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Artificial intelligence-driven approach for patient-focused drug development.

Prathamesh Karmalkar1, Harsha Gurulingappa1, Erica Spies2

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Summary
This summary is machine-generated.

Leveraging social media data with AI and text analytics offers valuable patient insights for pharmaceutical research and development. This approach helps understand patient needs, expectations, and unmet needs for targeted drug development.

Keywords:
artificial intelligencenatural language processingpatient experiencepatient-focused drug developmentsocial mediatext analyticsunmet needs

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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.