Title

AWS re:Invent 2023 - Building a life science data strategy for accelerating insights (LFS203)

Summary

• Valerie Delva leads data AIML strategy and solutions for AWS and discusses the importance of an integrated data strategy in healthcare and life sciences.
• Key themes include the critical role of data as a differentiator, the challenges and opportunities in the industry, and practical steps for building a data strategy.
• AWS offers a health data portfolio with fit-for-purpose services and solutions to support enterprise-level data strategies.
• Dharmesh Thakkar from Johnson & Johnson shares insights on commercial operations data insights and the importance of data and insights in addressing industry challenges.
• Patrick, a data scientist with J&J, discusses the use of generative AI in handling unstructured data and the Retrieval Augmented Generation (RAG) design framework.
• The session emphasizes that data is a differentiator for generative AI, ML use cases, and innovation, and aligns with business use cases and patient outcomes.

Insights

• The life sciences industry is facing challenges such as the need to speed up therapy development, handle exploding data volumes, collaborate efficiently with third parties, and leverage generative AI.
• AWS has recognized the need for specialized services in healthcare and life sciences, leading to the development of AWS HealthLake, HealthLake Imaging, Health Omics, and HealthScribe.
• AWS Data Exchange (ADX) and AWS CleanRooms are solutions designed to facilitate data collaborations and secure data analysis without sharing underlying datasets.
• AWS Data Zone is a service that addresses the need for specialized data catalog services in healthcare and life sciences.
• An integrated data strategy should focus on breaking down internal data silos, secure and governed access, and identifying critical use cases and data types.
• Johnson & Johnson's approach to data and insights is driven by a focus on patient-centered healthcare and the use of data to address challenges in the commercial space.
• Generative AI is being used to handle unstructured data, with the RAG design framework being a key method for integrating siloed research into actionable insights.
• AWS services like Kendra and Bedrock, as well as tools like AWS Canvas and AI Studio, are highlighted as instrumental in developing and optimizing generative AI solutions.
• The session concludes with a call to action to leverage data and AI to drive precision health and improve patient outcomes, exemplified by the partnership with the Children's Brain Tumor Network.