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Where does OMOP-OHDSI fit in the open source health informatics environment?

September 26, 2019 | By Maxim Moinat


OHDSI is a well-established observational health analytics platform, supported by a very active community of researchers, developers and clinicians. Originally, the efforts mainly focussed on digitization/analysis of health insurance claims data in the United States, but in recent years the platform has expanded to patient electronic health records (EHR) and even clinical trial data.

This raises the question where does OHDSI fit in the wider health informatics ecosystem? In this blog we compare OHDSI/OMOP to openEHR and FHIR as each of these platforms operates in its own domain and serves different use cases.

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OHDSI versus openEHR

openEHR is an e-health technology, consisting of open specifications, clinical models and software to create standards in clinics. It has an active open source community that produces and manages standards used to model clinical information in detail. openEHR has been designed to be used in the clinic to collect and exchange electronic health records (EHR) data, focussing on an extensive open standard including hundreds of ‘archetypes’, the key element of the standard. However, records exchange is not the primary focus, in contrast to standards like FHIR.

Although scientific research can be conducted with the data stored in openEHR, the platform is not specifically designed for exploration and analysis of observational data. OHDSI on the other hand is not designed for operation healthcare processes, but does provide scientists with a comprehensive tool set for observational research. The tooling and underlying data model (OMOP CDM) have been designed to generate high quality and reproducible real-world evidence. 

Let me illustrate the difference between openEHR and OHDSI/OMOP with an example of a key model difference.

Example: Observation period

A core data element of the OMOP CDM is the ‘observation period’, which has no equivalent in EHR systems. This period is person-specific and represents the time span in which healthcare encounters occur for this person. In research, this determines how many years of follow-up time are available for analysis, both in a cohort and per person. An absence of events in a particular period means that this person did not visit the clinic. For clinical researchers this is important information with regard to cohort inclusion criteria (e.g. minimum follow-up period). In clinical practice this is less relevant information, and therefore not normally captured.





FHIR (Fast Healthcare Interoperability Resources) is a universal standard for exchange of patient’s health records from EHR-systems. It is composed of so-called resources, bundled in profiles that specify how a particular information element should be represented. There are around 100 resources, including diagnostics, medication, physical measurements, et cetera. FHIR distinguishes itself by having a description of the API that exposes the resources from the underlying EHR system. This allows other applications to (securely) pull data from and push data to EHR systems. There are multiple sandboxes to explore the standards, e.g. Synthetic Mass, where a simulated population of Massachusetts has been exposed through FHIR.

In contrast to many other standards, FHIR is not the endpoint of the data. Rather than persisting data in this standard, it facilitates data transfer to other applications using the resource profiles and API definition. EHR-systems, like openEHR, can use FHIR to expose data to other applications. These can be consumer apps that make health data insightful to patients or research platforms like OHDSI. 

In the next section, we give an example of how FHIR facilitates the connection between EHR-systems and scientific research using the OHDSI tooling.

Example: Predictive modelling

The OHDSI analytics and FHIR exchange standards can strengthen each other. A predictive model is a good example. In this case, OHDSI tools support the creation of a model, using the Patient Level Prediction (PLP) package that runs on any OMOP CDM database. The resulting model can be applied to personal health data by using FHIR resources. If you would like to know more, you can read more about this research here: Clinical Predictive Modeling Development and Deployment through FHIR Web Services




As we have shown, openEHR, FHIR and OMOP/OHDSI are not exclusive tools, but each has its place in the health care and medical research data environment. Besides, they can be combined to serve a particular purpose. In summary:

  • openEHR focuses on comprehensive clinical information models and can be used to implement systems that collect and persist medical records using open standards. These standards enable the exchange of patient information between clinicians and/or to other standards.
  • FHIR focuses on pulling records from (and pushing to) a growing number of FHIR compliant EHR-systems. Medical apps can be built on top of FHIR to aid/support patients, health care providers and researchers.
  • OHDSI has been built for scientists, providing them with the tools to generate high quality and reproducible evidence from observational health data. The data can be extracted, transformed and loaded directly from medical claims data or EHR-systems, and exchange standards like FHIR can facilitate this process.