Volume 48 | Number 6pt1 | December 2013

Abstract List

Sheryl Davies M.A., Olga Saynina, Ellen Schultz M.S., Kathryn M. McDonald M.M., Laurence C. Baker


To quantify the differential impact on hospital performance of three readmission metrics: all‐cause readmission (), 3M Potential Preventable Readmission (), and enters for edicare and edicaid 30‐day readmission ().

Data Sources

2000–2009 alifornia Office of Statewide Health Planning and Development Patient Discharge Data Nonpublic file.

Study Design

We calculated 30‐day readmission rates using three metrics, for three disease groups: heart failure (), acute myocardial infarction (), and pneumonia. Using each metric, we calculated the absolute change and correlation between performance; the percent of hospitals remaining in extreme deciles and level of agreement; and differences in longitudinal performance.

Principal Findings

Average hospital rates for patients and the metric were generally higher than for other conditions and metrics. Correlations between the and metrics were highest ( = 0.67–0.84). Rates calculated using the and either or metrics were moderately correlated ( = 0.50–0.67). Between 47 and 75 percent of hospitals in an extreme decile according to one metric remained when using a different metric. Correlations among metrics were modest when measuring hospital longitudinal change.


Different approaches to computing readmissions can produce different hospital rankings and impact pay‐for‐performance. Careful consideration should be placed on readmission metric choice for these applications.