Rapid Development of Specialty Population Registries and Quality Measures from Electronic Health Record Data

Abstract

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 by product 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-generated 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,...

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“…Practical approaches to analytic interoperability among EHR-originated datasets would provide value both for population health analytics and for clinical research [1][2][3]. Clinical registries define most patient subpopulations-important clinical phenotypes-by either a shared condition or a shared exposure (e.g.…”

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“…There is an increasing focus internationally on improving the quality of maternity care and the development and application of measures to drive quality improvement . Two key policy drivers relevant to improving the quality of maternity care have recently been introduced in England.…”

“…There is an increasing focus internationally on improving the quality of maternity care and the development and application of measures to drive quality improvement. [1][2][3] Two key policy drivers relevant to improving the quality of maternity care have recently been introduced in England. The first of these was the announcement by the government in November 2015 of a national ambition to reduce maternal mortality, stillbirth, neonatal mortality and serious neonatal injury by 20% by 2020, and 50% by 2030.…”