Medicare : Demystifying Consumer Preference

Proportion

Science behind Medicare Modelling

Medicare

Science behind Medicare modelling

What is the data and what can it do?

Ample public data, available through CMS and Medicare, is used in a machine-learning based tool that mimics consumer choice in Medicare Advantage. By comparing plan features including benefits, MOOP, drug deductibles, star ratings and other attributes across Medicare Advantage Plans at each county level, firms can identify the top attributes that determine plan competitiveness, predict enrolments, create marketing strategies and design better products.

How to leverage data and associated challenges?

Models can be built to be flexible yet robust, and advanced ensemble techniques and bagging algorithms are used to predict Medicare Advantage enrolments for every single plan in each county in the country. Data from various sources and spared across various files will have to harmonized and married and maintained for building the database. The models will need to ingest a large volume of data – literally 4000 attributes for each plan. One will have to find an effective way to enable people to use it. And all this will need to be done with a high degree of accuracy and, given the short duration of the AEP period, within a very limited amount of time.

Is there a reliable and efficient way to do this?

TEG Analytics has created a holistic solution for this problem: HealthWorks- a platform where all CMS information is available in a single easy-to-use and intuitive dashboard. The models have been homed in over the years to give over 99% accuracy in enrolment predictions for plans with county-level granularity within 72 hrs of the release of CMS data. The findings can further be used to generate insights about factors affecting the performance of each plan.

To achieve this various data sources are mashed up together – across demographic information, eligible, market penetration and growth over time, income levels of Medicare-eligible; plan level features including costs, MOOP, benefits, deductibles, drug information, etc; county-level competitive features such as the number of new entrants and new plans rolled out; changes in market conditions due to increased costs, MOOP, momentum, etc. Robustness of models is ensured through hold-out validations that are done within and across sequential years, and our metrics minimise prediction errors at three different levels – within a county, within organizations, and across large, small and medium plans.

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