, particularly within health care, where national and regional differences in classifications and healthcare systems (e.g. ICD10CM, UCD10GM), often mandated by regulatory agencies or e.g. insurance systems influence technical choices. OHDSI also improves the interoperability by mandating the use of standardised vocabularies within the CDM. These vocabularies include well known terminologies like International Statistical Classification of Diseases and Related Health Problems (ICD), SNOMED, RXNorm and LOINC (this is common to CDISC SDTM and offers a bridge to this clinical trial data stack of standards).

OHDSI defines a standardised vocabulary to try to bridge this gap. It is such a fundamental part of the system that the use of the standardised vocabulary is mandatory for every OMOP CDM instance. While OHDSI uses existing vocabularies, a common format is defined and third party vocabularies have to be transformed from their original format into the OHDSI vocabulary tables, most notably the concept table. A term from the selected vocabularies and ontologies is seen as an OMOP concept, stored and referred to by an OMOP concept id rather than its original identifier or URI. The original code (i.e its identifier) is also stored as the ‘concept_code’. Vocabulary concepts are arranged in domains and categorised as standard and non-standard, but the convention is to only use the standard concepts to refer to events. The relationships and hierarchical structure of the ontologies are stored in the OMOP vocabulary tables and can be used when analysing the data. The Athena website provides an environment to search and find entities in the hosted vocabularies. 

Data analysis and evidence quality

After transforming data to the OMOP CDM, one can analyse the data using a variety of tools developed by the OHDSI community as open source software. The availability of a supporting  suite of tools is one of the big advantages of this data model. It also includes tools for validating the quality of data.

Using the analytics tools, a range of use cases can be addressed, as indicated in the OHDSI book chapter “Data Analytics use cases”. After analysing data, the question arises how reliable is this evidence? This is also addressed in detail in the OHDSI book in the chapter “Evidence quality”.

OHDSI study 

A study is represented in OHDSI by a study protocol and a so-called study package. The protocol is a human readable description of the observational study and should contain all necessary information to be able to reproduce the study by, e.g. including details about the study population or the methods or statistical procedures used throughout the study. The study package is a machine-readable implementation of the study. OHDSI supports this through tools for planning, documenting and reporting for observational studies. The data needs to be transferred/translated into the OMOP common data model, which we described previously. Despite all these measures, the reproducibility is not guaranteed and this is a point of discussion (this video gives more information on this problem). For further reading on OHDSI studies, we refer to this chapter in The Book of OHDSI

OHDSI standards and FAIR

As done when assessing CDISC standards, let’s look at both the FAIRness of the specifications themselves and that of the datasets.

FAIRness of OHDSI standards

All OHDSI development is available openly from a GitHub organisation, with dedicated repositories for the standard specifications ( ), the vocabulary ( ) or the supporting tools (e.g. ). Each of these repositories specify a licence (e.g. Apache 2.0 for CDM or the Data Quality Dashboard and the ‘unlicense licence’ for the vocabulary), which is key to establish Reuse. In spite of being hosted on GitHub, it seems that releases have not been submitted to CERN’s Zenodo document archive to obtain digital object identifier. Doing so would increase findability as it would create a record with associated metadata and version information. OHDSI standards have records in the catalogue ( for OHDSI Vocabulary, and for OMOP CDM).  

FAIRness of OHDSI standards encoded observational data

Observational data coded using the OHDSI standards stack provides a high potential for reuse through interoperability delivered by the use of a common syntax (OMOP CDM) and vocabularies (OMOP Vocabulary and ATHENA tools). However, as seen before in the clinical context with the CDISC standards, a number of features hamper full machine actionability and FAIRness. First, it is the lack of a semantic model representing the relations between OMOP CDM entities and fields. Then, due to sensitivity of the data stored in that format, accessing data is under the control of the data access committee. Making patient centric information findable requires careful considering, from data protection impact assessment to proper managed access brokering. However, this is beyond the scope of this guide so no further details will be given.

It could be that a GA4GH beacon (link) like approach could be considered for interrogating repositories of OHDSI coded datasets. Beacon is an API (sometimes extended with a user interface) that allows for data discovery of genomic and phenoclinic data.

Bearing this in mind, it is worth noting that the OHDSI group provides several “test” datasets, ranging from synthetic data ( ) to public, anonymised data ( ) for the purpose of training users and developers with the standards and the practice. These are important resources as they can be used to assess FAIR maturity using untethered data and perform theoretical work which could benefit the entire community.

The OMOP CDM was developed originally for US insurance claims data and was later adapted for hospital records, in general by mapping other data sources (e.g. EU data from general practitioners) to the OMOP CDM, which can be a resource intensive task. For instance, mapping of local source codes to the standard vocabularies used within the OHDSI community is time intensive and can lead to loss of nuance and details that are in the source data. For semantic mappings, the source data are stored in the database, however those values can’t be used in the analysis tools. On the other hand, extensions for the model have been developed to accommodate oncology data (episode tables in OMOP CDM v5.4) and are being developed for clinical trial data.

Further reading on OHDSI


FHIR®  (Fast Healthcare Interoperability Resources) is a specification for health care data exchange, published by HL7® which is Health Seven International ( ) which is a not-for-profit, ANSI-accredited standards developing organisation, like CDISC Foundation. HL7 provides a comprehensive framework and related standards for the exchange, integration, sharing and retrieval of electronic health information that supports clinical practice and the management, delivery and evaluation of health services. 

FHIR is an interoperability standard intended to facilitate the exchange of healthcare information between healthcare providers, patients, caregivers, payers, researchers, and anyone else involved in the healthcare ecosystem. It consists of 2 main parts – a content model in the form of ‘resources’, and a specification for the exchange of these resources (e.g messaging and documents) in the form of real-time RESTful interfaces. Thus, technically FHIR is implemented as a (RESTful) API service, which allows exposing discrete data elements instead of documents and therefore allows granular access to information and flexibility in data representation formats (e.g. JSON, XML or RDF). 

The home page for the FHIR standard ( ) presents the five levels of the standard/framework. FHIR defines the “basics” at level 1, “basic data types and metadata”, to represent information. Other levels include “implementation and binding to external specifications” on level 2, or “linking to real world concepts in the healthcare system” on level 3. Within each level, different FHIR modules are defined, e.g. clinical or diagnostics but also workflows or financials, so the FHIR specification covers all sorts of (clinical?) information and its exchange. The building blocks of FHIR are “FHIR resources” which can be extended if needed, and exchanged through standardised mechanisms as mentioned above


  • The initial release of the FHIR for FAIR Implementation Guide aims to provide guidance on how HL7 FHIR can be used for supporting FAIR health data at the study level, for similar reasons to this guide. We refer to the FHIR for FAIR Implementation Guide for further details:

In the context of real world evidence (RWE), the FHIR standards are a powerful enabler for interoperability. Furthermore, both OHDSI and CDISC now offer mappings for alignment to elements of FHIR.

It is noteworthy that FHIR and CDISC recommend or mandate similar common semantic resources (e.g. RxNorm, LOINC for drug treatments and laboratory tests description), so that semantic and syntactic convergence are beginning to emerge.

Further reading on FHIR