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Klann JG
; Abend A
; Raghavan VA
; Mandl KD
; Murphy SN
J Am Med Inform Assoc
2016[Sep]; 23
(5
): 909-15
PMID26911824
show ga
OBJECTIVE: Reinventing data extraction from electronic health records (EHRs) to
meet new analytical needs is slow and expensive. However, each new data research
network that wishes to support its own analytics tends to develop its own data
model. Joining these different networks without new data extraction, transform,
and load (ETL) processes can reduce the time and expense needed to participate.
The Informatics for Integrating Biology and the Bedside (i2b2) project supports
data network interoperability through an ontology-driven approach. We use i2b2 as
a hub, to rapidly reconfigure data to meet new analytical requirements without
new ETL programming. MATERIALS AND METHODS: Our 12-site National Patient-Centered
Clinical Research Network (PCORnet) Clinical Data Research Network (CDRN) uses
i2b2 to query data. We developed a process to generate a PCORnet Common Data
Model (CDM) physical database directly from existing i2b2 systems, thereby
supporting PCORnet analytic queries without new ETL programming. This involved: a
formalized process for representing i2b2 information models (the specification of
data types and formats); an information model that represents CDM Version 1.0;
and a program that generates CDM tables, driven by this information model. This
approach is generalizable to any logical information model. RESULTS: Eight
PCORnet CDRN sites have implemented this approach and generated a CDM database
without a new ETL process from the EHR. This enables federated querying within
the CDRN and compatibility with the national PCORnet Distributed Research
Network. DISCUSSION: We have established a way to adapt i2b2 to new information
models without requiring changes to the underlying data. Eight Scalable
Collaborative Infrastructure for a Learning Health System sites vetted this
methodology, resulting in a network that, at present, supports research on 10
million patients' data. CONCLUSION: New analytical requirements can be quickly
and cost-effectively supported by i2b2 without creating new data extraction
processes from the EHR.
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