What Makes an Effective Meaningful Use Measure Report?

We all know the popular curse that there are three kinds of lies: lies, damned lies and statistics. My college statistics professor used to ironically say “Never trust a data set you can’t eyeball.” In the world of “big” or complex data this visibility problem is even more severe. The data sets are too unwieldy to easily visually confirm a result. Tools are needed to help you understand the distribution and completeness of the data, and to confirm that the data you have really supports the results you are seeing.

Meaningful Use reports are no exception to this rule. For example, knowing that you have a median time between the decision to admit as an inpatient and the time of departure from the Emergency Department (ED-2) of one hour and thirty five minutes isn’t so useful if it obscures that fact that your Emergency Department only records the decision to admit time on 20% of the cases. Understanding the detail in the Clinical Quality Measures is even trickier. There are many factors that can include or exclude a patient from a measure denominator. The summary data can hide problems that you should try to remedy during your reporting period to ensure that your reports are accurate and will stand up to an audit.

To feel confident that your meaningful use numbers are accurate and complete, use a product that helps identify missing or inaccurate data and explains why each patient did or not make it into the population, denominator and/or numerator of each measure.  A review of each component of every measure’s result, and their supporting values, can give you the confidence you should have in the correctness and completeness of your attestation, before you sign on the dotted line.