Can AI Transform Drug Repurposing Policies? With: Bob Battista
In this wide-ranging interview, Bob Battista explains how AI can speed drug repurposing—if data can flow across today’s regulatory and commercial silos. He points to untapped knowledge inside pharmaceutical call centers, patient-reported outcomes, and physician off‑label use that rarely reaches researchers. Battista argues that safe-harbor policies for pharmaceutical data sharing would unlock thousands of potential indications hiding in plain sight, while AI organizes mechanistic and clinical evidence for decision makers. The conversation also explores living clinical guidelines, earlier diagnostic staging, and the economics that slow evidence adoption. For healthcare leaders, the message is clear: pair modern analytics with governance reform to expand access, improve outcomes, and strengthen patient empowerment in healthcare.
Episode Contents:
About the Guest
Bob Battista is a board member, founder, and health data innovator known for evidence-first approaches to market access and guideline design.
Key Takeaways
- Create safe-harbor rules so pharma can share nonpublic data for repurposing without eroding core markets
- Capture physician off‑label use and patient‑reported outcomes to surface new indications systematically
- Use AI to personalize guidelines and stage care earlier to cut costs and improve outcomes
Transcript Summary
Q:What is the biggest hurdle in drug repurposing today?
A: Sharing knowledge across regulatory and commercial firewalls. Much of what we need sits inside pharma—call center logs, patient‑reported outcomes, and internal R&D—yet cannot be shared or reused at scale.
Q:Don’t AI and real‑world data solve this already?
A: AI can map mechanisms and evidence, but the most valuable data remains behind the firewall. Safe‑harbor policies and financial instruments are needed so companies can share data and let others pursue niche indications without harming core markets.
Q:How do physician and patient insights matter?
A: Off‑label use is widespread and often effective. Patients also learn what works, including dosing. Systematically aggregating this knowledge can reveal strong repurposing signals.
Q:Where can AI improve care right now?
A: Dynamic, individualized guidelines. AI can read the patient chart and the literature to suggest earlier staging and better diagnostic choices, which reduces downstream costs and delays.
Q:What governance shifts would help?
A: Modernize privacy and data‑sharing rules, make records easy for patients to access, and study practices from integrated systems and ACOs to align incentives for earlier, evidence‑based care.
More Topics
- AI in Patient Care
- AI in the Healthcare Industry
- AI and Medical Innovation
- Healthcare Ethics and Policy
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About the Series
AI and Healthcare—with Mika Newton and Dr. Sanjay Juneja is an engaging interview series featuring world-renowned leaders shaping the intersection of artificial intelligence and medicine.
Dr. Sanjay Juneja, a hematologist and medical oncologist widely recognized as “TheOncDoc,” is a trailblazer in healthcare innovation and a rising authority on the transformative role of AI in medicine.
Mika Newton is an expert in healthcare data management, with a focus on data completeness and universality. Mika is on the editorial board of AI in Precision Oncology and is no stranger to bringing transformative technologies to market and fostering innovation.