Summary
Background: Creation of a new electronic health record (EHR)-based registry often can be a “one-off”
complex endeavor: first developing new EHR data collection and clinical decision support
tools, followed by developing registry-specific data extractions from the EHR for
analysis. Each development phase typically has its own long development and testing
time, leading to a prolonged overall cycle time for delivering one functioning registry
with companion reporting into production. The next registry request then starts from
scratch. Such an approach will not scale to meet the emerging demand for specialty
registries to support population health and value-based care.
Objective: To determine if the creation of EHR-based specialty registries could be markedly
accelerated by employing (a) a finite core set of EHR data collection principles and
methods, (b) concurrent engineering of data extraction and data warehouse design using
a common dimensional data model for all registries, and (c) agile development methods
commonly employed in new product development.
Methods: We adopted as guiding principles to (a) capture data as a byproduct of care of the
patient, (b) reinforce optimal EHR use by clinicians, (c) employ a finite but robust
set of EHR data capture tool types, and (d) leverage our existing technology toolkit.
Registries were defined by a shared condition (recorded on the Problem List) or a
shared exposure to a procedure (recorded on the Surgical History) or to a medication
(recorded on the Medication List). Any EHR fields needed - either to determine registry
membership or to calculate a registry-associated clinical quality measure (CQM) -
were included in the enterprise data warehouse (EDW) shared dimensional data model.
Extract-transform-load (ETL) code was written to pull data at defined “grains” from
the EHR into the EDW model. All calculated CQM values were stored in a single Fact
table in the EDW crossing all registries. Registry-specific dashboards were created
in the EHR to display both (a) real-time patient lists of registry patients and (b)
EDW-gener-ated CQM data. Agile project management methods were employed, including
co-development, lightweight requirements documentation with User Stories and acceptance
criteria, and time-boxed iterative development of EHR features in 2-week “sprints”
for rapid-cycle feedback and refinement.
Results: Using this approach, in calendar year 2015 we developed a total of 43 specialty chronic
disease registries, with 111 new EHR data collection and clinical decision support
tools, 163 new clinical quality measures, and 30 clinic-specific dashboards reporting
on both real-time patient care gaps and summarized and vetted CQM measure performance
trends.
Conclusions: This study suggests concurrent design of EHR data collection tools and reporting
can quickly yield useful EHR structured data for chronic disease registries, and bodes
well for efforts to migrate away from manual abstraction. This work also supports
the view that in new EHR-based registry development, as in new product development,
adopting agile principles and practices can help deliver valued, high-quality features
early and often.
Keywords
Registries - electronic health records - data collection - outcome and process assessment
(health care), - quality indicators - information storage and retrieval - data warehouse
- agile development - population health
