What do we want for our health data - silos of information models for different purposes or ones that bridge multiple use cases? From a series of emails shared on the HL7 Patient Care email list in the past few days...
Grahame Grieve (FHIR, HL7):
"Heather, you need to keep in mind the difference between FHIR and clinical models: it's not our business to say not to exchange data that people do have because some user in an edge case might not understand it. We define an exchange standard, not a clinical UI standard..."
"Heather, do not lose sight of the difference between a clinical standard for what care/records should be, and FHIR, which is an IT standard for how care records are."
"...I am concerned about developing another standard that you state clearly is only designed for exchange and not for what care records should be. If we are not designing to try to harmonise data requirements for health information exchange, how clinical care records are and how clinical care records should be, then we are building siloes of data structures again, that will require mappings and transforms ad infinitum. I’d hate to see us end up with a standard for exchange that can’t be implemented for persistence"...
If we end up with models for exchange, models representing current data in systems (whether or not they represent good clinical practice and models that are regarded as the roadmap for good data, then what have we got? Three sets of data models that perpetuate the nightmare of non-interoperability.
Our openEHR archetypes are attempting to bridge all of these. Use them in whatever context you choose - messages, document exchange, EHR persistence, CDS, secondary use, aggregation and analysis, querying etc. The 'secret sauce' is the use of a second layer of modelling - the template, that allows the correct expression of the archetype appropriate for the context of use.
Mappings and transformations are acceptable where we don't have any choice, especially with legacy data, but they open us up to vulnerabilities from errors, misinterpretation and ambiguity, concerns re data integrity and possible overt data loss. Given the choice, lets work towards creating high quality data that can be re-used in multiple contexts safely.