Artificial intelligence-driven approach for patient-focused drug development

Area of Science:

• Digital health and patient engagement
• Artificial intelligence in healthcare
• Social media analytics

Background:

• Increasing digital participation by patients offers opportunities for patient-centric research.
• Social media is a valuable data source for understanding patient populations and driving targeted impact.
• Analyzing vast amounts of online information requires advanced techniques like AI and text analytics.

Purpose of the Study:

• To demonstrate a scalable solution for utilizing social media data in pharmaceutical research and development.
• To capture and assess patient experiences and expectations regarding diseases, treatments, and unmet needs.
• To create a framework for applying this methodology across various indications and therapeutic areas.

Main Methods:

• Utilizing artificial intelligence and text analytics to process and identify relevant social media posts.
• Developing an enterprise-ready solution for capturing and assessing patient-generated data.
• Establishing a playbook for broader implementation in drug development.

Main Results:

• Demonstrated feasibility and utility of social media data for R&D.
• Identified key patient experiences and expectations related to diseases and treatments.
• Provided a scalable solution for healthcare business intelligence.

Conclusions:

• Social media data, when analyzed effectively, provides crucial patient insights for R&D.
• The developed solution enables a deeper understanding of patient needs and unmet medical needs.
• This approach supports patient-centric drug development and can be expanded to new therapeutic areas.