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To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database.

ABSTRACT: BACKGROUND: It is not clear which part of the variation in hospital readmissions can be attributed to the standard of care hospitals provide. This is in spite of their widespread use as an indicator of a lower quality of care. The aim of this study is to assess the variation in readmission...

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Autores principales: Hekkert, Karin, Kool, Rudolf B., Rake, Ester, Cihangir, Sezgin, Borghans, Ine, Atsma, Femke, Westert, Gert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6307249/
https://www.ncbi.nlm.nih.gov/pubmed/30591058
http://dx.doi.org/10.1186/s12913-018-3761-y
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author Hekkert, Karin
Kool, Rudolf B.
Rake, Ester
Cihangir, Sezgin
Borghans, Ine
Atsma, Femke
Westert, Gert
author_facet Hekkert, Karin
Kool, Rudolf B.
Rake, Ester
Cihangir, Sezgin
Borghans, Ine
Atsma, Femke
Westert, Gert
author_sort Hekkert, Karin
collection PubMed
description ABSTRACT: BACKGROUND: It is not clear which part of the variation in hospital readmissions can be attributed to the standard of care hospitals provide. This is in spite of their widespread use as an indicator of a lower quality of care. The aim of this study is to assess the variation in readmissions on the hospital level after adjusting for case-mix factors. METHODS: We performed multilevel logistic regression analyses with a random intercept for the factor ‘hospital’ to estimate the variance on the hospital level after adjustment for case-mix variables. We used administrative data from 53 Dutch hospitals from 2010 to 2012 (58% of all Dutch hospitals; 2,577,053 admissions). We calculated models for the top ten diagnosis groups with the highest number of readmissions after an index admission for a surgical procedure. We calculated intraclass correlation coefficients (ICC) per diagnosis group in order to explore the variation in readmissions between hospitals. Furthermore, we determined C-statistics for the models with and without a random effect on the hospital level to determine the discriminative ability. RESULTS: The ICCs on the hospital level ranged from 0.48 to 2.70% per diagnosis group. The C-statistics of the models with a random effect on the hospital level ranged from 0.58 to 0.65 for the different diagnosis groups. The C-statistics of the models that included the hospital level were higher compared to the models without this level. CONCLUSIONS: For some diagnosis groups, a small part of the explained variation in readmissions was found on the hospital level, after adjusting for case-mix variables. However, the C-statistics of the prediction models are moderate, so the discriminative ability is limited. Readmission indicators might be useful for identifying areas for improving quality within hospitals on the level of diagnosis or specialty. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12913-018-3761-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-63072492019-01-02 To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database. Hekkert, Karin Kool, Rudolf B. Rake, Ester Cihangir, Sezgin Borghans, Ine Atsma, Femke Westert, Gert BMC Health Serv Res Research Article ABSTRACT: BACKGROUND: It is not clear which part of the variation in hospital readmissions can be attributed to the standard of care hospitals provide. This is in spite of their widespread use as an indicator of a lower quality of care. The aim of this study is to assess the variation in readmissions on the hospital level after adjusting for case-mix factors. METHODS: We performed multilevel logistic regression analyses with a random intercept for the factor ‘hospital’ to estimate the variance on the hospital level after adjustment for case-mix variables. We used administrative data from 53 Dutch hospitals from 2010 to 2012 (58% of all Dutch hospitals; 2,577,053 admissions). We calculated models for the top ten diagnosis groups with the highest number of readmissions after an index admission for a surgical procedure. We calculated intraclass correlation coefficients (ICC) per diagnosis group in order to explore the variation in readmissions between hospitals. Furthermore, we determined C-statistics for the models with and without a random effect on the hospital level to determine the discriminative ability. RESULTS: The ICCs on the hospital level ranged from 0.48 to 2.70% per diagnosis group. The C-statistics of the models with a random effect on the hospital level ranged from 0.58 to 0.65 for the different diagnosis groups. The C-statistics of the models that included the hospital level were higher compared to the models without this level. CONCLUSIONS: For some diagnosis groups, a small part of the explained variation in readmissions was found on the hospital level, after adjusting for case-mix variables. However, the C-statistics of the prediction models are moderate, so the discriminative ability is limited. Readmission indicators might be useful for identifying areas for improving quality within hospitals on the level of diagnosis or specialty. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12913-018-3761-y) contains supplementary material, which is available to authorized users. BioMed Central 2018-12-27 /pmc/articles/PMC6307249/ /pubmed/30591058 http://dx.doi.org/10.1186/s12913-018-3761-y Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Hekkert, Karin
Kool, Rudolf B.
Rake, Ester
Cihangir, Sezgin
Borghans, Ine
Atsma, Femke
Westert, Gert
To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database.
title To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database.
title_full To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database.
title_fullStr To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database.
title_full_unstemmed To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database.
title_short To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database.
title_sort to what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large dutch database.
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6307249/
https://www.ncbi.nlm.nih.gov/pubmed/30591058
http://dx.doi.org/10.1186/s12913-018-3761-y
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