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.
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.
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.
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.
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.
The start of each new year brings a lot of “new-ness,” including a new round of price increases on brand-name drugs. This year has been no exception, and the media has been all over the subject. Unfortunately, we’ve found it difficult to figure out the “so what?” behind all of these price increases. On one hand, we are being told that the pharmaceutical manufacturers are back to “business as usual,” while on the other we are being told that the number of price increases are down meaningfully from last year. Which one is it? This felt like an opportunity to inject a healthy dose of facts into this discussion. So we set out to build a dashboard, 46brooklyn’s Brand Drug Price Change Box Score, that lets you visualize all brand-name manufacturer list price changes that are publicly-reported each week, drill down to the manufacturer and drug level, strength level, and compare and contrast different periods.
Average Wholesale Price (AWP) is a meaningless benchmark price for generic drugs. This has been known for at least a decade, if not longer. But despite its lack of substance, AWP-based payment models just won't go away. Unfortunately the contractual reliance on a benchmark that has no relevance to actual price makes it very difficult for the payer to know if they are getting a good deal or not. They are left to pay a fixed discount off of an unknown combination of meaningless, non-market-based, numbers. Seems like that would be tough sell, but this is drug pricing we are talking about, so of course, it's the norm. For the past couple months, we compiled data to create a visualization to help illustrate the problem that arises by anchoring generic drug costs to AWP. The finished product is embedded in this latest report "Inside AWP: The Arbitrary Pricing Benchmark Used to Pay for Prescription Drugs," along with our observations and analysis.
We’re told that drug prices are too high, but who actually is setting the price and where does the money end up going? CMS recently updated their State Utilization databases, which track what state Medicaid programs are being charged for prescription drugs. Most notably, they extended the 2018 data to include the second quarter. We have updated our datasets and visualizations to track the changes in drug markups relative to their actual costs. We’ve studied up and have come up with our “Top 20 over $20” list. In other words, the top 20 drugs dispensed through Medicaid managed care organizations (MCOs) with a markup in of over $20 per prescription. Think of it as a “top 40 under 40” list for generic drugs, with the main difference being that no self-respecting generic drug wants to be on this list. Check out our new visualization that highlights the drugs that are busting the budgets of state Medicaid programs.
Over the past couple weeks, we’ve been reflecting more on how neat Bloomberg’s Ohio Medicaid markup bubble chart was in their recent article, “The Secret Drug Pricing System Middlemen use to Rake in Millions.” We’re kicking ourselves now that we neglected to delve deeper into this in our report, “Bloomberg Puts Drug Pricing ‘Markups’ on the Map”. But like fine wine, good visualizations only get better with time, so it took us a few weeks to fully realize the possibilities that such data analysis could open up. Bloomberg’s excellent visualization does, however, leave a few open questions. What do other state Medicaid managed care programs look like? How do they compare to state fee-for-service programs? What do all states look like? To answer these questions, we set out to build a new visualization dashboard to compare drug markups between state Medicaid programs. We call our creation the “Medicaid Markup Universe” (because it looks very celestial). In this new visualization tool, we found a disturbingly large difference in drug markups across generic drugs in state Medicaid managed care programs, resulting in a slew of warped incentives that pressure supply chain members to value certain medications over others, and thus, certain patients over others.
Earlier this week, Bloomberg reporters published their results of a fascinating deep dive into Medicaid generic drug prices. The piece did an excellent job explaining the ins and outs of the hidden pricing spreads that exist on generic drugs, and it featured some intuitive visualizations that helped educate readers who may not have been familiar with these little-known drug price tactics. The analysis conducted by Bloomberg also integrated the results of a recent report from the state of Ohio's Auditor, which found that in a one-year span, PBMs pocketed more than $224 million dollars in spread pricing. Armed with this data, we set out to discover if we could deduce what pharmacy margins were over that same time period in an effort to peel back new layers of the onion and provide better information on where the money is going. Check out our newest drug pricing report to learn more about hidden prescription drug markups.