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. 2016 Jul 20:2016:184-93.
eCollection 2016.

Metadata-driven Clinical Data Loading into i2b2 for Clinical and Translational Science Institutes

Andrew R Post  1 , Akshatha K Pai  1 , Richard Willard  1 , Bradley J May  1 , Andrew C West  1 , Sanjay Agravat  1 , Stephen J Granite  2 , Raimond L Winslow  2 , David S Stephens  1
Affiliations

Metadata-driven Clinical Data Loading into i2b2 for Clinical and Translational Science Institutes

Andrew R Post et al. AMIA Jt Summits Transl Sci Proc. .

Abstract

Clinical and Translational Science Award (CTSA) recipients have a need to create research data marts from their clinical data warehouses, through research data networks and the use of i2b2 and SHRINE technologies. These data marts may have different data requirements and representations, thus necessitating separate extract, transform and load (ETL) processes for populating each mart. Maintaining duplicative procedural logic for each ETL process is onerous. We have created an entirely metadata-driven ETL process that can be customized for different data marts through separate configurations, each stored in an extension of i2b2 's ontology database schema. We extended our previously reported and open source Eureka! Clinical Analytics software with this capability. The same software has created i2b2 data marts for several projects, the largest being the nascent Accrual for Clinical Trials (ACT) network, for which it has loaded over 147 million facts about 1.2 million patients.

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Figures

Figure 1.
Figure 1.
Eureka! Clinical Analytics component diagram.
Figure 2.
Figure 2.
The Eureka! job submission screen, showing the submission of a job loading the MIMIC-II dataset (see Methods) into an i2b2 project. Data to load is specified by dragging concepts from the hierarchy on the left into the blue Concepts box on the right. Users also may specify a date range.
Figure 3.
Figure 3.
Eureka! internal data model, showing various entities and their properties (blue boxes), and references between entities (black lines).
Figure 4.
Figure 4.
Extended i2b2 metadata schema for use with Eureka!, showing four ontology tables that each have one additional column as compared with i2b2’s standard ontology tables, the standard TABLE_ACCESS table supporting concept lookup, and the Eureka!-specific EK_MODIFIER_INTERP table (see text for details).
Figure 5.
Figure 5.
Mappings from the Eureka! internal data model to facts and the dimension tables in i2b2’s star schema.
Figure 6.
Figure 6.
Eureka! data flow illustrated for three ETL processes for loading data into i2b2 (ACT Network, local copy of UHC Clinical Database, and local copy of MIMIC-II Database). Ont=Ontology; CRC=Clinical Research Chart; PM=Project Management.
Figure 7.
Figure 7.
Deployment diagram showing Eureka! receiving a feed from an enterprise clinical data warehouse. The diagram shows the database and application server deployments, and it shows the network zones in which those systems are deployed (DMZ, Academic, Healthcare).
See this image and copyright information in PMC

References

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