Achieving and Sustaining Automated Health Data Linkages for Learning Systems:
Barriers and Solutions
Authors: Van Eaton Erik G. , Devlin Allison B. , Devine Emily Beth , Flum David R. , Tarczy-Hornoch Peter 
Published: July 2014

Introduction: Delivering more appropriate, safer, and highly effective health care
is the goal of a learning health care system. The Agency for Healthcare Research and
Quality (AHRQ) funded enhanced registry projects: (1) to create and analyze valid data
for comparative effectiveness research (CER); and (2) to enhance the ability to monitor
and advance clinical quality improvement (QI). This case report describes barriers and
solutions from one state-wide enhanced registry project.Methods: The Comparative
Effectiveness Research and Translation Network (CERTAIN) deployed the commercially
available Amalga Unified Intelligence System™ (Amalga) as a central data repository to
enhance an existing QI registry (the Automation Project). An eight-step implementation
process included hospital recruitment, technical electronic health record (EHR) review,
hospital-specific interface planning, data ingestion, and validation. Data ownership and
security protocols were established, along with formal methods to separate data
management for QI purposes and research purposes. Sustainability would come from lowered
chart review costs and the hospital’s desire to invest in the infrastructure after
trying it.Findings: CERTAIN approached 19 hospitals in Washington State operating within
12 unaffiliated health care systems for the Automation Project. Five of the 19 completed
all implementation steps. Four hospitals did not participate due to lack of perceived
institutional value. Ten hospitals did not participate because their information
technology (IT) departments were oversubscribed (e.g., too busy with Meaningful Use
upgrades). One organization representing 22 additional hospitals expressed interest, but
was unable to overcome data governance barriers in time. Questions about data use for QI
versus research were resolved in a widely adopted project framework. Hospitals
restricted data delivery to a subset of patients, introducing substantial technical
challenges. Overcoming challenges of idiosyncratic EHR implementations required each
hospital to devote more IT resources than were predicted. Cost savings did not meet
projections because of the increased IT resource requirements and a different source of
lowered chart review costs.Discussion: CERTAIN succeeded in recruiting unaffiliated
hospitals into the Automation Project to create an enhanced registry to achieve AHRQ
goals. This case report describes several distinct barriers to central data aggregation
for QI and CER across unaffiliated hospitals: (1) competition for limited on-site IT
expertise, (2) concerns about data use for QI versus research, (3) restrictions on data
automation to a defined subset of patients, and (4) unpredictable resource needs because
of idiosyncrasies among unaffiliated hospitals in how EHR data are coded, stored, and
made available for transmission—even between hospitals using the same vendor’s EHR.
Therefore, even a fully optimized automation infrastructure would still not achieve
complete automation. The Automation Project was unable to align sufficiently with
internal hospital objectives, so it could not show a compelling case for
sustainability.