The importance criterion evaluates how evidence-based and crucial a measure is for improving health care quality and outcomes, especially in key areas like safety, timeliness, effectiveness, efficiency, and patient-centeredness. Information from testing often provides additional empirical evidence to support prior judgments of a measure’s importance. In particular, beta testing results may reveal that a measure assesses an area with substantial opportunities for improvement. Testing can also uncover that the measure addresses a high-impact or meaningful aspect of health care.

Examples of Empirical Evidence

Examples of empirical evidence for importance or improvement opportunities derived from testing data include

• Quantifying the frequency or cost of measured events to demonstrate no measurement of rare or low-cost events
• Identifying substantial variation among comparison groups (e.g., urban versus rural) or suboptimal performance for a large portion of the groups
• Demonstrating that methods for scoring and analysis of the measure allow for identification of statistically significant and practically/clinically meaningful differences in performance
• Showing gaps of care
• Identifying evidence that a measure is associated with consistent delivery of effective processes or access that leads to improved outcomes

Examples of Reported Data

Reported data to support the importance of a measure may include

• Descriptive statistics (e.g., means, medians, standard deviations, confidence intervals for proportions, percentiles) to demonstrate the existence of gaps
• Analyses to quantify the association (correlation) between the measure focus and comparison groups such as rural versus urban or the measure focus and a material outcome

Please note that the CMS consensus-based entity (CBE) requires a performance score decile table (sorted by entity performance score) be reported, when possible.