It’s the middle of July. A time of year for pool parties, barbecues, campfires. and generally sweating your butt off. It’s a relaxing time of year for the majority of us. But what about the ski bums out there? It’s a long six months (five if you are lucky) until the first big dump of the year. At 46brooklyn, we sympathize with people who are impatiently waiting for things – except rather than waiting to shred the gnar, we’ve been anxiously waiting to shred through Medicare Part D drug pricing data. Good news, drug pricing enthusiasts; we have a big (data) dump for you! After months of work on our newest drug pricing tool, we are pleased to release the new Medicare Part D Drug Pricing Ski Slope dashboard. In this viz, we give you the ability to select one of many drugs (brand and generic, oral solid and non-oral solid), and view the price set by thousands of Part D plans for your selected drug. For good measure, we added a line to show you the true market-based cost of the drug (based on NADAC), so you can see how frustratingly random Part D pricing is by different plans for the same drug. So get your balaclava on and join us as we traverse the Part D drug pricing slopes.
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For the first time in our existence, CMS has released drug pricing data that overlaps with the date of our launch. As we ripped open the data and examined CMS’ gift, we found some major changes in the way that the state of Ohio reported their data – and it has revealed some eye-opening information about PBM and pharmacy margins on prescription drugs. Given all the drug pricing noise out of Ohio, and the federal heat on PBM “spread pricing,” we decided to do a deep dive into what’s happening in Ohio, and how its new data provides incredible clues to determine where the money flows within the drug supply chain.
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Recently, CMS updated their State Utilization databases, which provides quarterly drug pricing data that shows what state Medicaid programs are spending on prescription drugs. This most recent data update ended up filling in most of the Q2 2018 potholes and extended the 2018 data to include large portions of the third quarter. We have updated all of our dashboards that rely on this dataset, rolling all of them forward to Q3 2018, and there are some insightful takeaways. This update is a really big deal for us at 46brooklyn, because it is the first time that a dataset’s timing will overlap with our existence as an organization. With our launch in August 2018, this meant that CMS’ latest utilization data update for Q3 2018 would be the first real quarter of data that could have been theoretically impacted by our work and Ohio’s work to bring transparency to drug pricing in state Medicaid programs and beyond. After analyzing the data, the results are nothing short of amazing, and a clear indication that this system is in the midst of change. Here’s our insights.
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Over the last nine months we have sliced and diced generic drug pricing within Medicaid managed care to arrive at the conclusion that generic prices in Medicaid are subject to wild and seemingly arbitrary distortions. But the million dollar (or should we say $2 billion dollar) question is whether this problem is isolated to Medicaid, or if it is a broader issue plaguing Medicare Part D and Commercial plans as well? We now attempt to bring data into the fold to help answer this question. In this report, we embark on an in depth investigation into the pricing of the top 15 generic drugs in Medicare Part D - drugs that represented roughly a third of overall Part D generic spending in 2017. It turns out that the same arbitrary generic pricing behavior we have observed in Medicaid is alive and well within Part D. If you make it all the way to this report, you will be rewarded with all the math that estimates this problem to be worth over $2 billion in 2017.
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A few months ago, we released our Medicaid Markup Universe that collects all generic drugs dispensed within different state Medicaid programs and displays each as a bubble. The larger the bubble, the greater the cost the state is paying for that drug relative to its acquisition cost (i.e. “markup”). While that dashboard provides a good qualitative feel for individual drug pricing distortions, it doesn’t help quantify the distortions. To better identify those distortions in each Medicaid program, we designed a new visualization that drills down to three different groups (or celestially-speaking, “galaxies”) within the universe. We call them the High-Cost, In-Range, and Low-Cost galaxies. Our newest dashboard, the Medicaid Markup Galaxies, shows which drugs state Medicaid programs may be underpaying for, and perhaps more importantly, which drugs they may be overpaying for.
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