I've talked about Quality Incentives (QI) before. In all my previous posts I always try to emphasize the importance of QI, how to improve results to improve them, how to calculate them, how to build an Electoral Map for QI in order to make more informed decisions inside and outside your MLTC plan.
Today I want to show you a sample of the type of information we can provide. I'm using an anonymous MLTC plan.
In the following table, I'm showing all measures inside the Quality Incentives calculation.
The columns show the result and the points obtained accordingly to the percentiles. In the last column, I'm just presenting the average of that measure considering all plans so you can compare the points the plan obtained against the average of the measure.
In the last row, I'm giving the final total points the plan obtained. It is important to note that Compliance measures are not considered in this table, because that information, to my knowledge, is not publicly available.
In the next table, I present the same information for the same plan but in a different measurement period July-December 2018.
You can get historical information, in the same way, I'm showing you, also the last calculation with the most recent information publicly available.
Find out how Byteflow's expertise can help you and your company.Â
Byteflows AI is a boutique NYC based data consultancy with a global reach. Our small team of dedicated AI & data science consultants and experts strives to deliver business value with AI. [Healthcare, MLTC, Insurance, FinTech, NLP, Hedge Funds, Pharma, Political campaigns & more]Â
Contact us: info@byteflows.com
Comments