In a previous post by our principal Research Scientist Andi Shehu, he described how it is possible to relate the presidential elections with Quality Incentives and Star Ratings for your MLTC plan.
In the case of elections, we know that New York has been a blue state for a long time. Democratic candidates have it "easy" in this state, to put it in a simplistic way.
A simple way to identify your Plan's blue measures is using historical data visualizations.
Just inspecting at a first level the trends over the measurement periods, we can identify which are your "best measures" (blue states).
In that case, we analyze how consistent is your result over time. If you are always improving and reaching the maximum points possible, over let say the las 3 measurement period, we consider that a blue measure.
After visualizing historical trends, we assume for example we are in the primary elections, competing with other members of your party that share the same features with you. You have to consider different things here:
1) if you are always reaching the maximum of points, who are your closest competitors? (which candidate offers similar policies to yours?)
2) Is there any other plans who can go up or down, how does it affect your plan? (is there any candidate who can drop the race or a new candidate entering the race?)
3) How much have you improved in this measure in the last measurement periods? (how much a candidate is improving in preferences of the electorate compared to the closest competitors?)
4) Does your denominator has helped you to improve? (how much has helped a candidate to improve in certain areas?)
Clearly, the blue measures (blue states) represent points (votes) you have secured, but it is important to analyze how strong these points are.
There are a lot of variables involved in improving Quality Incentives (as in primary and general elections). We know it might be difficult for you to keep track of all these variables, that is why we can do it for you with Lagrange AI.
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