Expanding the HCP target list for a new drug used to treat a rare disease in children

The challenge

Establishing a clear understanding of the disease market to identify the right HCPs

The pharma company needed to appropriately define the disease market and any physician-patient interactions, however the data gaps within the diagnosis codes for the rare disease and diverse patient symptomology were not conducive to a straightforward approach to identify and predict HCPs.

The solution

Use analytics to find rare disease patients and the HCPs who would treat them

Dynamic, disparate physician, patient, and payor data of patients currently on treatment were combined and analyzed to identify look-a-like patients for the rare pediatric disease who would most likely require treatment with the pharma company’s new drug. A novel prioritization algorithm was applied to identify potential patients with a rare form of cancer and target the right physicians most likely to treat them. Physicians were then segmented based on the highest probability of interacting with the patients.

The impact

Greatly increased and expanded the target list of HCPs for the rare disease

For this disease indication, the pharma’s company’s physician target list was tripled to include both treating and referring physicians, in addition to identifying key factors that may predict the next rare disease patient (including specific physician interactions, out of pocket spending, and disease treatments that may prompt a more active intervention leading to treatment with client product).