How predictive modeling will change value-based marketing

There's no doubt that prescription drug prices are on the rise. The costs of brand name prescription drugs have risen much more quickly than the general inflation rate. According to AARP, name brand drug prices rose 15.5 percent in 2015, while inflation rose 0.1 percent.

If pharmaceutical manufacturers can't find a way to make their products more affordable for patients, they risk incurring stricter government regulations. One way pharma companies are attempting to control drug prices is using predictive modeling to structure value-based contracts with payors. Equipped with analytics data, manufacturers hope to strengthen their position at the negotiating table.

What is predictive modeling?

Predictive modeling is a method of using historic data to create a model which can predict the future with some accuracy. It has many applications outside of the medical industry, as well as within it. For example, a hospital network in Paris used machine learning and 10 years of hospital admission records to predict future admission rates. According to big data expert Bernard Marr, the hospital system is able to make day and hour-level predictions of the expected number of admissions.

This concept can be applied to pharmaceutical drugs, as well. Let's say your company developed a product to reduce the risk of heart attacks five years ago. Since the drug went to market, it has been adopted by many cardiologists, who use it to treat their patients with a high risk of heart attack. Today, you can look at claims data from payers to see how effective your drug has been. For this example, let's say you discover that only five percent of patients who receive your drug experience a heart attack. Assuming that rate holds true for the future - and why wouldn't it? - you could negotiate for better rates from payors.

By demonstrating the value of your product, in terms of patient outcomes, everyone wins. Payors can pay less for medications that don't work, manufacturers ensure greater market penetration, and patients have better access to drugs that work.

Predictive modeling uses historic data to demonstrate the value of a product.

What is value-based marketing?

Value-based marketing takes the predictive modeling detailed above one step further. Aside from negotiating with payors, a pharmaceutical manufacturer with solid analytics data is empowered to make more realistic claims about their products.

Let's return to the previous example. A five percent readmission rate for heart attacks is low, which is good for patients who want to stay healthy, and good for doctors, whose Medicare reimbursements are based on the outcomes of their care. Fewer unintended readmissions are better for everyone. Therefore, highlighting those benefits in your physician-directed marketing could be an effective play.

How is predictive modeling used today?

Now let's look at a real-world example in a marketing setting. Modern Healthcare reported that Eli Lilly and Harvard Pilgrim Health Care struck a deal regarding the Type 2 diabetes drug Trulicity. If patients do better on competing drugs, Harvard Pilgrim will pay Eli Lilly less money for their product. Rather than simply making claims about superiority or effectiveness, manufacturers like Eli Lilly demonstrate a real financial incentive for producing meaningful outcomes.

Harvard Pilgrim currently has 12 value-based contracts with drug makers, and larger insurers like Cigna Corp. and Aetna are beginning to experiment with the concept, as well.

The entire medical industry is slowly shifting to a value-based payment system. If your company uses predictive modeling to develop value-based incentives for payors, you could see a financial benefit from sharing that information with doctors via display ads and other marketing channels.

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