Resources
Use Cases:
Optimizing Rare Disease Communication with Drug-GPT
Access the PDF Use Case.
At a Glance
Challenges
- Impact measurement difficulties: Struggled to accurately assess the effectiveness of educational materials across multiple data sources
- Varied data sources: Faced challenges in synthesizing insights from varied sources like surveys and educational content
- Lack of comprehensive analytics: Limited ability to gauge overall engagement and the real-world impact of Patient education efforts
Benefits
- Enhanced efficiency: Achieved an 80% increase in intelligence generation, enabling quicker analysis of educational material impact
- Deeper Patient insights: Provided longitudinal understanding of Patient alignment with key messages and tracked shifts in conversation trends and agree levels
- Improved Content Optimization: Identified areas for improvement, allowing for the continuous refinement of educational materials for better future engagement
Customer Objective
Healthcare Advertising Agency G struggled to understand and measure the impact and effectiveness of educational materials that had been generated by their Client surrounding HER1+ Breast Cancer using social data and survey data from Patient Interviews.
Brief
The Agency wanted an accurate way to quantify the alignment of Patients speaking in various data sources with key brand messaging from educational content. They wanted to be be able to track changes in alignment over time as a key performance indicator (KPI) and identify key brand messages written by Patients that have lower levels of alignment with these key brand messages.
Solution
This AI-assisted approach led to:
- An 80% improvement in efficiency
- Longitudinal insights generation
- The processing of more data from diverse sources
- A better understanding of Patient alignment with key brand messages
This allowed the Agency to effectively monitor the impact of
their educational material on Patient conversations and
alignment levels as well as identify areas for improvement for
optimization of future material.
