Development and use of an administrative claims measure for profiling hospital-wide performance on 30-day unplanned readmission

Abstract

Background: Existing publicly reported readmission measures are condition-specific, representing less than 20% of adult hospitalizations. An all-condition measure may better measure quality and promote innovation.

Objective: To develop an all-condition, hospital-wide readmission measure.

Design: Measure development study.

Setting: 4821 U.S. hospitals.

Patients: Medicare fee-for-service beneficiaries aged 65 years or older.

Measurements: Hospital-level, risk-standardized unplanned readmissions within 30 days of discharge. The measure uses Medicare fee-for-service claims and is a composite of 5 specialty-based, risk-standardized rates for medicine, surgery/gynecology, cardiorespiratory, cardiovascular, and neurology cohorts. The 2007-2008 admissions were randomly split for development and validation. Models were adjusted for age, principal diagnosis, and comorbid conditions. Calibration in Medicare and all-payer data was examined, and hospital rankings in the development and validation samples were compared.

Results: The development data set contained 8 018 949 admissions associated with 1 276 165 unplanned readmissions (15.9%). The median hospital risk-standardized unplanned readmission rate was 15.8 (range, 11.6 to 21.9). The 5 specialty cohort models accurately predicted readmission risk in both Medicare and all-payer data sets for average-risk patients but slightly overestimated readmission risk at the extremes. Overall hospital risk-standardized readmission rates did not differ statistically in the split samples (P = 0.71 for difference in rank), and 76% of hospitals' validation-set rankings were within 2 deciles of the development rank (24% were more than 2 deciles). Of hospitals ranking in the top or bottom deciles, 90% remained within 2 deciles (10% were more than 2 deciles) and 82% remained within 1 decile (18% were more than 1 decile).

Limitation: Risk adjustment was limited to that available in claims data.

Conclusion: A claims-based, hospital-wide unplanned readmission measure for profiling hospitals produced reasonably consistent results in different data sets and was similarly calibrated in both Medicare and all-payer data.

Primary funding source: Centers for Medicare & Medicaid Services.

Figure 1

Flow diagram of inclusion and exclusion criteria applied to 2008 MedPAR data. FFS: Fee for service; PPS: prospective payment service; CCS: clinical classification software

Figure 2

Observed and predicted readmission rates for patients in each decile of predicted probability Calibration plots, by cohort, for development data set (2007–2008 split sample), Medicare 2009 data, and California 2006 all-payer data.

Figure 2

Observed and predicted readmission rates for patients in each decile of predicted probability Calibration plots, by cohort, for development data set (2007–2008 split sample), Medicare 2009 data, and California 2006 all-payer data.

Figure 2

Observed and predicted readmission rates for patients in each decile of predicted probability Calibration plots, by cohort, for development data set (2007–2008 split sample), Medicare 2009 data, and California 2006 all-payer data.

Figure 3

Agreement of development and validation risk standardized readmission rates. Plot of the difference between the development and validation risk standardized readmission rates (RSRRs) against the average of the two. Horizontal and vertical lines reflect the bounds of 95% of the hospitals. The center box is area V. Hospitals in or near areas I, III, VII, and IX reflect those institutions with extreme rates that tend to vary substantially between the two datasets.