Using HCAHPS data to model correlates of medication understanding at hospital discharge

Rebecca J Bartlett Ellis,1 Karen L Werskey,2 Rachel M Stangland,2 Susan Ofner,1,3 Giorgos Bakoyannis1,3 1Indiana University School of Nursing, Indianapolis, IN, 2Schneck Medical Center, Seymour, IN, 3Department of Biostatistics, School of Medicine, Indiana University, Indianapolis, IN, USA...

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Autores principales:	Bartlett Ellis RJ, Werskey KL, Stangland RM, Ofner S, Bakoyannis G
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2017
Materias:	medication communication nurse communication provider communication hopsital quality Nursing RT1-120
Acceso en línea:	https://doaj.org/article/dddcd99fe972474da71df460e81260cc
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spelling	oai:doaj.org-article:dddcd99fe972474da71df460e81260cc2021-12-02T00:53:28ZUsing HCAHPS data to model correlates of medication understanding at hospital discharge2230-522Xhttps://doaj.org/article/dddcd99fe972474da71df460e81260cc2017-02-01T00:00:00Zhttps://www.dovepress.com/using-hcahps-data-to-model-correlates-of-medication-understanding-at-h-peer-reviewed-article-NRRhttps://doaj.org/toc/2230-522XRebecca J Bartlett Ellis,1 Karen L Werskey,2 Rachel M Stangland,2 Susan Ofner,1,3 Giorgos Bakoyannis1,3 1Indiana University School of Nursing, Indianapolis, IN, 2Schneck Medical Center, Seymour, IN, 3Department of Biostatistics, School of Medicine, Indiana University, Indianapolis, IN, USA Background: Hospitals are challenged to improve hospital transitions to home and are held accountable through public reporting.Design: This cross-sectional study used patients’ self-reported experience data from the publicly reported Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey to describe correlates of medication understanding at hospital discharge, using data collected from adult patients discharged from one Midwestern community hospital (N=154).Results: The final logistic regression model included four correlates of medication understanding: 1) nurse always communicates well, 2) physician always communicates well, 3) new prescriptions during hospital stay, and 4) very good or better mental health, and these classified 72.6% of the cases. Significant correlates of the patient strongly agreeing that they understood discharge medications were the “nurse always communicates well” (odds ratio =3.10, 95% confidence interval: 1.25, 7.66) and “very good or better self-perceived mental health” (odds ratio =2.17, 95% confidence interval: 1.02, 4.64).Conclusion: HCAHPS data can be used to model correlates of medication understanding, which are then useful for evaluating intervention effects following quality improvement. Keywords: medication, nurse communication, provider communication, hospital quality, nurse, nursing, physician, patient experience, patient satisfactionBartlett Ellis RJWerskey KLStangland RMOfner SBakoyannis GDove Medical Pressarticlemedication communication nurse communication provider communication hopsital qualityNursingRT1-120ENNursing: Research and Reviews, Vol Volume 7, Pp 1-7 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	medication communication nurse communication provider communication hopsital quality Nursing RT1-120
spellingShingle	medication communication nurse communication provider communication hopsital quality Nursing RT1-120 Bartlett Ellis RJ Werskey KL Stangland RM Ofner S Bakoyannis G Using HCAHPS data to model correlates of medication understanding at hospital discharge
description	Rebecca J Bartlett Ellis,1 Karen L Werskey,2 Rachel M Stangland,2 Susan Ofner,1,3 Giorgos Bakoyannis1,3 1Indiana University School of Nursing, Indianapolis, IN, 2Schneck Medical Center, Seymour, IN, 3Department of Biostatistics, School of Medicine, Indiana University, Indianapolis, IN, USA Background: Hospitals are challenged to improve hospital transitions to home and are held accountable through public reporting.Design: This cross-sectional study used patients’ self-reported experience data from the publicly reported Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey to describe correlates of medication understanding at hospital discharge, using data collected from adult patients discharged from one Midwestern community hospital (N=154).Results: The final logistic regression model included four correlates of medication understanding: 1) nurse always communicates well, 2) physician always communicates well, 3) new prescriptions during hospital stay, and 4) very good or better mental health, and these classified 72.6% of the cases. Significant correlates of the patient strongly agreeing that they understood discharge medications were the “nurse always communicates well” (odds ratio =3.10, 95% confidence interval: 1.25, 7.66) and “very good or better self-perceived mental health” (odds ratio =2.17, 95% confidence interval: 1.02, 4.64).Conclusion: HCAHPS data can be used to model correlates of medication understanding, which are then useful for evaluating intervention effects following quality improvement. Keywords: medication, nurse communication, provider communication, hospital quality, nurse, nursing, physician, patient experience, patient satisfaction
format	article
author	Bartlett Ellis RJ Werskey KL Stangland RM Ofner S Bakoyannis G
author_facet	Bartlett Ellis RJ Werskey KL Stangland RM Ofner S Bakoyannis G
author_sort	Bartlett Ellis RJ
title	Using HCAHPS data to model correlates of medication understanding at hospital discharge
title_short	Using HCAHPS data to model correlates of medication understanding at hospital discharge
title_full	Using HCAHPS data to model correlates of medication understanding at hospital discharge
title_fullStr	Using HCAHPS data to model correlates of medication understanding at hospital discharge
title_full_unstemmed	Using HCAHPS data to model correlates of medication understanding at hospital discharge
title_sort	using hcahps data to model correlates of medication understanding at hospital discharge
publisher	Dove Medical Press
publishDate	2017
url	https://doaj.org/article/dddcd99fe972474da71df460e81260cc
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Using HCAHPS data to model correlates of medication understanding at hospital discharge

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