While it appears there is very little most Americans can agree on when it comes to healthcare these days, one exception is that prescription drug therapies cost too much.
Whether the plan is government-sponsored or commercial, in a blue or red state, aimed at Boomers or millennials, or any other permutation, the feeling is drug prices are already high and threatening to go even higher—unless someone does something about it. By which people mean the government, despite all the risk and disruption legislation and regulation can bring.
Whether at the federal or state level, politicians on both sides of the aisle have been talking a great deal lately about finding ways to lower the cost of prescription therapies. But so far that’s all it’s been—talk. In the meantime, prescription drug prices continue to rise, and members must cover large and increasing out-of-pocket costs for the medications they need. On the member side, these high costs can lead to difficult choices—especially for Medicare and Medicaid members.
Some members will look to stretch their supply of medications by taking smaller doses or taking them every other day instead of every day. Others may forego them entirely when faced with the choice of paying for medications or purchasing other necessities such as food or rent. Still others may sacrifice their own medications so their children can get the care they need. No matter which choice they make, however, their health outcomes are negatively affected, reducing their quality of life while increasing their health plans’ long-term costs.
The good news for health plans (and their members) is they don’t have to risk a political solution to reduce the patient’s out-of-pocket cost for drugs. Instead, they can use a combination of machine-learning (ML)-based predictive analytics and hypertargeted communications to inform members about lower cost (but equally effective) alternative therapies—both generics and therapeutic equivalents—and motivate them to either make the change themselves or speak with their physicians about their options. Here’s a look at how that might work.
Determining the target audience
For this hypothetical example we will use a multi-state health plan with 250,000 covered lives including Medicare, Medicaid, and commercial members. Using pre-configured drug-switching rules, the analytics will look for members taking non-preferred products who have not previously tried (and failed) the lower-cost alternatives. The targets include members taking 200 name brand drugs which could be converted to lower cost generics, and 20 classes of high-cost drugs that could be converted to lower cost therapeutic equivalents.
These analytics yield a total cohort of 2,083 members who are presumed to be unaware that there are lower-cost alternatives to the medications they’re currently using.
Getting the word out
Once the targets have been confirmed, the payer begins its efforts to inform those members about alternatives. Rather than simply sending out generic messages, however, they use the analytics to search the members’ history, demographic and sociographic profiles, etc. to give them information they can use to create hyper-targeted, personalized messages that address each member’s specific needs and concerns.