Page Comparison

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:  

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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

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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 ODHSI OHDSI standards

All ODHSI OHDSI development is available openly from a GitHub organisation, with dedicated repositories for the standard specifications (https://github.com/OHDSI/CommonDataModel ), the vocabulary (https://github.com/OHDSI/Vocabulary-v5.0 ) or the supporting tools (e.g. https://github.com/OHDSI/DataQualityDashboard ). 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. ODHSI OHDSI standards have records in the FAIRsharing.org catalogue (https://doi.org/10.25504/FAIRsharing.658tcg for OHDSI Vocabulary, and https://doi.org/10.25504/FAIRsharing.qk984b for OMOP CDM).  

FAIRness of ODHSI OHDSI standards encoded observational data

Observational data coded using the ODHSI 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.

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