Document Type

Article

Publication Date

5-17-2021

Identifier

PMCID: PMC8378648

Abstract

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.

Journal Title

AMIA ... Annual Symposium proceedings [electronic resource] / AMIA Symposium. AMIA Symposium

Volume

2021

First Page

565

Last Page

574

MeSH Keywords

Databases, Factual; Electronic Health Records; Humans; Linear Models; Quality Improvement

Keywords

Factual Databases; Electronic Health Records; Linear Models; Quality Improvement

Comments

This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose.

Publisher's Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378648/

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