In ACA and QRS – Shoot for the Stars Part 1, I laid out the overall domains that are going to be used to score QHP plans offered through the Marketplace. In this blog, we will take a closer look at the data derived measures and what factors a QHP issuer should consider to achieve high scores.
For reference all 43 of the required measures can be found on the CMS website (click here).
Now let’s take a look at some factors to keep in mind when dealing with the data derived measures:
Leverage Accreditation Processes
Good UX Means Good Business
In a world where technology is rapidly advancing and user expectations are rising, it’s no longer enough to have an average user experience; to delight your users and surpass your competition you must strive for the exceptional.
It was required to get Health Plans Accredited to offer on-market. In addition, CMS aligned required QRS data measures with HEDIS measures that are typically required during accreditation. So, make sure to leverage current investments to keep initial costs low while jump starting QRS efforts.
Solution for Disparate Data Sources
The data measures published on CMS, as with HEDIS, cover a variety of areas. Some examples from the Beta Test Measure Set are Annual Dental Visit, Cervical Cancer Screening and Proportion of Days covered. This means that a plan must gather data from many areas like dental, medical and pharmacy. Look at creating a dedicated area for collecting, storing and analyzing the required data.
Find the Data
Due to the nature and history of the industry, there are numerous ways that patient information can be documented. When it comes to data measures, QHP issuers have to identify where the data might be located for each of the measures and ensure that the data source has the integrity required by NCQA to include in the measurement process used for QRS scoring.
Partner with Providers
Work with Providers to develop initiatives that will drive more accurate data into the system. This can be accomplished in a variety of ways from making integration of systems easier for reporting clinical data to modifying incentive programs to drive data needed.
So what factors should a QHP issuer take into consideration when evaluating the data requirements?
In my next blog, I will list some factors to consider for the survey data.