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

Identifieur interne : 000629 ( Istex/Corpus ); précédent : 000628; suivant : 000630

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

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

Source :

RBID : ISTEX:4F6D0DD339378B54C38C59AF807F93A936A7FB51

English descriptors

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

Links to Exploration step

ISTEX:4F6D0DD339378B54C38C59AF807F93A936A7FB51

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<div type="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.</div>
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<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.</abstract>
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<p>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.</p>
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<name>
<surname>Dharmarajan</surname>
<given-names>Kumar</given-names>
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<role>cardiology fellow</role>
<xref ref-type="aff" rid="aff1">1</xref>
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<name>
<surname>Hsieh</surname>
<given-names>Angela F</given-names>
</name>
<role>statistician</role>
<xref ref-type="aff" rid="aff2">2</xref>
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<name>
<surname>Lin</surname>
<given-names>Zhenqiu</given-names>
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<role>statistician</role>
<xref ref-type="aff" rid="aff2">2</xref>
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<name>
<surname>Bueno</surname>
<given-names>Héctor</given-names>
</name>
<role>associate professor of medicine</role>
<xref ref-type="aff" rid="aff3">3</xref>
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<name>
<surname>Ross</surname>
<given-names>Joseph S</given-names>
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<role>assistant professor of medicine</role>
<xref ref-type="aff" rid="aff4">4</xref>
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<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>
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<name>
<surname>Barreto-Filho</surname>
<given-names>José Augusto</given-names>
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<role>associate professor of cardiology</role>
<xref ref-type="aff" rid="aff5">5</xref>
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<name>
<surname>Kim</surname>
<given-names>Nancy</given-names>
</name>
<role>assistant professor of medicine</role>
<xref ref-type="aff" rid="aff4">4</xref>
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<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>
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<contrib contrib-type="author" corresp="no">
<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>
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<name>
<surname>Drye</surname>
<given-names>Elizabeth E</given-names>
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<role>research scientist in pediatrics</role>
<xref ref-type="aff" rid="aff2">2</xref>
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<name>
<surname>Krumholz</surname>
<given-names>Harlan M</given-names>
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<role>Harold J Hines Jr professor of medicine</role>
<xref ref-type="aff" rid="aff2">2</xref>
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<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>
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Department of Cardiology, Hospital General Universitario Gregorio Marañón, Dr Esquerdo 46 Madrid 28007, Spain</aff>
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Section of General Internal Medicine, Yale University School of Medicine, PO Box 208056, New Haven, CT 06520, USA </aff>
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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">
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Section of Rheumatology, Yale University School of Medicine, PO Box 208031, New Haven, CT 06520, USA</aff>
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<author-notes>
<corresp>Correspondence to: K Dharmarajan
<email>kumar.dharmarajan@columbia.edu</email>
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<copyright-statement>© Dharmarajan et al 2013</copyright-statement>
<copyright-year>2013</copyright-year>
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<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:
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<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>
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<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>
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<namePart type="given">Kumar</namePart>
<namePart type="family">Dharmarajan</namePart>
<affiliation>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</affiliation>
<affiliation>E-mail: kumar.dharmarajan@columbia.edu</affiliation>
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<namePart type="given">Angela F</namePart>
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<affiliation>Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT 06510, USA</affiliation>
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<affiliation>Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT 06510, USA</affiliation>
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<namePart type="given">Héctor</namePart>
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<affiliation>Department of Cardiology, Hospital General Universitario Gregorio Marañón, Dr Esquerdo 46 Madrid 28007, Spain</affiliation>
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<namePart type="given">Joseph S</namePart>
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<affiliation>Section of General Internal Medicine, Yale University School of Medicine, PO Box 208056, New Haven, CT 06520, USA</affiliation>
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<affiliation>Section of General Internal Medicine, Yale University School of Medicine, PO Box 208056, New Haven, CT 06520, USA</affiliation>
<description>assistant professor of medicine</description>
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<namePart type="given">José Augusto</namePart>
<namePart type="family">Barreto-Filho</namePart>
<affiliation>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</affiliation>
<description>associate professor of cardiology</description>
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<description>assistant professor of medicine</description>
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<namePart type="given">Lisa G</namePart>
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<affiliation>Section of Rheumatology, Yale University School of Medicine, PO Box 208031, New Haven, CT 06520, USA</affiliation>
<description>assistant professor of medicine</description>
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<namePart type="given">Susannah M</namePart>
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<affiliation>Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT 06510, USA</affiliation>
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<namePart type="given">Elizabeth E</namePart>
<namePart type="family">Drye</namePart>
<affiliation>Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT 06510, USA</affiliation>
<description>research scientist in pediatrics</description>
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<namePart type="given">Harlan M</namePart>
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<affiliation>Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT 06510, USA</affiliation>
<description>Harold J Hines Jr professor of medicine</description>
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<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.</abstract>
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