SVP, Head of Commercial Analytics
Accurate and Actionable Predictions
Essentially, what Brett DiNatale’s work boils down to is understanding—mathematically—data patterns surrounding drug utilization in combination with payer activity and both physician and patient behaviors to predict with incredible accuracy market outcome scenarios for new medications under development.
A good example of Brett’s creative solutions is the Artificial Neural Network he developed to help clients understand what a payer’s coverage may look like for a product two to three years away from the market. The network can examine every pharmacy benefit product on the market today and develop its characteristic profile to include how payers cover it, in which markets it fits, which type of medication segment, etc., and point out exactly which aspects of it are going to be pros and cons to payers so clients can develop the appropriate mitigation steps.
On the other hand, patient behaviors are an entirely different formula, and Brett designed a statistical model several years ago to get a better understanding of patient adherence. Brett and his team built a system that examines the patients’ patterns of using the product, their likelihood to go from one month to the next and continue treatment, as well as the patients’ characteristics. Additionally, if other factors are altered—such as the lowering of a copay—the system can quantify the percentage change of medication adherence.
In between the payer and patient is the physician. Accuracy in determining a physician’s likelihood to prescribe a medication has jumped from 25% to 80% thanks to Brett and his team’s model. By examining physicians’ characteristics (including the market access landscape in their practice) and combining those with patients’ characteristics (such as demographics and their prior treatment history), Brett can develop predictive models indicating whether physicians will prescribe a drug to a specific patient.
Not only is the model impressive, so are some of the results Brett and the PRECISIONxtract team produce. Working with a client to optimize their prelaunch market access strategy, analytics integrating payer contracting and patient assistance modeled the behaviors of both access levers to save the client as much as an estimated $28M per year by 2030.