A pharmaceutical company encountered significant hurdles in identifying patients for a novel rare skeletal dysplasia therapy due to the limitations of traditional claims data. To overcome this, they explored lab data-driven patient identification solutions. This case study examines their successful partnership with Prognos Health, a provider focused on delivering 100% actionable data by linking identified patients directly to relevant specialists. By prioritizing quality over quantity, the company achieved a higher return on investment, avoided wasted resources, and efficiently engaged key healthcare providers.
A pharmaceutical company launching a first-line therapy for a rare skeletal dysplasia faced challenges in identifying eligible patients. Their initial approach, relying on claims data, proved inefficient in pinpointing individuals with a confirmed diagnosis and the specific genetic mutation targeted by the therapy. Recognizing the limitations of claims data for this rare condition, the company sought alternative solutions leveraging lab data to improve patient identification and outreach.
The company evaluated two real-world data providers, Prognos Health and a competitor, both offering patient identification solutions based on lab data.
Prognos Health differentiated itself by focusing on delivering 100% actionable data, ensuring every patient identification was linked to a relevant specialist. This focus on actionable insights aligned perfectly with the company's need to efficiently target healthcare providers (HCPs) specializing in the diagnosis and management of this rare disorder.
Prognos Health:
Prognos HCP Alerts provides a valuable tool for pharmaceutical companies seeking to ethically and compliantly engage HCPs in the rare disease space. By leveraging anonymized patient data and adhering to strict ethical guidelines, Prognos Alerts enables precision targeting that supports informed clinical decision-making, fosters appropriate resource utilization, and ultimately contributes to improved patient outcomes.