Initiatives to promote performance measurement need to be accompanied by support to improve care. Quality measure data should not only be technically correct, but should be organized such that their dissemination is a resource to aid in quality improvement activities. As such, quality measurement should be viewed as just one component of a learning health care system that also includes advancing the science of quality improvement, building providers’ capacity to improve care, transparently reporting performance, and creating formal accountability systems.
There are several strategies to make quality measure data more actionable for quality improvement purposes. For example, for publicly reported outcome measures, CMS provides hospitals with lists of the patients who are included in the calculation. Since the outcomes may occur outside the hospital for mortality and for readmissions that are at other hospitals, this information is often beyond what the hospitals already have available to them. These data give providers the ability to investigate care provided to individual patients, which in turn can support a variety of quality improvement efforts.
In addition, collaborative activities among institutions can produce insights that may elude them individually. Measures can help identify top performers, and detailed analysis can identify what distinguishes those who excel. As an example, the marked improvement nationwide in the “door-to-balloon” time it takes patients experiencing symptoms of a heart attack to receive a treatment to open up occluded coronary arteries was largely a result of relevant and valid measurement of provider-specific timeliness, followed by intense investigation of the features of top performance, and only then a national campaign to transform practice using the best practices uncovered by the top performers – all facilitated by the intrinsic motivation of health professionals on the front lines to improve patient outcomes.
To facilitate a learning health care system, investments are also needed to advance quality improvement sciences and to build capacity among provider organizations to practice these sciences. For example, although root causes analysis is a promising tool, its full potential has not been realized in health care; a likely explanation, at least in part, is that health care is one of the only risky industries in which lawyers and practitioners, rather than safety experts with formal training, investigate adverse events. Promising efforts to improve quality and safety are based on adherence to professional norms and include peer-to-peer review, a technique borrowed from the nuclear industry [27]. In addition, EHR vendors and other medical device manufacturers will need to agree to share their data and open it for analysis.
Robert A. Berenson, MD is an institute fellow at the Urban Institute.
Peter J. Pronovost, MD, PhD is the director of the Armstrong Institute for Patient Safety and Quality at Johns Hopkins, as well as Johns Hopkins Medicine’s senior vice president for patient safety and quality.
Harlan M. Krumholz, MD, is the director of the Yale-New Haven Hospital Center for Outcomes Research and Evaluation, director of the Robert Wood Johnson Foundation Clinical Scholars program at Yale University, and the Harold H. Hines, Jr. professor of cardiology, investigative medicine, and public health.
The authors thank Lawrence Casalino, MD, PhD, chief of the Division of Outcomes and Effectiveness Research and an associate professor at Weill Cornell Medical College, and Andrea Ducas, MPH and Anne Weiss, MPP of the Robert Wood Johnson Foundation for their helpful comments on this paper. This research was funded by theRobert Wood Johnson Foundation, where the report was originally published.
Notes
27. Pronovost and Hudson, 2012.
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