Serveur d'exploration Hippolyte Bernheim

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Hospital readmission performance and patterns of readmission: retrospective cohort study of Medicare admissions

Identifieur interne : 000103 ( Ncbi/Merge ); précédent : 000102; suivant : 000104

Hospital readmission performance and patterns of readmission: retrospective cohort study of Medicare admissions

Auteurs : Kumar Dharmarajan [États-Unis] ; Angela F. Hsieh [États-Unis] ; Zhenqiu Lin [États-Unis] ; Héctor Bueno [Espagne] ; Joseph S. Ross [États-Unis] ; Leora I. Horwitz [États-Unis] ; José Augusto Barreto-Filho [Brésil] ; Nancy Kim [États-Unis] ; Lisa G. Suter [États-Unis] ; Susannah M. Bernheim [États-Unis] ; Elizabeth E. Drye [États-Unis] ; Harlan M. Krumholz [États-Unis]

Source :

RBID : PMC:3898430

Abstract

Objectives To determine whether high performing hospitals with low 30 day risk standardized readmission rates have a lower proportion of readmissions from specific diagnoses and time periods after admission or instead have a similar distribution of readmission diagnoses and timing to lower performing institutions.

Design Retrospective cohort study.

Setting Medicare beneficiaries in the United States.

Participants Patients aged 65 and older who were readmitted within 30 days after hospital admission for heart failure, acute myocardial infarction, or pneumonia in 2007-09.

Main outcome measures Readmission diagnoses were classified with a modified version of the Centers for Medicare and Medicaid Services’ condition categories, and readmission timing was classified by day (0-30) after hospital discharge. Hospital 30 day risk standardized readmission rates over the three years of study were calculated with public reporting methods of the US federal government, and hospitals were categorized with bootstrap analysis as having high, average, or low readmission performance for each index condition. High and low performing hospitals had ≥95% probability of having an interval estimate respectively less than or greater than the national 30 day readmission rate over the three year period of study. All remaining hospitals were considered average performers.

Results For readmissions in the 30 days after the index admission, there were 320 003 after 1 291 211 admissions for heart failure (4041 hospitals), 102 536 after 517 827 admissions for acute myocardial infarction (2378 hospitals), and 208 438 after 1 135 932 admissions for pneumonia (4283 hospitals). The distribution of readmissions by diagnosis was similar across categories of hospital performance for all three conditions. High performing hospitals had fewer readmissions for all common diagnoses. Median time to readmission was similar by hospital performance for heart failure and acute myocardial infarction, though was 1.4 days longer among high versus low performing hospitals for pneumonia (P<0.001). Findings were unchanged after adjustment for other hospital characteristics potentially associated with readmission patterns.

Conclusions High performing hospitals have proportionately fewer 30 day readmissions without differences in readmission diagnoses and timing, suggesting the possible benefit of strategies that lower risk of readmission globally rather than for specific diagnoses or time periods after hospital stay.


Url:
DOI: 10.1136/bmj.f6571
PubMed: 24259033
PubMed Central: 3898430

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PMC:3898430

Le document en format XML

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<name sortKey="Barreto Filho, Jose Augusto" sort="Barreto Filho, Jose Augusto" uniqKey="Barreto Filho J" first="José Augusto" last="Barreto-Filho">José Augusto Barreto-Filho</name>
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<name sortKey="Kim, Nancy" sort="Kim, Nancy" uniqKey="Kim N" first="Nancy" last="Kim">Nancy Kim</name>
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<div type="abstract" xml:lang="en">
<p>
<bold>Objectives</bold>
To determine whether high performing hospitals with low 30 day risk standardized readmission rates have a lower proportion of readmissions from specific diagnoses and time periods after admission or instead have a similar distribution of readmission diagnoses and timing to lower performing institutions.</p>
<p>
<bold>Design </bold>
Retrospective cohort study.</p>
<p>
<bold>Setting</bold>
Medicare beneficiaries in the United States.</p>
<p>
<bold>Participants</bold>
Patients aged 65 and older who were readmitted within 30 days after hospital admission for heart failure, acute myocardial infarction, or pneumonia in 2007-09. </p>
<p>
<bold>Main outcome measures</bold>
Readmission diagnoses were classified with a modified version of the Centers for Medicare and Medicaid Services’ condition categories, and readmission timing was classified by day (0-30) after hospital discharge. Hospital 30 day risk standardized readmission rates over the three years of study were calculated with public reporting methods of the US federal government, and hospitals were categorized with bootstrap analysis as having high, average, or low readmission performance for each index condition. High and low performing hospitals had ≥95% probability of having an interval estimate respectively less than or greater than the national 30 day readmission rate over the three year period of study. All remaining hospitals were considered average performers.</p>
<p>
<bold>Results</bold>
For readmissions in the 30 days after the index admission, there were 320 003 after 1 291 211 admissions for heart failure (4041 hospitals), 102 536 after 517 827 admissions for acute myocardial infarction (2378 hospitals), and 208 438 after 1 135 932 admissions for pneumonia (4283 hospitals). The distribution of readmissions by diagnosis was similar across categories of hospital performance for all three conditions. High performing hospitals had fewer readmissions for all common diagnoses. Median time to readmission was similar by hospital performance for heart failure and acute myocardial infarction, though was 1.4 days longer among high versus low performing hospitals for pneumonia (P<0.001). Findings were unchanged after adjustment for other hospital characteristics potentially associated with readmission patterns.</p>
<p>
<bold>Conclusions</bold>
High performing hospitals have proportionately fewer 30 day readmissions without differences in readmission diagnoses and timing, suggesting the possible benefit of strategies that lower risk of readmission globally rather than for specific diagnoses or time periods after hospital stay.</p>
</div>
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<article-meta>
<article-id pub-id-type="pmid">24259033</article-id>
<article-id pub-id-type="pmc">3898430</article-id>
<article-id pub-id-type="publisher-id">dhak013336</article-id>
<article-id pub-id-type="doi">10.1136/bmj.f6571</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Research</subject>
</subj-group>
<subj-group subj-group-type="hwp-journal-coll">
<subject>1779</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Hospital readmission performance and patterns of readmission: retrospective cohort study of Medicare admissions</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Dharmarajan</surname>
<given-names>Kumar</given-names>
</name>
<role>cardiology fellow</role>
<xref ref-type="aff" rid="aff1">1</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Hsieh</surname>
<given-names>Angela F</given-names>
</name>
<role>statistician</role>
<xref ref-type="aff" rid="aff2">2</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Lin</surname>
<given-names>Zhenqiu</given-names>
</name>
<role>statistician</role>
<xref ref-type="aff" rid="aff2">2</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Bueno</surname>
<given-names>Héctor</given-names>
</name>
<role>associate professor of medicine</role>
<xref ref-type="aff" rid="aff3">3</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ross</surname>
<given-names>Joseph S</given-names>
</name>
<role>assistant professor of medicine</role>
<xref ref-type="aff" rid="aff4">4</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Horwitz</surname>
<given-names>Leora I</given-names>
</name>
<role>assistant professor of medicine</role>
<xref ref-type="aff" rid="aff4">4</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Barreto-Filho</surname>
<given-names>José Augusto</given-names>
</name>
<role>associate professor of cardiology</role>
<xref ref-type="aff" rid="aff5">5</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Kim</surname>
<given-names>Nancy</given-names>
</name>
<role>assistant professor of medicine</role>
<xref ref-type="aff" rid="aff4">4</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Suter</surname>
<given-names>Lisa G</given-names>
</name>
<role>assistant professor of medicine</role>
<xref ref-type="aff" rid="aff6">6</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Bernheim</surname>
<given-names>Susannah M</given-names>
</name>
<role>acting director of quality measurement programs</role>
<xref ref-type="aff" rid="aff2">2</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Drye</surname>
<given-names>Elizabeth E</given-names>
</name>
<role>research scientist in pediatrics</role>
<xref ref-type="aff" rid="aff2">2</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Krumholz</surname>
<given-names>Harlan M</given-names>
</name>
<role>Harold J Hines Jr professor of medicine</role>
<xref ref-type="aff" rid="aff2">2</xref>
</contrib>
<aff id="aff1">
<label>1</label>
Division of Cardiology, Department of Internal Medicine, Columbia University Medical Center, 630 West 168th Street, Box 93, PH 10-203, New York, NY 10032, USA</aff>
<aff id="aff2">
<label>2</label>
Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT 06510, USA</aff>
<aff id="aff3">
<label>3</label>
Department of Cardiology, Hospital General Universitario Gregorio Marañón, Dr Esquerdo 46 Madrid 28007, Spain</aff>
<aff id="aff4">
<label>4</label>
Section of General Internal Medicine, Yale University School of Medicine, PO Box 208056, New Haven, CT 06520, USA</aff>
<aff id="aff5">
<label>5</label>
Division of Cardiology, Federal University of Sergipe, and Clínica e Hospital São Lucas, Rua Claudio Batista, S/N, Bairro Santo Antonio, 49060-100, Aracaju, Sergipe, Brazil</aff>
<aff id="aff6">
<label>6</label>
Section of Rheumatology, Yale University School of Medicine, PO Box 208031, New Haven, CT 06520, USA</aff>
</contrib-group>
<author-notes>
<corresp>Correspondence to: K Dharmarajan
<email>kumar.dharmarajan@columbia.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="collection">
<year>2013</year>
</pub-date>
<pub-date pub-type="epub">
<day>20</day>
<month>11</month>
<year>2013</year>
</pub-date>
<volume>347</volume>
<elocation-id>f6571</elocation-id>
<history>
<date date-type="accepted">
<day>21</day>
<month>10</month>
<year>2013</year>
</date>
</history>
<permissions>
<copyright-statement>© Dharmarajan et al 2013</copyright-statement>
<copyright-year>2013</copyright-year>
<copyright-holder>Dharmarajan et al</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/">
<license-p>This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/">http://creativecommons.org/licenses/by-nc/3.0/</ext-link>
.</license-p>
</license>
</permissions>
<self-uri xlink:title="pdf" xlink:type="simple" xlink:href="bmj.f6571.pdf"></self-uri>
<abstract>
<p>
<bold>Objectives</bold>
To determine whether high performing hospitals with low 30 day risk standardized readmission rates have a lower proportion of readmissions from specific diagnoses and time periods after admission or instead have a similar distribution of readmission diagnoses and timing to lower performing institutions.</p>
<p>
<bold>Design </bold>
Retrospective cohort study.</p>
<p>
<bold>Setting</bold>
Medicare beneficiaries in the United States.</p>
<p>
<bold>Participants</bold>
Patients aged 65 and older who were readmitted within 30 days after hospital admission for heart failure, acute myocardial infarction, or pneumonia in 2007-09. </p>
<p>
<bold>Main outcome measures</bold>
Readmission diagnoses were classified with a modified version of the Centers for Medicare and Medicaid Services’ condition categories, and readmission timing was classified by day (0-30) after hospital discharge. Hospital 30 day risk standardized readmission rates over the three years of study were calculated with public reporting methods of the US federal government, and hospitals were categorized with bootstrap analysis as having high, average, or low readmission performance for each index condition. High and low performing hospitals had ≥95% probability of having an interval estimate respectively less than or greater than the national 30 day readmission rate over the three year period of study. All remaining hospitals were considered average performers.</p>
<p>
<bold>Results</bold>
For readmissions in the 30 days after the index admission, there were 320 003 after 1 291 211 admissions for heart failure (4041 hospitals), 102 536 after 517 827 admissions for acute myocardial infarction (2378 hospitals), and 208 438 after 1 135 932 admissions for pneumonia (4283 hospitals). The distribution of readmissions by diagnosis was similar across categories of hospital performance for all three conditions. High performing hospitals had fewer readmissions for all common diagnoses. Median time to readmission was similar by hospital performance for heart failure and acute myocardial infarction, though was 1.4 days longer among high versus low performing hospitals for pneumonia (P<0.001). Findings were unchanged after adjustment for other hospital characteristics potentially associated with readmission patterns.</p>
<p>
<bold>Conclusions</bold>
High performing hospitals have proportionately fewer 30 day readmissions without differences in readmission diagnoses and timing, suggesting the possible benefit of strategies that lower risk of readmission globally rather than for specific diagnoses or time periods after hospital stay.</p>
</abstract>
</article-meta>
<notes notes-type="data-supplement">
<label>Web Extra</label>
<title>Extra material supplied by the author</title>
<supplementary-material content-type="local-data">
<caption>
<p>
<bold>Appendix 1</bold>
: Supplementary tables</p>
</caption>
<media xlink:href="dhak013336.ww1_default.pdf">
<caption>
<p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data">
<caption>
<p>
<bold>Appendix 2</bold>
: Supplementary figures</p>
</caption>
<media xlink:href="dhak013336.ww2_default.pdf">
<caption>
<p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
</notes>
</front>
</pmc>
<affiliations>
<list>
<country>
<li>Brésil</li>
<li>Espagne</li>
<li>États-Unis</li>
</country>
<region>
<li>Communauté de Madrid</li>
<li>Connecticut</li>
<li>Sergipe</li>
<li>État de New York</li>
</region>
</list>
<tree>
<country name="États-Unis">
<region name="État de New York">
<name sortKey="Dharmarajan, Kumar" sort="Dharmarajan, Kumar" uniqKey="Dharmarajan K" first="Kumar" last="Dharmarajan">Kumar Dharmarajan</name>
</region>
<name sortKey="Bernheim, Susannah M" sort="Bernheim, Susannah M" uniqKey="Bernheim S" first="Susannah M" last="Bernheim">Susannah M. Bernheim</name>
<name sortKey="Drye, Elizabeth E" sort="Drye, Elizabeth E" uniqKey="Drye E" first="Elizabeth E" last="Drye">Elizabeth E. Drye</name>
<name sortKey="Horwitz, Leora I" sort="Horwitz, Leora I" uniqKey="Horwitz L" first="Leora I" last="Horwitz">Leora I. Horwitz</name>
<name sortKey="Hsieh, Angela F" sort="Hsieh, Angela F" uniqKey="Hsieh A" first="Angela F" last="Hsieh">Angela F. Hsieh</name>
<name sortKey="Kim, Nancy" sort="Kim, Nancy" uniqKey="Kim N" first="Nancy" last="Kim">Nancy Kim</name>
<name sortKey="Krumholz, Harlan M" sort="Krumholz, Harlan M" uniqKey="Krumholz H" first="Harlan M" last="Krumholz">Harlan M. Krumholz</name>
<name sortKey="Lin, Zhenqiu" sort="Lin, Zhenqiu" uniqKey="Lin Z" first="Zhenqiu" last="Lin">Zhenqiu Lin</name>
<name sortKey="Ross, Joseph S" sort="Ross, Joseph S" uniqKey="Ross J" first="Joseph S" last="Ross">Joseph S. Ross</name>
<name sortKey="Suter, Lisa G" sort="Suter, Lisa G" uniqKey="Suter L" first="Lisa G" last="Suter">Lisa G. Suter</name>
</country>
<country name="Espagne">
<region name="Communauté de Madrid">
<name sortKey="Bueno, Hector" sort="Bueno, Hector" uniqKey="Bueno H" first="Héctor" last="Bueno">Héctor Bueno</name>
</region>
</country>
<country name="Brésil">
<region name="Sergipe">
<name sortKey="Barreto Filho, Jose Augusto" sort="Barreto Filho, Jose Augusto" uniqKey="Barreto Filho J" first="José Augusto" last="Barreto-Filho">José Augusto Barreto-Filho</name>
</region>
</country>
</tree>
</affiliations>
</record>

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