Real world evidence (RWE) could transform drug development and clinical care, but most organisations still struggle to turn messy clinical data into decisions they can trust. In this episode, Dr Andree Bates speaks with Dr Danielle Bower, CEO of QuadraticMed, about bridging the “data use divide” between clinical expertise and data science, so real world data becomes usable evidence rather than noise.

Danielle explains why real world data is both more valuable and more difficult than clinical trial data. It reflects broad, diverse patient populations over longer timelines, with richer signals across labs, medications, imaging, pathology, and increasingly digital sources like wearables. But it’s also incomplete, inconsistent, siloed, and collected through real clinical judgement rather than strict protocols.

A core message is that tools don’t replace domain expertise. Danielle shares how data processing without medical context can silently change the meaning of clinical variables, producing flawed conclusions even when the analytics look “correct”. Trustworthy outcomes require the right clinical question, appropriate comparisons, careful handling of missingness, and validation against biological reality.

They also unpack what’s real versus hype in healthcare AI. GenAI is already helping with documentation and summarisation, but the bigger value is using RWE at scale to personalise treatment, detect risk earlier, and improve care efficiency. The main constraint is rarely the model. It’s data quality, governance, and cross functional communication.

Topics Covered

RWE vs clinical trial data
Why real world data is messy but essential
Domain expertise and clinical validation
Data quality, missingness, and contradictions
The real bottleneck: workflow + communication
What GenAI can and can’t do today
Regulation, privacy, and trust
Measuring success in pharma and healthcare systems

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If this episode described your situation, send me a LinkedIn DM starting with ‘SENSECHECK’ and two things: the question you’re trying to answer internally, and what’s currently in flight. I’ll reply with what I’d need to see to turn that activity into a defensible plan, and the next step.

About the Podcast

AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.
This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

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E219: Bridging the Data-Use Divide: How QuadraticMed’s Dr. Danielle Bower Bridges Medicine and Data Science to Unlock Real-World Evidence

AI For Pharma Growth

E219: Bridging the Data-Use Divide: How QuadraticMed’s Dr. Danielle Bower Bridges Medicine and Data Science to Unlock Real-World Evidence