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Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry

Evaluation of sustainability after quality improvement (QI) projects in healthcare settings is an essential part of monitoring and future QI planning. With limitations in adopting quasi-experimental study design in real-world practice, healthcare professionals find it challenging to present the sust...

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Autores principales: Cho, Jungwon, Shin, Sangmi, Jeong, Youngmi, Lee, Eunsook, Ahn, Soyeon, Won, Seunghyun, Lee, Euni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471240/
https://www.ncbi.nlm.nih.gov/pubmed/34574961
http://dx.doi.org/10.3390/healthcare9091187
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author Cho, Jungwon
Shin, Sangmi
Jeong, Youngmi
Lee, Eunsook
Ahn, Soyeon
Won, Seunghyun
Lee, Euni
author_facet Cho, Jungwon
Shin, Sangmi
Jeong, Youngmi
Lee, Eunsook
Ahn, Soyeon
Won, Seunghyun
Lee, Euni
author_sort Cho, Jungwon
collection PubMed
description Evaluation of sustainability after quality improvement (QI) projects in healthcare settings is an essential part of monitoring and future QI planning. With limitations in adopting quasi-experimental study design in real-world practice, healthcare professionals find it challenging to present the sustained effect of QI changes effectively. To provide quantitative methodological approaches for demonstrating the sustainability of QI projects for healthcare professionals, we conducted data analyses based on a QI project to improve the computerized provider order entry system to reduce patients’ dosing frequencies in Korea. Data were collected for 5 years: 24-month pre-intervention, 12-month intervention, and 24-month post-intervention. Then, analytic approaches including control chart, Analysis of Variance (ANOVA), and segmented regression were performed. The control chart intuitively displayed how the outcomes changed over the entire period, and ANOVA was used to test whether the outcomes differed between groups. Last, segmented regression analysis was conducted to evaluate longitudinal effects of interventions over time. We found that the impact of QI projects in healthcare settings should be initiated following the Plan–Do–Study–Act cycle and evaluated long-term effects while widening the scope of QI evaluation with sustainability. This study can serve as a guide for healthcare professionals to use a number of statistical methodologies in their QI evaluations.
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spelling pubmed-84712402021-09-27 Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry Cho, Jungwon Shin, Sangmi Jeong, Youngmi Lee, Eunsook Ahn, Soyeon Won, Seunghyun Lee, Euni Healthcare (Basel) Article Evaluation of sustainability after quality improvement (QI) projects in healthcare settings is an essential part of monitoring and future QI planning. With limitations in adopting quasi-experimental study design in real-world practice, healthcare professionals find it challenging to present the sustained effect of QI changes effectively. To provide quantitative methodological approaches for demonstrating the sustainability of QI projects for healthcare professionals, we conducted data analyses based on a QI project to improve the computerized provider order entry system to reduce patients’ dosing frequencies in Korea. Data were collected for 5 years: 24-month pre-intervention, 12-month intervention, and 24-month post-intervention. Then, analytic approaches including control chart, Analysis of Variance (ANOVA), and segmented regression were performed. The control chart intuitively displayed how the outcomes changed over the entire period, and ANOVA was used to test whether the outcomes differed between groups. Last, segmented regression analysis was conducted to evaluate longitudinal effects of interventions over time. We found that the impact of QI projects in healthcare settings should be initiated following the Plan–Do–Study–Act cycle and evaluated long-term effects while widening the scope of QI evaluation with sustainability. This study can serve as a guide for healthcare professionals to use a number of statistical methodologies in their QI evaluations. MDPI 2021-09-09 /pmc/articles/PMC8471240/ /pubmed/34574961 http://dx.doi.org/10.3390/healthcare9091187 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cho, Jungwon
Shin, Sangmi
Jeong, Youngmi
Lee, Eunsook
Ahn, Soyeon
Won, Seunghyun
Lee, Euni
Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry
title Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry
title_full Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry
title_fullStr Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry
title_full_unstemmed Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry
title_short Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry
title_sort healthcare quality improvement analytics: an example using computerized provider order entry
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471240/
https://www.ncbi.nlm.nih.gov/pubmed/34574961
http://dx.doi.org/10.3390/healthcare9091187
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