Predicting Patient Drop-Off and Switch with AI in the Pharmaceutical Industry

The Challenge

In the pharmaceutical industry, patient drop-off and switching between therapies represent significant challenges for drug manufacturers. Traditional methods for identifying and addressing these issues often rely on reactive measures, which may not effectively prevent patient attrition. Pharmaceutical companies needed a proactive solution to predict patient drop-off and switch events, enabling them to take timely actions and improve retention.

ProcDNA's Solution

Predictive Analytics

Advanced machine learning algorithms forecast patient drop-off and switch events, allowing for early intervention.

Actionable Insights

The solution provides personalized insights, including suggested actions and engagement strategies, to help sales representatives address patient concerns and improve retention.

Improved Sales Effectiveness

With proactive insights, sales representatives can engage more meaningfully with healthcare providers, addressing concerns before they escalate and optimizing retention efforts.

Data-Driven Decision Making

The solution provides valuable data-driven insights that enable pharmaceutical companies to optimize their sales and marketing strategies, improving overall patient retention.

Impact

Increased Adherence to Call Plans

Sales representatives adhered more closely to their call plans, resulting in higher engagement with healthcare providers and better retention strategies.

Improved Patient Retention

By proactively addressing patient concerns and providing timely interventions, the solution successfully reduced patient drop-off and switch rates, ultimately enhancing therapy continuity.

Enhanced Marketing and Market Access Strategies

The insights provided by the solution enabled marketing and market access teams to identify high-risk, high-value patients, leading to more targeted and effective strategies.