How AI Could Change a Doctor’s Daily Workflow
In this insightful discussion, Pelu Tran explores the practical role of artificial intelligence in enhancing clinician productivity and navigating the widespread inefficiencies in healthcare. Drawing from Ferrum Health’s platform, Tran explains how AI enables faster diagnosis, supports care coordination, and amplifies provider capacity—particularly in overburdened fields like radiology. He also highlights the real cost of AI adoption, pointing to the significant resources required to integrate AI into existing healthcare systems. This conversation sheds light on the evolving impact of AI for healthcare productivity and reframes the debate: it’s not just about quality vs. efficiency—it’s about enabling clinicians to sustain care at scale.
Episode Contents:
About the Guest
Pelu Tran is the cofounder and CEO of Ferrum Health. He studied both medicine and engineering at Stanford University and was four months away from receiving his MD when he launched his first company.
Notable Quote
80% of what a clinician does is unnecessary for optimizing outcomes—AI helps reclaim that time
Key Takeaways
- AI reduces inefficiencies by parsing large volumes of patient data in seconds.
- Radiology and care coordination are high-impact areas for AI-driven productivity.
- The major barrier to AI adoption is the organizational cost of deployment, not lack of value.
- Ferrum Health accelerates AI onboarding through a centralized orchestration platform.
- AI in healthcare is not just about quality or efficiency—it’s about enabling sustainable care delivery.
Condensed Transcript
Q: How will AI affect a physician’s workflow?
Pelu Tran: 80% of healthcare data—and 80% of what clinicians do—is unnecessary for optimizing patient care. AI can rapidly parse patient records, identify relevant data, and drastically reduce manual review time. This allows doctors to focus on what truly impacts outcomes, mitigating workforce shortages and productivity pressures.
Q: Can AI drive measurable revenue or RVU gains?
Pelu Tran: Absolutely, especially in areas like radiology and care coordination where AI has matured. Tools that enhance clinician confidence or reduce time spent per case lead directly to more RVUs and better productivity.
Q: What’s blocking AI adoption if the value is clear?
Pelu Tran: The biggest barrier isn’t value—it’s cost. Health systems face major expenses onboarding and integrating each AI tool. While many already know which models they need, deployment costs remain a key hurdle.
Q: How does Ferrum Health help streamline AI deployment?
Pelu Tran: Ferrum reduces onboarding time to weeks and offers a unified architecture for deploying multiple AI tools. This reduces integration complexity and vendor lock-in, making AI adoption more scalable and sustainable.
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.