Updated: Jul 1
I have talked about the MLTC star rating in previous posts and videos. And one of the most popular subjects has been the calculations for the star rating.
I have shown before, even that it seems a very complicated calculation, in the end, it is possible to obtain the results in an extremely accurate way, and most importantly take advantage of this calculation before the next rating is published.
In this post, I will give a very simple example of how we can work for your company.
This is the last MLTC consumer guide published by the state of New York in March of 2018. Basically we can see how the stars are distributed per domain (every column) and overall rating (last column).
We will consider a plan, GuildNet. GuildNet is no longer a plan in MLTC cause they closed doors at the beginning of this year. But for the sake of exemplification, I believe it can be a good example.
Let's remember that for the MLTC Consumer guide, every domain can be rated with a minimum of 1 star up to a maximum of 5 stars. In total the maximum number of stars per plan can de 60, and the minimum 12.
In left-hand side table is the total of star GuildNet obtain in all the domains. They obtained 42 and in the right-hand side table, the overall was 4 stars.
By analyzing the data of the las MLTC Consumer guide, we notice that 42 total stars it was on the edge of the 5-star overall tier. This means that GuildNet could have obtained an overall 5 stars by getting just one more star in any of the domains.
Considering domains 2, 3, 5, 7, 8 and 9 GuildNet lost 14 stars by getting between 2 and 3 stars per domain. In domains 1,4, 5 and 12 they lost 4 stars by getting 4 stars in tose domain.
What can GuildNet do?
Discover where did they lose that 1 star that kept them out of the 5-star tier. And allocate resources to the right domain for the next measurement period.
In domain 1 after processing the raw data, GuildNet obtains 1.61, this falls exactly at the starting point for the tier of 4 stars in that domain. They would need to reach a 4.52 to jump to the 5-star tier for that domain.
In domain 2, the picture is a little better, they were close to the threshold line to jump to de 4-star tier. After processing the raw data, they obtained 1.15, and they jump they needed to do to reach the 4-star tier is 1.43.
In this domain, GuildNet obtained a 96 as a raw result with a denominator (number members who participate in this particular measure) of 6761.
After some simulations, if GuildNet would have obtained 98 as a raw result instead of 96 (the same as the statewide rate) they would have achieved 2.28 giving them the margin to achieve 4 stars.
With this single star, they would have obtained 43 stars in total, obtaining a 5-star overall.
In domain 12 a similar situation happens, they obtained after processing the raw data a 4.15, and the threshold to the 5-star tier is 5.32.
If GuildNet would have reached an 83 in the raw results instead of 82, they would have obtained as a result after processing the data a 5.92. This change would have signified an extra star in domains, giving them the extra star they need for an overall of 5 stars.
As we can see with these two simple examples, by finding the domains were the plans to lose stars and identifying the easiest past to improve, it can be easy to get more stars overall.
The raw results published by the state are important, but the star rating needs more careful consideration in order to improve by domains and the number of stars.
Do not forget the raw results for the next MLTC Consumer guide star rating is going to be published soon, we can help you right now!
Find out how Byteflow's expertise can help you and your company.
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]
Contact us: email@example.com