David Weiss and Bill Woywod, published in Pharmaceutical Executive
A pharmaceutical company with a cardiovascular therapy wanted to determine which hospitals the treatment option suited best and define their respective stakeholders and influencers to help spur a connection. In the past, like most life sciences companies, the company would have relied heavily on its expertise and best intuition to select analog therapies and proxies for patient volume, such as number of admissions or discharges. In this case, however, the company took a novel approach.
The company took advantage of the increased availability of claims data, abundant computing resources, and machine learning methodologies to precisely identify and determine top-tier hospitals across the United States most likely to adopt and prescribe its new drug therapy. Utilizing the combination of innovative on-point query and analysis design, skilled programming, and industry expertise, the pharmaceutical company gained the critical insights needed to then prioritize these targets based on likelihood of adoption and optimize field resources accordingly.
This article explains how machine learning can provide an on-demand portfolio-wide view of optimal provider targeting and related field deployment. It also explains why life sciences companies should design their field teams to be comprised of individuals who demonstrate agility over singular therapeutic-area expertise.