Improving Access, Removing Barriers and Speeding Up Time to Therapy Patient demand for telehealth and a flood of industry challenges are disrupting the traditional healthcare landscape. Lack of patient awareness, access to care and overburdened clinicians have created significant wait times to see a provider and a prolonged journey to diagnosis and treatment. The lengthy […]
For the first time ever, pharmaceutical brands can now have a direct relationship with the patient from beginning to end of their journey. EVERSANA’s Direct-to-Patient Care Model offers pharma companies hyper-targeted patient acquisition strategies that:
- Leverage data-fueled audience segmentation,
- Embed telehealth integration on the brand’s website,
- Consider where patients are in their journey to enhance their experience, and
- Measure real-time results across all media channels to optimize lift in prescriptions.
Increasing the Patient Demand Funnel
This innovative model offers pharma companies a new patient acquisition strategy to increase their reach, accelerate brand awareness and engage patients and HCPs to generate sustainable total prescription (TRx) growth.
Customized by brand, the model can serve as a non-personal promotion channel, or it can be integrated with the manufacturer’s field deployment strategy. Each solution is customized to the brand’s unique needs, stakeholders and life cycle.
Patients researching symptoms may be served digital ads supported by marketing spend that direct them to your brand’s website. Here, they can directly request an appointment, speak with a provider and receive and fulfill prescriptions for treatment.
Patient engagement and adherence are critical to the success of any therapy. Driven by predictive analytics and machine learning, ACTICS Patient Relationship Management (ACTICS PRM) accelerates and customizes engagement by delivering brand messaging to patients and HCPs via their preferred channels.
Once a patient has been engaged, ACTICS PRM also creates highly-targeted patient personas, based on identified patient behaviors and patterns, to predict the “next best action” for personalized engagement. As a result, patients are more likely to engage and respond, leading to greater adherence.