[Editors Note: Elizabeth McGlynn, Kaiser Permanente, Timothy Ferris, Partners HealthCare, Robert Galvin, The Blackstone Group, Rebekah Gee, Louisiana Department of Health and Hospitals, and Eve Kerr, University of Michigan/VA Ann Arbor Healthcare System, will discuss innovative approaches to quality measurement, leveraging new sources and methods of acquiring data, and harmonizing measurement efforts across payers and organizations at the 2015 AcademyHealth National Health Policy Conference on February 9, 2015. Learn more and register here. Online registration ends January 30.]
I have written many times before, both here and in other media, about how pay for performance is failing to live up to its promise. That doesn’t mean that we can’t identify areas where things could be improved. Two recent Perspectives pieces in the NEJM can help us with this effort.
The first, “Getting More Performance from Performance Measurement”, identifies some important barriers to success:
Many observers fear that a proliferation of measures is leading to measurement fatigue without commensurate results. An analysis of 48 state and regional measure sets found that they included more than 500 different measures, only 20% of which were used by more than one program. Similarly, a study of 29 private health plans identified approximately 550 distinct measures, which overlapped little with the measures used by public programs.
There are so many metrics that systems must measure that they must devote huge numbers of resources to gathering them. Moreover, as we’ve discussed before, many of those metrics are not associated with actual outcomes. Therefore, by definition, collecting many of them may be wasteful.
Moreover, some metrics can backfire:
One example of a measurement effort that had unintended consequences was the CMS quality measure for community-acquired pneumonia. This metric assessed whether providers administered the first dose of antibiotics to a patient within 6 hours after presentation, since analyses of Medicare databases had shown that an interval exceeding 4 hours was associated with increased in-hospital mortality. But the measure led to inappropriate antibiotic use in patients without community-acquired pneumonia, had adverse consequences such as Clostridium difficile colitis, and did not reduce mortality.
Not all actions are entirely beneficial. In this case, encouraging antibiotics, and making it a quality metric, led to an inappropriate overuse of antibiotics, and even led to an increase in C. diff colitis. The take-home message of this piece is that we should think carefully about metrics. Only those with proven outcome association should be used. Others consume money and time needlessly and can lead to worsening quality.
The second piece, “Reimagining Quality Measurement”, takes this and goes a bit further:
A fruitful alternative approach, in our view, would be guided by three principles: quality measurement should be integrated with care delivery rather than existing as a parallel, separate enterprise; it should acknowledge and address the challenges that confront doctors every day — common and uncommon diseases, patients with multiple coexisting illnesses, and efficient management of symptoms even when diagnosis is uncertain; and it should reflect individual patients' preferences and goals for treatment and health outcomes and enable ongoing development of evidence on treatment heterogeneity.
The authors argue that first, our quality measurement should be part of patient care, rather than something which must be added to it. This would reduce the resources necessary to implement QI programs and pay for performance systems.
Second, they maintain that metrics should be more cognizant of how medicine is practiced in the real world. They should acknowledge that many patients have a number of problems, with guidelines that overlap and sometimes even conflict with each other. They should also recognize that many patients, and the decisions for their care, aren’t as cut and dry as many guidelines make them to be.
Finally, they ask that metrics reflect patient preferences as well as those the system values. There has been a real thrust in research recently, to recognize that patient preferences and patient-centered outcomes matter. It’s hard not to argue that quality metrics should, too.
These pieces are short, and worth consideration. Go read!