Webinar: Evidence-based quality metrics are only the beginning
To illustrate the multiple dimensions of the quality improvement enterprise, Diamond cited six broad dimensions that factor into the quality improvement enterprise: Quality measures; clinical decision support (CDS) at the point of care; engagement of healthcare professionals; engagement of patients and families; the implementation components (leadership, culture, supportive health IT); and drivers (such as accreditation and certification and financial incentives).
Diamond stated that the Institute of Medicine defines “quality” as the degree to which health services for individuals and populations increase the likelihood of desired health outcomes.
Diamond noted that there are a couple of trends currently bridging the quality and performance gap, including:
- Continued interest in measuring quality and cost of care/efficiency;
- Focus on pay-for-performance and reimbursement realignment;
- Public reporting of performance metrics;
- Shift in focus from measuring performance of health plans and hospitals only to the physician and accountable care organizations;
- National Quality Forum adopts multiple measurement sets for physicians level performance assessment and have developed U.S. priorities for action;
- Health IT and performance improvement have merged through the American Reinvestment and Recovery Act;
- The Centers for Medicare & Medicaid Services implementing pay-for-results programs for physician level measures;
- Building out the health IT infrastructure to support quality measurement and point-of-care decision support systems;
- Recognition of the limitations of evidence; and
- Commitment to patient-centeredness.
“Evidence is at the center of the quality measurement enterprise,” said Diamond. Evidence-based medicine is defined as the conscientious and judicious use of current best evidence from clinical care research in the management of individual patients, according to Diamond. “Derived from evidence are clinical practice guidelines that then derive point-of-care decision support and performance measures.”
Diamond went on to state that point-of-care decision support is focused on individual patients while performance measures are more directed at populations, noting that these two need to be linked. “We cannot be in the business of measuring performance and then sending messages at the point of care to physicians and professionals that are different from what we are measuring,” he said. “Professional judgment and patient preference all really have to be considered in this paradigm.”
A clinical performance measure is a rate and assesses “what is” rather than “what should be,” Diamond said. As quality is measured, consideration needs to be given to how to execute quality and improvement quality. He noted in his presentation that the dimensions of a good quality measure should include:
- Linkage between a process and outcome;
- Processes and outcomes derived from clinical practice guidelines or otherwise derived from evidence;
- Precisely defined inputs/outputs (numerator, denominator, exclusions);
- Methodology (data source, sampling, scoring, display, etc.);
- Measures that are actionable; and
- Tested measures.
“As we move from administrative and related data to accessing data such as laboratory and pharmaceutical data, we will find ... more access to richer, robust measurement sets and have the ability to more closely link process and outcomes, and more closely assign clinical actionability to the measures,” stated Diamond.
However, limitations need to be understood as well, Diamond said. Evidence-based medicine is derived from medicine and the interaction with patient preferences and clinical expertise.
“Evidence changes,” Diamond stated. “As we use evidence as building blocks for performance measures, we need to take into consideration patient preference and clinical expertise.”