In a recently published article from Med Ad News, co-authors Iyiola Obayomi, SVP, Managing Director, Data & Analytics, at EVERSANA INTOUCH, and Pierantonio Russo, MD, FCPP, FAAP, STS, Chief Medical Officer at EVERSANA, discuss the power of data and analytics and machine learning (ML) to optimize HCP targeting and prescription volume.
In an era where AI and ML in healthcare are driving significant prescription gains by engaging healthcare professionals (HCPs) more effectively, success now depends on identifying and engaging the right target audiences with personalized, relevant communication, which boosts the likelihood of desired behavior changes. However, pinpointing the right HCPs for specific patient populations remains complex and time sensitive.
Identifying optimal HCP targets involves numerous challenges, such as integrating insights about patients, payers and HCP data; managing frequently updated target lists; optimizing under resource constraints; tailoring lists for diverse needs; and making timely decisions amid broader marketing activities and competitive pressures.
How can we harness healthcare data and AI/ML to enhance targeting strategies and drive better outcomes?
Combining comprehensive data, advanced AI/ML analysis and specialized expertise enables rapid, precise targeting. Forward-thinking brands now have access to these elements, but many struggle with adoption due to entrenched processes, fragmented responsibilities and resistance to change.
Building in-house capabilities is often cost-prohibitive, especially for smaller organizations or those in rare disease spaces. To overcome these challenges, many brands partner with companies such as EVERSANA and EVERSANA INTOUCH, which invest in connected data sets, AI/ML methodologies and multidisciplinary teams to deliver actionable targeting recommendations and translate insights into effective strategies and execution.
The impact of data and ML is evident. For example, at EVERSANA INTOUCH, we used AI/ML to identify 1,200 new HCP targets, boosting new-to-brand prescriptions by 80% for a rare disease brand. We also identified nearly 5,000 undiagnosed rare disease patients with 94% accuracy, prioritizing 3,000 HCPs for timely treatment.
Data and ML are crucial for brand success in healthcare. Early adopters see measurable gains, while slow adopters risk falling behind. Midsize organizations and rare disease brands can benefit from partnering with experts in healthcare data/AI to bridge resource gaps and achieve transformative results. Now is the time to embrace data and ML to deliver better value for patients.
Read the full article from Med Ad News here.