If all hospitals were performing on par with what HealthGrades terms a five-star hospital, the study suggests the U.S. healthcare system could have saved the lives of more than 230,000 Medicare beneficiaries over the three-year period. More than half of the preventable deaths were associated with sepsis, pneumonia, respiratory failure, and heart failure.
Although the high number raises the question of whether some preventable deaths might exist only on paper, the study does raise other eye-popping calculations. Typical patients who went to a five-star hospital instead of a one-star hospital had a 72% lower risk of dying and reduced their risk by 53% compared with U.S. hospitals overall. The survival advantage persisted after hospitalization, too: Patients discharged from five-star-rated hospitals were 57% less likely to die within 30 days than all patients.
Ali Mokdad, PhD, professor of global health at the Institute for Health Metrics and Evaluation, says one big caveat to such rankings is the matter of adjusted risk. What kind of patient populations are these hospitals treating? Are people in the area inherently less healthy? Are significant barriers to healthcare blocking access to preventive medicine?
Dr. Murray says measuring quality with risk-adjusted outcomes has periodically fallen in and out of favor, due in part to concerns over how the risk is calculated and whether the assessments could be biased against providers that see more difficult patients. Nonetheless, he believes the metric is underused in the U.S. “I think the pendulum went way away from risk-adjusted outcomes to process measures too much, and we need to have a mixed combination,” he says.
With improvements to the methodology, he sees a wealth of potential in picking out risk predictors from large data sets. “The world is getting better at predicting rehospitalization, predicting death from attributes of the patient,” he says. “If you can do a better job at risk adjustment, you can do a better job on identifying quality.”
Risk Adjustment
One area in which the U.S. has lagged is in integrating the risk of death due to chronic conditions into broader measures of healthcare. At the recent Global Health Metrics & Evaluation Conference in Seattle, Dr. Mokdad pointed out the stringent oversight applied to commercial airliners. An avoidable crash and loss of life would quickly lead to a full-scale investigation. Why, he wondered, can’t the same scrutiny be brought to bear on preventable deaths due to chronic conditions such as diabetes and heart disease?
An ambitious new surveillance project, in fact, is trying to do exactly that. Known as the Monitoring Disparities in Chronic Conditions (MDCC) Study, the effort will use Washington state’s King County as a test case to hone the necessary data collection techniques. If it pans out, the study could become a national model for how to assess a population’s health status. “You know how a physician takes your pulse?” Dr. Mokdad says. “We’re doing that for the community.”
The research team, which includes Dr. Mokdad, Dr. Murray, and collaborators from Dartmouth and Harvard universities, will administer in-depth, culturally sensitive surveys to more than 3,000 county residents. A subset of 750 participants also will receive physical exams that measure markers of health and activity.
One goal is to work out how to efficiently integrate data from multiple sources so researchers can apply their risk adjustment strategies. For example, can they get enough information to ask how many heart attack patients are on beta-blockers one year after a hospital discharge? “There is also this big question of community background health risk,” Dr. Murray says. “Is this a community where people are just sicker, and how do you factor that in addition to taking into account the comorbidities that individuals have when they show up in the hospital?”