Skip to main content

Advertisement

Log in

Predicting 30-day all-cause hospital readmissions

  • Published:
Health Care Management Science Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Jencks SF, Williams MV, Coleman EA (2009) Rehospitalizations among patients in the Medicare fee-for service program. N Engl J Med 360:1418–1428

    Article  Google Scholar 

  2. Jha AK, Orav EJ, Epstein AM (2009) Public reporting of discharge planning and rates of readmissions. N Engl J Med 361:2637–2645

    Article  Google Scholar 

  3. Hernandez AF, Greiner MA, Fonarow GC, Hammill BG, Heidenreich PA, Yancy CW, Peterson ED, Curtis LH (2010) Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA 303(17):1716–1722

    Article  Google Scholar 

  4. Pizer SD (2013) Should hospital readmissions be reduced through payment penalties? Med Care 51(1):20–22

    Article  Google Scholar 

  5. Monette M (2012) Hospital readmission rates under the microscope. Can Med Assoc 184(12):E651–E652

    Article  Google Scholar 

  6. Westert GP, Lagoe RJ, Keskimäki I, Leyland A, Murphy M (2002) An international study of hospital readmissions and related utilization in Europe and the USA. Health Policy 61(3):269–278

    Article  Google Scholar 

  7. Snow V, Beck D, Budnitz T, Miller DC, Potter J, Wears RL, Weiss KB, Williams MV (2009) Transitions of care consensus policy statement: American College of Physicians, Society of General Internal Medicine, Society of Hospital Medicine, American Geriatrics Society, American College of Emergency Physicians, and Society for Academic Emergency Medicine. J Gen Intern Med 24(8):971–976

    Article  Google Scholar 

  8. Canadian Institute for Health Information (2012) All-cause readmission to acute care and return to the emergence department https://secure.cihi.ca/estore/productFamily.htm?locale=en&pf=PFC1823. Accessed 11 January 2013

  9. Medicare Payment Advisory Commission (2008) Report to the congress: reforming the delivery system. MedPAC, Washington

    Google Scholar 

  10. Averill RF, McCullough EC, Hughes JS, Goldfield NI, Vertrees JC, Fuller RL (2009) Redesigning the Medicare inpatient PPS to reduce payments to hospitals with high readmission rates. Health Care Financing Rev 30(4):1–15

    Google Scholar 

  11. Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, Kripalani S (2011) Risk prediction models for hospital readmission, a systematic review. JAMA 306(15):1688–1698

    Article  Google Scholar 

  12. Amarasingham R, Moore BJ, Tabak YP et al (2010) An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Med Care 48(11):981–988

    Article  Google Scholar 

  13. Coleman EA, Min SJ, Chomiak A, Kramer AM (2004) Post hospital care transitions: patterns, complications, and risk identification. Heal Serv Res 39(5):1449–1465

    Article  Google Scholar 

  14. Stroupe KT, Tarlov E, Zhang Q, Haywood T, Owens A, Hynes DM (2010) Use of Medicare and DoD data for improving VA race data quality. J Rehabil Res Dev 47(8):781–795

    Article  Google Scholar 

  15. Trivedi AN, Crebia RC, Wright SM, Washinton DL (2011) Despite improved quality of care in the veterans affairs health system, racial disparity persists for important clinical outcomes. Heal Aff 4:707–715

    Article  Google Scholar 

  16. DxCG. http://www.veriskhealth.com/content/verisk-health-sightlines-dxcg-risk-solutions?gclid. Accessed 11 January 2013

  17. Ellis RP, Ash A (1995) Refinements to the diagnostic cost group (DCG) model. Inquiry 32:418–429

    Google Scholar 

  18. Liu CF, Sales AE, Sharp ND, Fishman P, Sloan KL, Todd-Stenberg J, Nichol WP, Rosen AK, Loveland S (2003) Case-mix adjusting performance measures in a veteran population: pharmacy- and diagnosis-based approaches. Heal Serv Res 38(5):1319–1338

    Article  Google Scholar 

  19. Sales AE, Liu CF, Sloan KL, Malkin J, Fishman PA, Rosen AK, Loveland S, Nichol W, Suzuki NT, Perrin E, Sharp ND, Todd-Stenberg J (2003) Predicting costs of care using a pharmacy-based measure risk adjustment in a veteran population. Med Care 41(6):753–760

    Google Scholar 

  20. Zhao Y, Ash AS, Ellis RP, Ayanian JZ, Pope GC, Bowen B, Weyuker L (2005) Predicting pharmacy costs and other medical costs using diagnoses and drug claims. Med Care 43(1):34–43

    Google Scholar 

  21. Joynt KE, Orav EJ, Jha AK (2011) Thirty-day readmission rates for Medicare beneficiaries by race and site of care. JAMA 305:675–681

    Article  Google Scholar 

  22. McHugh MD, Margo Brooks Carthon J, Kang XL (2010) Medicare readmissions policies and racial and ethnic health disparities: a cautionary tale. Policy Polit Nurs Pract 11(4):309–316

    Article  Google Scholar 

  23. Rathore SS, Foody JM, Wang Y, Smith GL, Herrin J, Masoudi FA, Wolfe P, Havranek EP, Ordin DL, Krumholz HM (2003) Race, quality of care, and outcomes of elderly patients hospitalized with heart failure. JAMA 289:2517–2524

    Article  Google Scholar 

  24. Luthi JC, Lund MJ, Sampietro-Colom L, Kleinbaum DG, Ballard DJ, McClellan WM (2003) Readmissions and the quality of care in patients hospitalized with heart failure. Int J Qual Healthcare 15(5):413–421

    Article  Google Scholar 

  25. Harrell FE, Lee KL, Mark DB (1996) Tutorial in biostatistics multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387

    Article  Google Scholar 

  26. Efron B (1983) Estimating the error rate of a prediction rule: improvement on cross-validation. J Am Stat Assoc 78:316–331

    Article  Google Scholar 

  27. Ross JS, Mulvey GK, Stauffer B et al (2008) Statistical models and patient predictors of readmission for heart failure: a systematic review. Arch Intern Med 168(13):1371–1386

    Article  Google Scholar 

  28. Joynt KE, Jha AK (2011) Who has higher readmission rates for heart failure, and why? Implications for efforts to improve care using financial incentives. Circ Cardiovasc Qual Outcomes 4(1):53–59

    Article  Google Scholar 

  29. Williams S, Bottle A, Aylin P (2005) Length of hospital stay and subsequent emergency readmission. BMJ 331:371

    Article  Google Scholar 

  30. Maddala GS (1992) Introduction to Econometrics, 2nd edn. Macmillan

  31. CCS. http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed 11 January 2013

  32. CMS HCC. http://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/Risk_adjustment_prior.html. Accessed 11 January 2013

  33. Anderson D, Price C, Golden B, Jank W, Wasil E (2011) Examining the discharge practices of surgeons at a large medical center. Health Care Manag Sci 14(4):338–347

    Article  Google Scholar 

  34. Anderson D, Golden B, Jank W, Wasil E (2012) The impact of hospital utilization on patient readmission rate. Health Care Manag Sci 15:29–36

    Article  Google Scholar 

Download references

Acknowledgments

This material is based upon work supported in part by the Office of Research and Development, Department of Veterans Affairs. The authors wish to thank an anonymous statistician for her thorough statistical support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mollie Shulan.

Additional information

MSC: 62P10

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shulan, M., Gao, K. & Moore, C.D. Predicting 30-day all-cause hospital readmissions. Health Care Manag Sci 16, 167–175 (2013). https://doi.org/10.1007/s10729-013-9220-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10729-013-9220-8

Keywords

Navigation