Predicting and Preventing Patient Dropout: An AI Approach to Diabetes Care

A global diabetes care leader used AI to reduce insulin treatment dropouts by identifying at-risk patients and delivering personalized support. The AI-powered Early Warning Engine and tailored engagement program led to a 30% drop in patient discontinuation, a 25% increase in adherence, and better health outcomes. The initiative also improved resource efficiency by 20%, showcasing AI’s potential to enable proactive, patient-centered care.

Objective

To identify at-risk patients and prevent treatment discontinuation by implementing an AI-powered Early Warning Engine and personalized long-term support program.

Challenges

• High Dropout Rates: Patients often discontinued insulin due to lifestyle barriers, side effects, or lack of support.
• Data Complexity: Integrating diverse patient data from prescriptions, feedback, and health metrics was challenging.
• Need for Personalization: Each patient’s journey was unique, requiring highly personalized support interventions. 

Innovation Accelerator Deployed  

• Early Warning Engine: AI predicted dropout risk, flagging high-risk patients for proactive intervention.
• Personalized Support Program: AI delivered personalized resources and reminders through automated channels for continuous engagement.
• Data-Driven Insights and Continuous Improvement: Patient feedback refined interventions, ensuring they remained relevant and effective.

Benefits Delivered

• 30% Reduction in Dropouts: The early warning system allowed timely intervention, decreasing patient dropouts significantly.
• Improved Adherence and Health Outcomes: Medication adherence improved by 25%, with patients showing a 0.5% average reduction in HbA1c levels, enhancing overall health. 50
• Optimized Resources: Targeted interventions reduced support program costs by 20%, enhancing resource efficiency and scalability

Conclusion

The AI-powered approach to predicting and preventing patient dropout proved transformative for the pharmaceutical leader, achieving notable reductions in dropout rates and improving patient health outcomes. By implementing an Early Warning Engine and a personalized support program, the company effectively enhanced medication adherence, optimized resources, and delivered tailored interventions that supported patients throughout their treatment journey. This AI-driven solution not only met immediate goals but also set a strong foundation for scalable, patient-centered care, underscoring AI’s potential to drive impactful, data-driven healthcare improvements