title: Limitations of using same-hospital readmission metrics.
abstract: OBJECTIVE
  To quantify the limitations associated with restricting readmission metrics to same-hospital only readmission.
  
  
  METHODS
  Using 2000-2009 California Office of Statewide Health Planning and Development Patient Discharge Data Nonpublic file, we identified the proportion of 7-, 15- and 30-day readmissions occurring to the same hospital as the initial admission using All-cause Readmission (ACR) and 3M Corporation Potentially Preventable Readmissions (PPR) Metric. We examined the correlation between performance using same and different hospital readmission, the percent of hospitals remaining in the extreme deciles when utilizing different metrics, agreement in identifying outliers and differences in longitudinal performance. Using logistic regression, we examined the factors associated with admission to the same hospital.
  
  
  RESULTS
  68% of 30-day ACR and 70% of 30-day PPR occurred to the same hospital. Abdominopelvic procedures had higher proportions of same-hospital readmissions (87.4-88.9%), cardiac surgery had lower (72.5-74.9%) and medical DRGs were lower than surgical DRGs (67.1 vs. 71.1%). Correlation and agreement in identifying high- and low-performing hospitals was weak to moderate, except for 7-day metrics where agreement was stronger (r = 0.23-0.80, Kappa = 0.38-0.76). Agreement for within-hospital significant (P < 0.05) longitudinal change was weak (Kappa = 0.05-0.11). Beyond all patient refined-diagnostic related groups, payer was the most predictive factor with Medicare and MediCal patients having a higher likelihood of same-hospital readmission (OR 1.62, 1.73).
  
  
  CONCLUSIONS
  Same-hospital readmission metrics are limited for all tested applications. Caution should be used when conducting research, quality improvement or comparative applications that do not account for readmissions to other hospitals.
authors.affiliation: 117 Encina Commons, Stanford, CA 94305. smdavies@stanford.edu.
authors.forename: Sheryl M
authors.initials: SM
authors.lastname: Davies
authors.forename: Olga
authors.initials: O
authors.lastname: Saynina
authors.forename: Kathryn M
authors.initials: KM
authors.lastname: McDonald
authors.forename: Laurence C
authors.initials: LC
authors.lastname: Baker
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002140:California / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data; D005260:Female; D006761:Hospitals / Q000592:standards / Q000706:statistics & numerical data*; D006801:Humans; D007348:Insurance, Health / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D008875:Middle Aged; D010359:Patient Readmission / Q000706:statistics & numerical data*; D019984:Quality Indicators, Health Care / Q000592:standards / Q000706:statistics & numerical data; D020379:Risk Adjustment; D014481:United States; D055815:Young Adult
keywords: hospital quality; quality indicators; readmissions