Predictive Analytics in Life Sciences: Co-Payment Mitigation

Published by RoadMap Technologies on October 24, 2012October 24, 2012

Specialty pharmacies have emerged as a strategic distribution channel for pharmaceutical and biotechnology companies, as the dollar share of distribution by this channel will soon reach 40% of the U.S. market. Specialty pharmacies provide a wide variety of services beyond filling an RX.  Patient support coordinators assist patients in administration of drugs, coordination of insurance benefits, adherence to therapy and scheduling of refills

Privacy concerns over patient data can be addressed by the process of de-identifying patient data which removes all personal information from prescription records while maintaining important data on demographic groups, insurance coverage and medical conditions which are necessary to model behavior at the patient level.  Patient level analytics allow drug companies to model persistency of therapy and adherence, which are often critical to patient outcomes.

Co-Payment Mitigation

With rising prescription costs and economic pressures, payers have shifted more specialty drugs into higher co-payment tiers.  Without co-pay mitigation, many patients face hundreds of dollars of copayments per month, which have an enormous impact on adherence to therapy.  With patient analytics, life science companies can develop and optimize programs to buy down co-payments for patients, therefore maintaining high quality access to needed medications.

Utilizing Big Data For Strategic Patient Insights:

Patient-Level Modeling Ensures That All Impacts on Patient Behavior Are Captured.

Not so long ago, pharmaceutical companies had to try to analyze the impact of marketing programs using monthly RX data by sales territory or physician decile.  Insights on patient behavior were gleaned from pharmaceutical call reporting systems and survey research with doctors.

RoadMap utilizes state of the art nonlinear statistical modeling techniques that are specifically designed to model the behavior of hundreds of thousands of individual patients on a daily basis.  Unlike linear models used for aggregate RX data, nonlinear models use statistical distributions designed for discrete patient choices such as Binomial and Multinomial Logits.

Event based models of individual patient persistency on therapy also require nonlinear methods like the Cox Proportional Hazards model.  Despite the sophistication of the models, RoadMap is committed to explaining and documenting all modeling results, so that management does not have to rely on a fragile “Black-Box” approach for critical strategic insights.