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As a self-insured employer or benefits advisor tracking specialty medications, you’ve probably experienced the sad truth of the American pharmacy industry: prescription drug prices are higher than ever, and it’s difficult to limit the increasing costs. Drug companies argue that high prices cover innovation and research, but examples like Daraprim and EpiPen’s price hike tell a different story.
The cost of prescription drugs are expected to increase at double-digit rates, and this means that self-insured employers are scrambling to predict benefits spending. That’s where healthcare data analytics can really shine. Employers and consultants can use pharmacy and healthcare claims data to find cost drivers, make formulary adjustments, and keep drug costs reasonable for employers and employees.
Using the Artemis Platform, we were able to figure out the most expensive prescriptions employers are covering under their plans. Spoiler alert: many of them are specialty medications. But before we jump into this, let’s start with the basics.
There is no single, reliable definition of a “specialty medication,” but there are some ways to identify them.
Employee benefits leaders can spot specialty medications by using definitions set by their Pharmacy Benefit Manager (PBM), looking at prescription drug costs, or getting input from clinical staff, like a chief medical officer or chief pharmacy officer. Some common specialty medications may include drugs for rheumatoid arthritis, cancer, and multiple sclerosis. They often come with a high price tag because they are delivered through a unique mechanism (like an injectable pen) or are prescribed as long-term medications for symptom reduction.
Healthcare data analytics can give us a window into the costs of these expensive prescriptions. The Artemis Platform integrates data from self-insured employers and calculates costs, measures program performance, and offers insights into employee benefits data. Employee benefits leaders use Artemis’ tools to get a holistic view of their benefits and take action that helps employees lead healthier lives.
Artemis integrates data from large, self-insured employers. Our de-identified, demo data set includes healthcare claims for over 20,000 members. From our demo data, we can see that the most expensive prescriptions are:
The drugs on this list are all classified as specialty medication, and they’re all used to treat chronic illnesses. Some specialty medications require special handling, monitoring or approval before they are prescribed. Looking at the chart above, we can see that while just 28 people are being prescribed Humira in our data set, the employer paid amount totals $895,806. That is a whopping $31,993 per person!
Both Humira and Enbrel, used to treat rheumatoid arthritis, fall in the class of drugs called “biologics.” These are specialty drugs that are manufactured from a living organism like a plant or animal cell or human tissue.
The drug manufacturers argue that biologic drugs are incredibly complex and sometimes impossible to replicate. Because of this, the FDA has labeled generic versions of the drugs as “biosimilars,” rather than generics. This distinction delays these medications from entering the market because pharmaceutical companies that create biosimilars need to prove that their products actually produce the same effects as the drug they are replicating, forcing them to fund case studies and tests with real patients.
In the case of Gilenya and Tecfidera, used to treat MS flare-ups, competition in the specialty medication market can also actually drive costs up. According to the American Academy of Neurology, the huge price jump for MS treatments from $8,000 to $60,000 per year was a result of new disease-modifying therapies (DMT) entering the market with a higher price point and as a result, older therapies increased their prices to match the competition.
Patents are also an issue for generic drug advocates. According to the New York Times, while the main patent of Humira expired the end of 2016, AbbVie, the company behind Humira, has amassed about 70 new patents covering the drug that could keep away biosimilars until 2022.
Truvada, a prescription to help prevent HIV infections in people who are HIV negative, is a combination of two HIV antiretroviral drugs: emtricitabine and tenofovir. Combining these drugs together creates a treatment therapy known as pre-exposure prophylactics or PrEP. As of 2017, Truvada is the only FDA approved HIV prevention therapy on the market. When taken consistently, Truvada has been shown to reduce the risk of HIV infection in people who are at high risk by up to 92%.
A prescription for Truvada cost $17,258 per person a year in the U.S. in 2017. Compare this to a generic version of the pill sold in India that costs $67 per person.
The manufacturer of Truvada, Gilead, owns U.S. patents for emtricitabine and tenofovir, which are both needed to make Truvada. While their patent for tenofovir expired late 2017, their patent for emtricitabine does not expire until 2021.
In our Artemis infographic, we discussed ways to avoid overspending on specialty medication. Some of these tips include:
All of these tactics require self-insured employers and benefits advisors to keep a close eye on employee benefits data. They will need to compare across medical and prescription data feeds to see if everyone receiving a certain drug has also been diagnosed with the condition it’s designed to treat. Employers and advisors should also work closely with Pharmacy Benefit Managers (PBMs) to get high-quality, accurate prescription claims data. They’ll want to monitor their formularies alongside the PBM to ensure members are getting the medications they need at a cost that’s sustainable and predictable for their organization.
Specialty drugs can drive up employer healthcare costs, but there are ways to track, measure, address this spending. Healthcare data analytics is the first step to finding what kinds of specialty drugs are being prescribed, how many employees are using these medications, and how these drugs impact overall healthcare spending for self-insured employers. They say an apple a day keeps the doctor away. We’d add that robust health data analytics also keeps the inefficient spending away.