Ask the Experts- MCC Answers Your Metric Questions Archive

With more organizations focusing on metrics, the MCC has received an increase of questions ranging from how to use metrics, why some metrics are better than others, which type of metrics is best to use, as well as questions about specific MCC Metrics. This column provides a forum for us to share these questions and answers with you

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Meet the Experts


Keith Dorricott, MCC Ambassador and Director,
Dorricott Metrics and Process Improvement, LTD

Linda B. Sullivan, Co-Founder & President,
Metrics Champion Consortium


A: Each MCC metric includes a written description of the metric, a formula and a performance target. The purpose of the performance target is to establish performance expectations or a level of performance that is acceptable for that particular time, cost or quality measurement. Without a target, it is difficult to interpret the results and determine whether additional action is required – the target provides the context in which to interpret the results.

MCC Work Group participants define performance targets as part of the metric development process. Many of the MCC metrics developed by the Clinical Operations Sub-Group have green-amber-red performance levels. Results that fall into the “green zone” are good results; results in the “amber zone” are in the to be watched grouping as they fall outside of good results but don’t need immediate action steps; and results in the “red zone” are poor results that require action steps.

Some MCC metrics do not have standardized performance targets because targets varied by therapeutic area.

A: When looking to summarize data people often use the mean (also termed the common average). This works well when the distribution of data is even – looks something like a normal curve. But often data is not normally distributed in this way. This is particularly true when measuring cycle times. These tend to have a low peak and then a long tail. The long tail impacts the mean such that it can be a long way off the peak of the distribution.

For example, if the cycle times for completing Monitoring Visit Reports are 5, 9, 10, 10, 10, 11, 11, 24, and 56 days, the Mean is 16.2. But when we look at the numbers, is 16.2 a good representation when 7 out of the 9 cycle times are less than 16.2? This is where the median works better. The middle value in this example is 10 and by definition, half are below and half above. [Figure 1]

There are non-parametric statistical tests that you can run comparing medians (as you can with a T-test for comparing means). They are not as powerful as the T-test but do not rely on the assumption of an underlying normal curve.

A word of caution about the term “average.” Most people use “average” and “mean” interchangeably. However, the definition is ambiguous – it can refer to the median or the mode of the data, too. Here’s a dictionary entry on the term.