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NVIDIA AI Podcast

How Agentic AI Shortens Drug Development and Boosts Patient Outcomes - Ep. 277

33 min episode · 2 min read
·
Raja Shankar,Avanar Broy

Episode

33 min

Read time

2 min

Topics

Health & Wellness, Fundraising & VC, Leadership

AI-Generated Summary

Key Takeaways

  • Clinical Trial Automation: Agentic AI reduces clinical trial timelines by automating hundreds of manual processes including document generation, site readiness tracking, and patient enrollment monitoring, potentially saving sponsors hundreds of millions in development costs and accelerating market access by six months or more.
  • Data Connection Over Collection: Market research with 107 life sciences executives reveals 80% of time is spent stitching data together versus analyzing it. The primary challenge is not data scarcity but connecting heterogeneous data sources across behavioral, consumer, execution, and clinical datasets to generate actionable insights for drug launches.
  • Commercial Launch Strategy: Agentic AI transforms three critical phases: pre-launch strategy including patient burden analysis and payer messaging, operational planning for HCP segmentation and territory design, and engagement optimization through automated marketing channels and personalized physician outreach to reach underserved patient populations faster.
  • Implementation Framework: Successful agentic AI adoption requires four elements: start with clear business KPIs tied to strategic goals, fail quickly through rapid pilot-to-decision cycles, ensure compliant data access with sufficient metadata for model training, and design for organizational scale including change management and workforce readiness from day one.

What It Covers

IQVIA executives Raja Shankar and Avanar Broy explain how agentic AI transforms pharmaceutical development and commercialization by automating clinical trial workflows, accelerating drug launches, and connecting fragmented healthcare data across 1 billion patient records globally.

Key Questions Answered

  • Clinical Trial Automation: Agentic AI reduces clinical trial timelines by automating hundreds of manual processes including document generation, site readiness tracking, and patient enrollment monitoring, potentially saving sponsors hundreds of millions in development costs and accelerating market access by six months or more.
  • Data Connection Over Collection: Market research with 107 life sciences executives reveals 80% of time is spent stitching data together versus analyzing it. The primary challenge is not data scarcity but connecting heterogeneous data sources across behavioral, consumer, execution, and clinical datasets to generate actionable insights for drug launches.
  • Commercial Launch Strategy: Agentic AI transforms three critical phases: pre-launch strategy including patient burden analysis and payer messaging, operational planning for HCP segmentation and territory design, and engagement optimization through automated marketing channels and personalized physician outreach to reach underserved patient populations faster.
  • Implementation Framework: Successful agentic AI adoption requires four elements: start with clear business KPIs tied to strategic goals, fail quickly through rapid pilot-to-decision cycles, ensure compliant data access with sufficient metadata for model training, and design for organizational scale including change management and workforce readiness from day one.

Notable Moment

Raja Shankar argues that refusing to apply AI to national health system datasets like NHS data due to privacy concerns is costing lives, since generating insights from this data could fundamentally change treatment paradigms for cancer, diabetes, and cardiovascular patients today.

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Episode Transcript

Hello, and welcome to the NVIDIA AI podcast. I'm your host, Noah Kravitz. AgenTek AI is reshaping the pharmaceutical landscape from streamlining clinical trials to enhancing patient engagement. Global health care intelligence company IQVIA processes data from over 1,000,000,000 non identified patient records across more than 100 countries, making our guests uniquely positioned to discuss how intelligent automation can transform health care outcomes at scale. Raja Shankar serves as vice president of machine learning at IQVIA, where he spearheads the application of artificial intelligence to transform research and development workflows in the life sciences industry. His expertise lies in developing AI solutions that accelerate clinical research and drug development processes. Avanar Broy is vice president of commercial analytics solutions at IQVIA, focusing on how AI can revolutionize pharmaceutical commercialization strategy. He brings extensive experience in leveraging advanced analytics and machine learning to optimize brand outreach and market access in health care. Gentlemen, welcome to the NVIDIA AI podcast, and thank you so much for taking the time to join. Thank you for having us. Thanks for having us. Absolutely. I really appreciate it. I know you're calling in from different time zones, had to kind of move some things to get this to work, so appreciate you guys rolling with us to get, the podcast out. Let's start with the basics, and maybe, Avanav, you can start and just tell us a little bit about what IQVIA is for listeners who might not know, and then a bit about your own role, and then, Raja, maybe you can talk about your role as well. Definitely, Noah. It's a pleasure to be here. So IQVIA is a leader in using data, tech, and analytics to accelerate innovation from clinical to commercial to drive life sciences pipeline. Right? So if you think about this, the main objective is how do we bring the drugs that are crucial for patients faster, quicker, and when they need it. Right? Right. And IQ goes the engine that is working with every life sciences company and health care organization to make that dream come true. Right? With the power of the data and analytics, which is now the the way to to approach this problem statement. In my role, I've primarily focused on the commercial business, which is basically once the drug is approved, now you have to actually take this drug and make it available for the patients. So in that, you need to understand where is your where are your patients? How do you reach them? What's the messaging? How do you make sure that every patient in every directions get those drugs at the right time for their disease test? And my job is to bring the tech and analytics together with the power of the data to make that happen. And, Raja, on your side of the house? So I'm the counterpart to Avinapp more on the r and d side. So just as he mentioned, we start, only, from a clinical …

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