Selecting quality improvement projects can often be a reactive process. In order to demonstrate a data-driven strategy, we used multi-site, de-identified electronic health record (EHR) data to prioritize the severity of a quality concern: inappropriate A1c test orders for sickle cell disease patients in two randomly chosen facilities (Facility A & B). The best linear unbiased predictions (BLUP) generated from Generalized Linear Mixed Model (GLMM) was estimated for all 393 facilities with 37,151 SCD patients in the Cerner Health FactsTM (HF) data warehouse based on the ratio of inappropriate A1c orders. Ranking the BLUP after applying the GLMM indicates that the facility A being in the second quartile may not have a quality gap as significant as facility B in the top quartile for this quality concern. This study illustrates the utility of multisite EHR data for evaluating QI projects and the utility of GLMM to enable this analysis.
AMIA ... Annual Symposium proceedings [electronic resource] / AMIA Symposium. AMIA Symposium
Databases, Factual; Electronic Health Records; Humans; Linear Models; Quality Improvement
Factual Databases; Electronic Health Records; Linear Models; Quality Improvement
Sivasankar S, Cheng AL, Hoffman M. Ranking Methodology to Evaluate the Severity of a Quality Gap Using a National EHR Database. AMIA Annu Symp Proc. 2021;2021:565-574. Published 2021 May 17.