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Cons:
o It is not clear how fast the library keeps up with new FHIR standards
o Seems to not be rigorous enough - we have found examples that pass offline validation checks but fail online
To combine the best of both worlds, namely, a fast validator that keeps up to date with changing FHIR standards, we recommend
implementing a private FHIR validation server in the future. This could have the added benefit of building a custom version that, for
instance, validates FHIR data that is also compliant with PRSB core information standards.
We would recommend implementing validation at the point when the config is being read, and when an output is generated. This will protect against an invalid entry / output being generated.
The text was updated successfully, but these errors were encountered:
Validating the FHIR health record that is generated by the Alpha data model can currently be done in two ways.
Offline - using a python library called fhir.resources - https://github.com/nazrulworld/fhir.resources:
o It is not clear how fast the library keeps up with new FHIR standards
o Seems to not be rigorous enough - we have found examples that pass offline validation checks but fail online
it is kept up to date with current FHIR standards
To combine the best of both worlds, namely, a fast validator that keeps up to date with changing FHIR standards, we recommend
implementing a private FHIR validation server in the future. This could have the added benefit of building a custom version that, for
instance, validates FHIR data that is also compliant with PRSB core information standards.
We would recommend implementing validation at the point when the config is being read, and when an output is generated. This will protect against an invalid entry / output being generated.
The text was updated successfully, but these errors were encountered: