Literature DB >> 25402406

Development and use of an administrative claims measure for profiling hospital-wide performance on 30-day unplanned readmission.

Leora I Horwitz, Chohreh Partovian, Zhenqiu Lin, Jacqueline N Grady, Jeph Herrin, Mitchell Conover, Julia Montague, Chloe Dillaway, Kathleen Bartczak, Lisa G Suter, Joseph S Ross, Susannah M Bernheim, Harlan M Krumholz, Elizabeth E Drye.   

Abstract

BACKGROUND: Existing publicly reported readmission measures are condition-specific, representing less than 20% of adult hospitalizations. An all-condition measure may better measure quality and promote innovation.
OBJECTIVE: To develop an all-condition, hospital-wide readmission measure.
DESIGN: Measure development study.
SETTING: 4821 U.S. hospitals. PATIENTS: Medicare fee-for-service beneficiaries aged 65 years or older. MEASUREMENTS: Hospital-level, risk-standardized unplanned readmissions within 30 days of discharge. The measure uses Medicare fee-for-service claims and is a composite of 5 specialty-based, risk-standardized rates for medicine, surgery/gynecology, cardiorespiratory, cardiovascular, and neurology cohorts. The 2007-2008 admissions were randomly split for development and validation. Models were adjusted for age, principal diagnosis, and comorbid conditions. Calibration in Medicare and all-payer data was examined, and hospital rankings in the development and validation samples were compared.
RESULTS: The development data set contained 8 018 949 admissions associated with 1 276 165 unplanned readmissions (15.9%). The median hospital risk-standardized unplanned readmission rate was 15.8 (range, 11.6 to 21.9). The 5 specialty cohort models accurately predicted readmission risk in both Medicare and all-payer data sets for average-risk patients but slightly overestimated readmission risk at the extremes. Overall hospital risk-standardized readmission rates did not differ statistically in the split samples (P = 0.71 for difference in rank), and 76% of hospitals' validation-set rankings were within 2 deciles of the development rank (24% were more than 2 deciles). Of hospitals ranking in the top or bottom deciles, 90% remained within 2 deciles (10% were more than 2 deciles) and 82% remained within 1 decile (18% were more than 1 decile). LIMITATION: Risk adjustment was limited to that available in claims data.
CONCLUSION: A claims-based, hospital-wide unplanned readmission measure for profiling hospitals produced reasonably consistent results in different data sets and was similarly calibrated in both Medicare and all-payer data. PRIMARY FUNDING SOURCE: Centers for Medicare & Medicaid Services.

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Year:  2014        PMID: 25402406      PMCID: PMC4235629          DOI: 10.7326/M13-3000

Source DB:  PubMed          Journal:  Ann Intern Med        ISSN: 0003-4819            Impact factor:   25.391


  28 in total

1.  Changes in rates of unscheduled hospital readmissions and changes in efficiency following the introduction of the Medicare prospective payment system. An analysis using risk-adjusted data.

Authors:  S DesHarnais; A J Hogan; L F McMahon; S Fleming
Journal:  Eval Health Prof       Date:  1991-06       Impact factor: 2.651

2.  Standards for statistical models used for public reporting of health outcomes: an American Heart Association Scientific Statement from the Quality of Care and Outcomes Research Interdisciplinary Writing Group: cosponsored by the Council on Epidemiology and Prevention and the Stroke Council. Endorsed by the American College of Cardiology Foundation.

Authors:  Harlan M Krumholz; Ralph G Brindis; John E Brush; David J Cohen; Andrew J Epstein; Karen Furie; George Howard; Eric D Peterson; Saif S Rathore; Sidney C Smith; John A Spertus; Yun Wang; Sharon-Lise T Normand
Journal:  Circulation       Date:  2005-12-19       Impact factor: 29.690

3.  Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care.

Authors:  Patricia Halfon; Yves Eggli; Isaline Prêtre-Rohrbach; Danielle Meylan; Alfio Marazzi; Bernard Burnand
Journal:  Med Care       Date:  2006-11       Impact factor: 2.983

4.  Measuring outcomes of hospital care using multiple risk-adjusted indexes.

Authors:  S DesHarnais; L F McMahon; R Wroblewski
Journal:  Health Serv Res       Date:  1991-10       Impact factor: 3.402

5.  Quality of care information makes a difference: an analysis of market share and price changes after publication of the New York State Cardiac Surgery Mortality Reports.

Authors:  D B Mukamel; A I Mushlin
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6.  Measuring readmission rates.

Authors:  M Chambers; A Clarke
Journal:  BMJ       Date:  1990-11-17

7.  The care transitions intervention: results of a randomized controlled trial.

Authors:  Eric A Coleman; Carla Parry; Sandra Chalmers; Sung-Joon Min
Journal:  Arch Intern Med       Date:  2006-09-25

8.  Application of an analytic model to early readmission rates within the Department of Veterans Affairs.

Authors:  N P Wray; N J Peterson; J Souchek; C M Ashton; J C Hollingsworth
Journal:  Med Care       Date:  1997-08       Impact factor: 2.983

9.  Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial.

Authors:  M D Naylor; D Brooten; R Campbell; B S Jacobsen; M D Mezey; M V Pauly; J S Schwartz
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10.  Risk adjustment of Medicare capitation payments using the CMS-HCC model.

Authors:  Gregory C Pope; John Kautter; Randall P Ellis; Arlene S Ash; John Z Ayanian; Lisa I Lezzoni; Melvin J Ingber; Jesse M Levy; John Robst
Journal:  Health Care Financ Rev       Date:  2004
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  38 in total

1.  Reliability of 30-Day Readmission Measures Used in the Hospital Readmission Reduction Program.

Authors:  Michael P Thompson; Cameron M Kaplan; Yu Cao; Gloria J Bazzoli; Teresa M Waters
Journal:  Health Serv Res       Date:  2016-10-21       Impact factor: 3.402

2.  Hospital Characteristics Associated With Risk-standardized Readmission Rates.

Authors:  Leora I Horwitz; Susannah M Bernheim; Joseph S Ross; Jeph Herrin; Jacqueline N Grady; Harlan M Krumholz; Elizabeth E Drye; Zhenqiu Lin
Journal:  Med Care       Date:  2017-05       Impact factor: 2.983

3.  Predicting all-cause readmissions using electronic health record data from the entire hospitalization: Model development and comparison.

Authors:  Oanh Kieu Nguyen; Anil N Makam; Christopher Clark; Song Zhang; Bin Xie; Ferdinand Velasco; Ruben Amarasingham; Ethan A Halm
Journal:  J Hosp Med       Date:  2016-02-29       Impact factor: 2.960

4.  Differences in Hospital Readmission Risk across All Payer Groups in South Carolina.

Authors:  Hrishikesh Chakraborty; Robert Neal Axon; Jordan Brittingham; Genevieve Ray Lyons; Laura Cole; Christine B Turley
Journal:  Health Serv Res       Date:  2016-09-28       Impact factor: 3.402

5.  40 years of training physician-scientists: a journey from clinical pearls to evidence-based practice and policies.

Authors:  Carol M Mangione; Lee Goldman
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6.  Seasonal Variation in Readmission Risk for Patients Hospitalized with Cardiopulmonary Conditions.

Authors:  Saul Blecker; Ji Young Kwon; Jeph Herrin; Jacqueline N Grady; Leora I Horwitz
Journal:  J Gen Intern Med       Date:  2018-05       Impact factor: 5.128

7.  Trends in Hospital Readmission of Medicare-Covered Patients With Heart Failure.

Authors:  Saul Blecker; Jeph Herrin; Li Li; Huihui Yu; Jacqueline N Grady; Leora I Horwitz
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8.  Rethinking Thirty-Day Hospital Readmissions: Shorter Intervals Might Be Better Indicators Of Quality Of Care.

Authors:  David L Chin; Heejung Bang; Raj N Manickam; Patrick S Romano
Journal:  Health Aff (Millwood)       Date:  2016-10-01       Impact factor: 6.301

9.  Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data.

Authors:  Leora I Horwitz; Jacqueline N Grady; Dorothy B Cohen; Zhenqiu Lin; Mark Volpe; Chi K Ngo; Andrew L Masica; Theodore Long; Jessica Wang; Megan Keenan; Julia Montague; Lisa G Suter; Joseph S Ross; Elizabeth E Drye; Harlan M Krumholz; Susannah M Bernheim
Journal:  J Hosp Med       Date:  2015-07-07       Impact factor: 2.960

10.  Hospital-Readmission Risk - Isolating Hospital Effects from Patient Effects.

Authors:  Harlan M Krumholz; Kun Wang; Zhenqiu Lin; Kumar Dharmarajan; Leora I Horwitz; Joseph S Ross; Elizabeth E Drye; Susannah M Bernheim; Sharon-Lise T Normand
Journal:  N Engl J Med       Date:  2017-09-14       Impact factor: 91.245

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