Effect of Replacing Vendor QTc Alerts with a Custom QTc Risk Alert in Inpatients.

The aim of the study is to implement a customized QTc interval clinical decision support (CDS) alert strategy in our electronic health record for hospitalized patients and aimed at providers with the following objectives: minimize QTc prolongation, minimize exposure to QTc prolonging medications, and decrease overall QTc-related alerts. A strategy that was based on the validated QTc risk scoring tool and replacing medication knowledge vendor alerts with custom QTc prolongation alerts was implemented. This is a retrospective quasi-experimental study with a pre-intervention period (August 2019 to October 2019) and post-intervention period (December 2019 to February 2020). The custom alert was implemented in November 2019. In the pre-implementation group, 361 (19.3%) patients developed QTc prolongation, and in the post-implementation group, 357 (19.6%) patients developed QTc prolongation (OR: 1.02, 95% CI: 0.87-1.20, p = 0.81). The odds ratio of an action taken post-implementation compared with pre-implementation was 18.90 (95% CI: 14.03-25.47, p <0. 001). There was also a decrease in total orders for QTc prolonging medications from 7,921 (5.5%) to 7,566 (5.3%) with an odds ratio of 0.96 (95% CI: 0.93-0.99, p = 0.01). We were able to decrease patient exposure to QTc prolonging medications while not increasing the rate of QTc prolongation as well as improving alert action rate. Additionally, there was a decrease in QTc prolonging medication orders which illustrates the benefit of using a validated risk score with a customized CDS approach compared with a traditional vendor-based strategy. Further research is needed to confirm if an approach implemented at our organization can reduce QTc prolongation rates. Thieme. All rights reserved.

Citation

Steven Stettner, Sarah Adie, Sarah Hanigan, Michael Thomas, Kristen Pogue, Christopher Zimmerman. Effect of Replacing Vendor QTc Alerts with a Custom QTc Risk Alert in Inpatients. Applied clinical informatics. 2022 Jan;13(1):19-29

Mesh Tags

PMID: 34986493

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