A meta-analysis of hospital 30-day avoidable readmission rates

J Eval Clin Pract. 2012 Dec;18(6):1211-8. doi: 10.1111/j.1365-2753.2011.01773.x. Epub 2011 Nov 9.


Rationale and objectives: Urgent readmission to hospital is commonly used to measure hospital quality of care. Hospitals that measure the proportion of urgent readmissions judged avoidable need to know previously published rates for comparison. In this study, we generated a literature-based estimate for the proportion of 30-day urgent readmissions deemed avoidable for hospitals to use to gauge their performance in avoidable readmissions.

Methods: We searched the Medline and Embase databases to identify published studies that reported the proportion of 30-day urgent readmissions deemed avoidable. We then modelled the overall proportion of 30-day urgent readmissions deemed avoidable.

Results: We included 16 studies that used a wide variety of patients and a diverse range of methods to classify readmissions as avoidable. Studies reported a broad range for the proportion of urgent 30-day readmissions deemed avoidable. Overall, 848 of 3669 readmissions (23.1%, 95% confidence interval, 21.7-24.5) of 30-day urgent readmissions were classified as avoidable. This proportion varied significantly based on hospital teaching status and number of reviewers for each case [teaching hospitals: with one reviewer, 9.3% (4.2-19.3); with >1 reviewer, 21.6% (13.2-33.3); non-teaching hospital: with one reviewer, 32.2% (11.4-63.9); with >1 reviewer, 39.9% (37.6-42.2)]. Significant heterogeneity remained between studies even after clustering studies by these covariates.

Conclusions: Less than one in four readmissions were deemed avoidable. Health system planners need to use caution in interpreting all cause readmission statistics as they are only partially influenced by quality of care.

Publication types

  • Meta-Analysis

MeSH terms

  • Hospital Administration / statistics & numerical data*
  • Humans
  • Patient Readmission / statistics & numerical data*
  • Quality of Health Care / statistics & numerical data
  • Time Factors