The Measure and Mismeasure of Hospital Quality: Appropriate Risk-Adjustment Methods in Comparing Hospitals

Hospital quality assessment is a growth industry. Initial attention has focused on mortality rates as a measure of quality. Since the Medicare mortality rates were first released in 1986 [1], increasingly numerous and various parties have compiled actual and expected hospital mortality rates to motivate improvements in quality and to compare hospitals. The federal government, state governments, peer review organizations, private insurers, and hospital associations have been among those producing such assessments. Lately, large employers have also become interested in assessing hospital quality [2]. Concerned about the value they receive for dollars invested in employee health benefit packages, companies are taking decisions about the buying of health care into their own hands, as Localio and colleagues describe in this issue [3].

In general, physicians and hospital administrators react defensively to the concept that medical outcomes should be measured, and they worry about the consequences of these data being made public [4]. In particular, they worry about being discriminated against. These worries are not unfounded, because perceptions of quality appear to play a role in choice of hospital [5]; hospitals with high mortality rates can expect not only negative publicity but also fewer patients in the future. But is a hospital designated as a high-mortality outlier because of deficiencies in quality or because of the characteristics of the patient populations it serves? Although most physicians are unfamiliar with methods of risk adjustment and are skeptical about their accuracy, it is increasingly important that clinicians understand these methods.

Outcomes of care in hospitals are a function of patient characteristics, quality of care, and chance. When hospitals differ, it is important to know how much of the variation results from differences in severity of illness, how much from quality of care, and how much from random events. A …