The Implementation Network for Sharing Population Information from Research Entities (INSPIRE) project is assembling technologies and standards in support of a data hub that facilitates federated and/or shared research capable of interoperating across often-neglected low-resource settings: it aims to provide a platform-as-a-service, which can make data of disparate types available to many different styles of analysis, among which AI systems are increasingly prominent.
INSPIRE uses OMOP, a common data model that is becoming the gold standard for systematically integrating health data from disparate sources and conducting observational research at scale using routine clinical care data. However, OMOP is not completely FAIR29 and further work is needed to improve the ability to integrate diverse sources of data.
This case study team will improve the interoperation of OMOP with other standards to enable machine-actionable descriptions of data structure and provenance (e.g., DDI-CDI, PROV-O, SDTL); the composition of measurements focused on the objects of research (e.g., I-ADOPT); record linkage modeling for creating and evaluating bridges that connect domains, vocabularies (e.g., SKOS); and data discovery (e.g., Schema.org, DCAT). This suite of standards forms the basis of an ‘AI-Ready’ description of data suitable for use across domain and institutional boundaries.
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Work Package Leads
Population Health Featured Outputs
Population Health Data Implementation Guide
This implementation guide describes the way all aspects of the data are made available for use, both within and from outside the INSPIRE Network community, using standard metadata to describe the data. This is an exploration of how generic standards can be used to express the agreed community metadata set.
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More outputs coming soon.
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