Validating Innovation: AI, Commercialization, and Clinical Fit With: Dr. Bernardo Perez-Villa
Dr. Bernardo Perez-Villa, Senior Innovations Engagement Partner at the Cleveland Clinic, shares grounded insights into the true drivers of successful innovation in healthcare. Drawing on his global background in biodesign and commercialization, he discusses the real-world challenges of bringing AI technologies from concept to clinic. From assessing unmet clinical needs to understanding payer dynamics and regulatory bottlenecks, Perez-Villa emphasizes frameworks that distinguish viable solutions from hype. Hosted by Dr. Sanjay Juneja, this conversation unpacks the realities of product-market fit, digital health business models, and how to responsibly scale innovation without compromising on patient safety or financial sustainability.
Episode Contents:
About the Guest
Dr. Bernardo Perez-Villa is the Senior Innovations Engagement Partner at the Cleveland Clinic. He specializes in the evaluation and commercialization of healthcare technologies. More about Bernardo
Key Takeaways
- Begin with unmet clinical needs, not tech capabilities.
- Validate the market before building the solution.
- Regulatory and payer realities shape innovation outcomes.
Transcript Summary
How Do You Ensure an AI Innovation Solves a Real Problem?
Q: What is the biggest reason most startups fail?
A: Lack of an unmet clinical need and poor product-market fit. Perez-Villa explains that many founders start with a solution and try to retrofit it into a market, often skipping validation with actual users or ignoring payer incentives.
Q: How should startups validate their solution?
A: By doing primary and secondary market research. Talk to end users without pitching a solution. Understand their pain points, then determine if a pattern exists. Supplement this with literature reviews and social platforms like Reddit and Google Trends.
What Makes a Digital Health Business Model Sustainable?
Q: Why do AI startups struggle with sustainability in healthcare?
A: The U.S. system is largely fee-for-service. Without a CPT code and reimbursement structure that exceeds production costs, even great technologies fail to scale.
Q: What are the components of reimbursement?
A: Coding, coverage, and payment. Perez-Villa breaks down how a CPT code is only one part of getting paid—and how reimbursement rates can make or break a business.
What Innovation Pathways Work Best in Healthcare?
Q: When should a technology be spun out as a startup vs. licensed?
A: Broad-platform technologies with cross-specialty applications and scalability potential are best suited for startups. Incremental innovations are better for licensing.
Q: What's misunderstood about "disruptive" technologies?
A: True disruptive innovation targets non-consumers and is initially less effective than existing solutions. Over time, it evolves to outperform incumbents. Perez-Villa uses ChatGPT and iPhone evolution to illustrate.
What Threatens AI's Future in Healthcare?
Q: What risks could derail AI in healthcare?
A: Perez-Villa points to hype-driven duplication, regulatory lag, and unsustainable business models. He emphasizes the need for realistic commercialization plans and infrastructure efficiency.
How Can We Improve Interoperability and Data Ownership?
Q: Why is interoperability so hard in digital health?
A: It’s not a technical limitation but a matter of incentives, business models, and data ownership. He explores blockchain and fractional data ownership as emerging frameworks.
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.