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Development and Use of an Administrative Claims Measure for Profiling Hospital-wide Performance on 30-Day Unplanned ReadmissionAdministrative Claims Measure for Profiling Hospital-Wide Performance on Readmission

Leora I. Horwitz, MD, MHS*; Chohreh Partovian, MD, PhD; Zhenqiu Lin, PhD; Jacqueline N. Grady, MS; Jeph Herrin, PhD; Mitchell Conover, BA; Julia Montague, MPH; Chloe Dillaway, BA; Kathleen Bartczak, BA; Lisa G. Suter, MD*; Joseph S. Ross, MD, MHS*; Susannah M. Bernheim, MD, MHS*; Harlan M. Krumholz, MD, SM; and Elizabeth E. Drye, MD, SM
[+] Article, Author, and Disclosure Information

* Former Robert Wood Johnson Foundation Clinical Scholar.

This article is part of the Annals supplement “RWJF Clinical Scholars in Pursuit of the Value Proposition: Evaluations of Low-Cost Innovations for Prevention and Management of Conditions.” The Robert Wood Johnson Foundation provided funding for publication of this supplement. Carol M. Mangione, MD, MPH (co-director of the RWJF Clinical Scholars Program at the University of California, Los Angeles); Jaya K. Rao, MD, MHS (Annals Deputy Editor); and Christine Laine, MD, MPH (Annals Editor in Chief), served as editors for this supplement.


From Yale School of Medicine and Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, and Health Research & Educational Trust, Chicago, Illinois.

An earlier version of this work was presented at the AcademyHealth 2012 Annual Research Meeting, Orlando, Florida, 25 June 2012.

Note: Dr. Lin had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views or policies of the U.S. Department of Health and Human Services, the National Institute on Aging, the National Heart, Lung, and Blood Institute, or the American Federation for Aging Research.

Acknowledgment: The authors thank Lein Han, PhD, and Michael Rapp, MD, for support and guidance; Sharon-Lise Normand, PhD, for statistical guidance; and Chinwe Nwosu for research assistance.

Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M13-3000.

Grant Support: This work was performed under contract HHSM-500-2008-0025I/HHSM-500-T0001, Modification no. 000008, titled “Measure Instrument Development and Support,” funded by CMS, an agency of the U.S. Department of Health and Human Services. Dr. Horwitz is supported by the National Institute on Aging (grant K08 AG038336) and by the American Federation for Aging Research through the Paul B. Beeson Career Development Award Program. Dr. Horwitz is also a Pepper Scholar with support from the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (grant P30AG021342 NIH/NIA). Dr. Ross is supported by the National Institute on Aging (grant K08 AG032886) and by the American Federation for Aging Research through the Paul B. Beeson Career Development Award Program. Dr. Krumholz is supported by grant U01 HL105270-03 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute.

Reproducible Research Statement:Study protocol and data set: Not available. Statistical code: Available from cmsreadmissionmeasures@yale.edu.

Requests for Single Reprints: Leora I. Horwitz, MD, MHS, Department of Population Health, New York University School of Medicine, 550 First Ave, TRB Room 607, New York, NY 10016; e-mail, leora.horwitz@nyumc.org.

Current Author Addresses: Dr. Horwitz: Department of Population Health, New York University School of Medicine, 550 First Ave, TRB Room 607, New York, NY 10016.

Dr. Partovian: Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 109 Church Street, Unit 607, New Haven, CT 06510.

Drs. Lin, Suter, Bernheim, Krumholz, and Drye; Ms. Grady; and Ms. Montague: Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT 06510.

Dr. Herrin: PO Box 2254, Charlottesville, VA 22902.

Mr. Conover: 1227 Northcreek Drive, Durham, NC 27707.

Ms. Dillaway: 10300 Jollyville Road, #1031, Austin, TX 78759.

Ms. Bartczak: ul. Racławicka 9a, 30-075 Krakow, Poland.

Dr. Ross: Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, PO Box 208093, New Haven, CT 06520.

Author Contributions: Conception and design: L.I. Horwitz, C. Partovian, Z. Lin, M. Conover, J. Montague, C. Dillaway, L.G. Suter, J.S. Ross, S.M. Bernheim, H.M. Krumholz, E.E. Drye.

Analysis and interpretation of the data: L.I. Horwitz, C. Partovian, Z. Lin, J.N. Grady, J. Herrin, M. Conover, J. Montague, C. Dillaway, L.G. Suter, J.S. Ross, H.M. Krumholz, E.E. Drye.

Drafting of the article: L.I. Horwitz, J. Herrin.

Critical revision of the article for important intellectual content: L.I. Horwitz, C. Partovian, Z. Lin, J. Herrin, M. Conover, J. Montague, C. Dillaway, L.G. Suter, J.S. Ross, S.M. Bernheim, H.M. Krumholz, E.E. Drye.

Final approval of the article: L.I. Horwitz, C. Partovian, Z. Lin, J. Herrin, M. Conover, J. Montague, C. Dillaway, L.G. Suter, S.M. Bernheim, H.M. Krumholz, E.E. Drye.

Provision of study materials or patients: H.M. Krumholz.

Statistical expertise: Z. Lin, J.N. Grady, J. Herrin.

Obtaining of funding: H.M. Krumholz, E.E. Drye.

Administrative, technical, or logistic support: J.N. Grady, M. Conover, J. Montague, K. Bartczak.

Collection and assembly of data: Z. Lin, J.N. Grady.


Ann Intern Med. 2014;161(10_Supplement):S66-S75. doi:10.7326/M13-3000
Text Size: A A A

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.

Figures

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Figure 1.

Study flow diagram.

CCS = clinical classification software; FFS = fee for service; PPS = prospective payment system.

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Figure 2.

Calibration plots, by cohort, for development data set (2007–2008 split sample), Medicare 2009 data, and California 2006 all-payer data.

Boxes represent predicted probability, and diamonds represent observed predictability.

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Figure 3.

Difference between the development and validation RSRRs, plotted against the average of the two.

Horizontal and vertical lines show the bounds of 95% of the hospitals. The center box is area V. Hospitals in or near areas I, III, VII, and IX had extreme rates that tended to vary substantially between the 2 data sets. RSRR = risk-standardized readmission rate.

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