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To make clinical data semantically interoperable, the eCRF should be built with interoperability in mind. Instead of using your own local definitions for you data items or defining definitions from scratch, reuse of existing definitions should be considered. For this purpose, many initiatives exist that aim to provide templates, such as CDASH, provided by CDISC and Common Data Elements (CDEs) offered by, for example, the National Institute of Health. Furthermore, data definitions (codebooks) may be available for reuse in online solutions such as ART-DECOR or OpenEHR [iCRF].
[hri_edc] https://www.health-ri.nl/electronic-data-capture-edc-or-electronic-case-record-form-ecrf-systems
[iCRF] https://f1000research.com/articles/9-81
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The eCRF was designed to collect data for the CDEs (described in step 2) in the Castor EDC system [5]. Several dependencies, e.g. only show ‘Date of death’ when the patient is deceased, and validations, e.g. validate whether the entered Online Mendelian Inheritance in Man (OMIM) genetic disorder code follows the OMIM standard, were included in order to collect high-quality data (the eCRF questions can be found in [6]). To this end, we mostly worked with closed questions and/or drop-down menus and prevented entering free text as much as possible. An example from the eCRF is shown in Figure S1A. The eCRF template containing the CDEs and the ontologies to annotate them (see step 5) was described in a codebook. This codebook was made openly available in ART-DECOR, a platform from Nictiz, the Dutch competence centre for electronic exchange of health and care information [7], and can be directly implemented in the Castor EDC system or other EDC systems using the openly available iCRF Generator tool [8].
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The quality of the eCRF will greatly influence the quality of the collected data. Furthermore, by using reusing existing definitions to build these eCRFs, the collected data will be more interoperable. <say something about semantic modelling?>
Expertise requirements for this step
This section could describe the expertise required. Perhaps the Build Your Team step could then be an aggregation of all the “Expertise requirements for this step” steps that someone needs to fulfil his/her FAIRification goals.
How to
If common data elements (CDEs) are used (Step X), these should be used as the basis for your eCRFs, since the items in the CDEs have been unambiguously defined. Furthermore, if the CDEs have been annotated with ontologies, these should be used in the eCRF.
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Depending on your project, data collection could take place in one or more EDCs.
Expertise requirements for this step
This section could describe the expertise required. Perhaps the Build Your Team step could then be an aggregation of all the “Expertise requirements for this step” steps that someone needs to fulfil his/her FAIRification goals.
Practical Examples from the Community
This section should show the step applied in a real project. Links to demonstrator projects.
References & Further reading
[De Novo] https://ojrd.biomedcentral.com/articles/10.1186/s13023-021-02004-y
[hri_edc] https://www.health-ri.nl/electronic-data-capture-edc-or-electronic-case-record-form-ecrf-systems
[iCRF] https://f1000research.com/articles/9-81
Authors / Contributors
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Training
Add links to training resources relevant for this step. Since the training aspect is still under development, currently many steps have “Relevant training will be added in the future if available.”