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Assessing the Accuracy of Hospital Clinical Performance Measures

Department of Health Care Policy, Harvard Medical School, Department of Biostatistics, Harvard School of Public Health, Brigham and Women’s Hospital, Boston, Massachusetts, sharon{at}hcp.med.harvard.edu

Robert E. Wolf, MS

Department of Health Care Policy, Harvard Medical School, Brigham and Women’s Hospital, Boston, Massachusetts

John Z. Ayanian, MD, MPP

Department of Health Care Policy, Harvard Medical School, Division of General Medicine, Brigham and Women’s Hospital, Boston, Massachusetts

Barbara J. McNeil, MD, PhD

Department of Health Care Policy, Harvard Medical School, Department of Radiology, Brigham and Women’s Hospital, Boston, Massachusetts

Objective. To control costs and improve quality, payers are designing new hospital reimbursement policies that link payment to quality. The authors determine the extent to which quality measures discriminate hospitals into tiers in 2 geographic areas.

Data Sources. Administrative and medical record data for patients discharged with acute myocardial infarction (AMI) in 368 California and 81 Massachusetts hospitals.

Methods. Through simulation, the minimum numbers of patients per hospital needed to identify highquality hospitals with sensitivity ranging from 75% to 95% under a variety of clinical scenarios are determined.

Results. Massachusetts hospitals had twice the number of eligible patients per hospital than California hospitals. Regardless of state, few hospitals had sufficient sample size needed to achieve >85% sensitivity for high-variation quality measures.

Clinical Implications. Reliability of quality-based reimbursement systems relies on the distribution of the hospital sample sizes within geographic areas and the size of practice differences. Selection of conformance thresholds and sensitivity levels depends on the user of the information. To assess the usefulness of performance measures to tier hospitals, information regarding between-hospital variation in quality for specific clinical measures needs to be collected and reported.

Key Words: sensitivity • performance measurement • simulation • sample size • AMI

Medical Decision Making, Vol. 27, No. 1, 9-20 (2007)
DOI: 10.1177/0272989X06298028


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