Enhanced prediction
Created targeted interventions
Boosted adherence
Saved $5M yearly
The challenge
Tackling medication non-adherence with data-driven insights
Medication non-adherence in chronic conditions leads to poor health outcomes and higher costs. A top health insurer sought to enhance adherence, reduce risks, and improve patient engagement through predictive insights.
Key challenges
High costs due to poor medication adherence
Increased health risks for chronic patients
Limited insights to predict non-adherence
Need for effective intervention strategies
The solution
Data-driven adherence insights
Predictive framework
Detected adherence risks
Mapped key factors
Enabled interventions
Data-driven insights
Classified adherence patterns
Integrated patient data
Improved decisions
Implementation approach
1
Problem solving
Analyzed 200+ factors
Identified intervention
Mapped risk contributors
2
Predictive modeling
Used ML for prediction
Assessed patient and cost factors
Focused on refills
3
Action plan execution
Segmented patients
Targeted outreach
Enhanced adherence
The impact
Driving adherence and reducing costs
Better adherence
Improved compliance
Reduced health risks
Stronger disease management
Smart interventions
Personalized outreach
Data-driven engagement
Optimized care plans
Cost savings
5M annual savings
Fewer hospital visits
Lower treatment costs
Looking ahead
Advancing predictive insights
Expanding predictive models for better adherence insights
Enhancing intervention strategies
Enhancing personalized intervention strategies
Sustaining long-term impact
Driving continuous cost savings and patient outcomes
