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Predicting 30-day all-cause hospital readmissions

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  • Mollie Shulan
  • Kelly Gao
  • Crystal Moore

Abstract

Hospital readmission rate has been broadly accepted as a quality measure and cost driver. However, success in reducing readmissions has been elusive. In the US, almost 20 % of Medicare inpatients are rehospitalized within 30 days, which amounts to a cost of $17 billion. Given the skyrocketing healthcare cost, policymakers, researchers and payers are focusing more than ever on readmission reduction. Both hospital comparison of readmissions as a quality measure and identification of high-risk patients for post-discharge interventions require accurate predictive modeling. However, most predictive models for readmissions perform poorly. In this study, we endeavored to explore the full potentials of predictive models for readmissions and to assess the predictive power of different independent variables. Our model reached the highest predicting ability (c-statistic =0.80) among all published studies that used administrative data. Our analyses reveal that demographics, socioeconomic variables, prior utilization and Diagnosis-related Group (DRG) all have limited predictive power; more sophisticated patient stratification algorithm or risk adjuster is desired for more accurate readmission predictions. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Mollie Shulan & Kelly Gao & Crystal Moore, 2013. "Predicting 30-day all-cause hospital readmissions," Health Care Management Science, Springer, vol. 16(2), pages 167-175, June.
  • Handle: RePEc:kap:hcarem:v:16:y:2013:i:2:p:167-175
    DOI: 10.1007/s10729-013-9220-8
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    References listed on IDEAS

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    1. David Anderson & Bruce Golden & Wolfgang Jank & Edward Wasil, 2012. "The impact of hospital utilization on patient readmission rate," Health Care Management Science, Springer, vol. 15(1), pages 29-36, March.
    2. Westert, Gert P. & Lagoe, Ronald J. & Keskimaki, Ilmo & Leyland, Alastair & Murphy, Mark, 2002. "An international study of hospital readmissions and related utilization in Europe and the USA," Health Policy, Elsevier, vol. 61(3), pages 269-278, September.
    3. David Anderson & Carter Price & Bruce Golden & Wolfgang Jank & Edward Wasil, 2011. "Examining the discharge practices of surgeons at a large medical center," Health Care Management Science, Springer, vol. 14(4), pages 338-347, November.
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    Cited by:

    1. Suiyao Chen & Nan Kong & Xuxue Sun & Hongdao Meng & Mingyang Li, 2019. "Claims data-driven modeling of hospital time-to-readmission risk with latent heterogeneity," Health Care Management Science, Springer, vol. 22(1), pages 156-179, March.
    2. Claudia Fischer & Hester F Lingsma & Perla J Marang-van de Mheen & Dionne S Kringos & Niek S Klazinga & Ewout W Steyerberg, 2014. "Is the Readmission Rate a Valid Quality Indicator? A Review of the Evidence," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-9, November.
    3. Rita Hamad & Sepideh Modrek & Jessica Kubo & Benjamin A Goldstein & Mark R Cullen, 2015. "Using “Big Data” to Capture Overall Health Status: Properties and Predictive Value of a Claims-Based Health Risk Score," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-14, May.
    4. Issac Shams & Saeede Ajorlou & Kai Yang, 2015. "A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD," Health Care Management Science, Springer, vol. 18(1), pages 19-34, March.
    5. Jacek Kryś & Błażej Łyszczarz & Zofia Wyszkowska & Kornelia Kędziora-Kornatowska, 2019. "Prevalence, Reasons, and Predisposing Factors Associated with 30-day Hospital Readmissions in Poland," IJERPH, MDPI, vol. 16(13), pages 1-14, July.
    6. Francesca Ieva & Anna Maria Paganoni & Teresa Pietrabissa, 2017. "Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure," Health Care Management Science, Springer, vol. 20(3), pages 353-364, September.

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