Despite the depth of personal genomic testing, primary care physicians cannot integrate these consumer-generated results into official medical records. This reveals a significant gap between the potential of consumer health tech and its practical application in clinical settings.

We possess millions of data points on interventions, but they are useless to AI models because they're trapped in thousands of disparate EMRs in varied formats. The challenge is not generating more data, but solving the human incentive and alignment problems required to create unified data registries.

While speed is crucial in clinical trials, the ability to transfer data across borders is a growing pain point. Data privacy regulations, particularly in China, complicate due diligence and global collaboration, making data portability a key factor for efficiency and a significant hurdle for M&A.

Novartis's CEO highlights a surprising inefficiency: clinical trial nurses often record patient data on paper, which is then manually entered into multiple digital systems. This archaic process creates immense friction, cost, and risk of error, representing a huge, unsolved "boring problem" in biotech.

Regeneron's Genetics Center is a key competitive advantage, functioning as a discovery engine for new drug targets. By sequencing millions of patient genomes and linking them to health records, it allows Regeneron to identify novel genetic variants associated with diseases, feeding its antibody development pipeline with proprietary targets.

To reach its goal of 20 million sequenced individuals, Regeneron plans to use tokenization to link de-identified EHR data from one source (like a hospital) with biospecimens from another (like LabCorp). This strategy moves beyond single-institution collaborations to massively scale its ability to create linked datasets.

To overcome the scaling challenges of traditional biobanks, Regeneron is pioneering a new model. They partner with companies specializing in aggregating de-identified health records and, separately, with groups handling bio-sampling. This "uncoupled" approach allows them to link massive, independent data streams to achieve unprecedented scale.

To scale its database from millions to tens of millions, Regeneron is moving beyond bespoke global studies. The new model involves large-scale partnerships with health systems and consumer data sources, using privacy-preserving tokenization to securely link genetic data with vast electronic health records.

The primary bottleneck in drug development isn't creating therapies but identifying the right targets. Regeneron built its massive genetics database to find rare, protective genetic mutations in humans, effectively de-risking the target identification process and aiming to improve the industry's low success rate.