Any moment now, the supreme court will rule on a case that challenges the legality of subsidies granted to eligible low and middle-income populations buying insurance through the federal health exchange. The Kaiser Family Foundation’s analysis of the case’s impact paints a stark picture of what’s at stake. About 6.4 million insureds are at risk of losing their subsidies across the 34 states that are currently in the federal marketplace. This population will see an average increase in premium of 287%. No matter what the metric is, the projected impact is material and will impact the future of health care.
At Stotle, our perspective on this matter is motivated by our mission that drives everything we do: enable pharma unlock the full potential of their data through superior data science and analysis. So, when we looked at the case in front of the supreme court, we asked ourselves three key questions:
- Will this decision increase or decrease data transparency?
- Will the ACA data universe be more fragmented or more concentrated?
- Finally, will the data lend itself to more easier or more meaningful analysis?
Why are these questions important? Consider this: one of the biggest impacts of ACA is the acceleration of the transparency into the payer market. Each payer participating in the federal and state exchange market is required to provide detailed information on plan premium, benefit design, drug co-pays, and physician visit costs.
To see what is already available, check out our health-exchange analyzer that allows users to better understand benefit design, premium patterns, and level of competition within the federal exchange market. Let’s now consider the 3 questions:
Currently, the federal exchange dataset is much more granular and captures a wider array of information compared to the public datasets made available by the state exchanges. For instance, the federal exchange data will give precise health plan level premiums for various actuarial segments and also provide drug co-pay information etc. But, a state such as California publishes limited payer information related to drug co-pays and benefit design.
Should the court rule for the plaintiffs, some of the states are likely to set up their own state-based exchanges to keep the subsidies flowing to their constituencies. Essentially, this will result in a growth in the number of state exchanges and a reduction in the number of states on the federal marketplace. Basically, a significant portion of the payer data will be published at a more aggregate level and with less detail.
Result: Decreased transparency
Similarly, a move to the state exchanges will make it more difficult to do analysis across various markets. For instance, to understand a single payer’s participation within the exchange market, you will now have to go to all the markets in which the payer operates and stitch together the data to have a holistic picture. Result: Increased fragmentation
Finally, HHS has been pushing for greater transparency into the quality of prescription drug coverage afforded within the exchanges. Currently, patients cannot search for plans that cover the specific medications they take since drug formularies are not available prior to the purchasing decision. The recently published final rule for 2016 requires drug formulary information among other things from the plan issuers. Patients should now be able to shop better and understand the breadth and depth of the formularies within a specific QHP. The newly established state exchanges are unlikely to publish a broad dataset with all of the new data elements that HHS is requiring of insurers. This will limit ability to perform statistical analysis on formulary design etc. since the federal sample size will likely be reduced Result: Lower quality of insight into the exchange market
So, the impact is clear. Should the government lose the case, we will all be poorer for it due to lesser data transparency, lower data accessibility, and potentially less powerful data analysis.
That's aid, our goal remains the same: continue to apply the best data science methodologies to help clients put together a winning payer strategy in an increasingly difficult payer environment. Regardless of what happens over the next few days, we will be ready and waiting!
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