OM DETTA AVSNITT
In this episode of TechBio Talks, host Najat Khan, PhD, incoming CEO and President of Recursion, talks to Amy Abernethy, MD, PhD, cofounder of Highlander Health, about how structured, analyzable, real-world data is transforming healthcare and medicine.
Amy is a physician, innovator, and executive who has pioneered oncology real-world evidence generation to address gaps in patient care and access.
They discuss the three “waves” of real-world data leading to today’s multimodal data; how that real-world data ties into patients’ longitudinal health story; and the difference between real-world data and real-world evidence. And they dive into how this data is driving major innovative shifts to improve clinical trials and drug discovery.
⏱️ TIMESTAMPS ⏱️
(00:00) Introduction.
(01:47) Defining Real-World Data (RWD) & Real-World Evidence (RWE): The difference between data as a byproduct of care and analyzed evidence.
(03:16) The Three Waves of RWD: From claims data to EHRs, and now multimodal, longitudinal data (genomics, imaging).
(05:49) Use Cases for RWE: Telling the story of what happened (retrospective) vs. planning for the future (clinical trial design).
(06:38) RWE in Drug Discovery: Combining deep biological data with clinical phenotypes to find new targets.
(09:14) Strategic Shift in Pharma: Moving RWE from a "replacement product" to a core strategic asset across the entire R&D lifecycle.
(11:06) Regulatory Evolution: How FDA guidance on data quality and curation is enabling broader RWE adoption.
(14:25) The Evidence Accelerator: How rapid-cycle meetings with the FDA during COVID-19 built regulatory familiarity with RWE.
(16:18) Introducing Highlander Health: A dual-model approach (non-profit institute + for-profit investment arm) to accelerate clinical research.
(21:48) AI’s Role in Evidence Generation: Using AI for data curation, study design optimization, and patient recruitment.
(24:41) The "Mantra": AI requires high-quality data; we must go back to the roots of getting the data right.
(27:47) What to Watch For: Disciplined, LLM-enabled data curation and the rise of prospective-retrospective hybrid study designs.
