The Snail

In our last blog post in the “Mosaic Medicine” series, we introduced the concept of “The Snail”: a way to conceptualize and visualize the evolving evidence generation industrial complex. Diving again into clinical evidence generation of the past, we tend to think and operate in a stepwise and protocolized manner. Phase 1 trials flow into phase 2 and phase 3 studies, resulting in eventual approval for the lucky few products that demonstrate safety and effectiveness. Post approval, we encounter a data void due to the lack of systematic evidence generation. This is because the intervention is prescribed at-will by health care professionals and any/all data generation is based on what the current infrastructure is able to capture from that uptake. The image below maps this concept onto our Snail diagram.

This story is beginning to change. We are seeing earlier approvals based on shorter, smaller studies. Both in practice and in policy, we are seeing FDA approve medical products with a lower bolus of evidence, propelled by deeper understanding of biology and updated regulatory pathways. To accommodate this move towards earlier approvals, treatment evaluation has shifted into the post-approval space. Once siloed, pre-approval and post-approval data are now being blended. In some cases, they are mutually reinforcing. The Modern Snail is depicted below:

Supporting this shift, post-market evidentiary requirements are becoming more commonplace, as are pragmatic clinical trials, real-world evidence, and other approaches. Data collection is beginning to become continuous during a therapy’s life cycle, with these new data sources providing more granular evidence. Effectively leveraging available post-market data could help empower clinical trials, which are often completely blind to how participants live their daily lives. In addition, access to comprehensive, post-trial data would provide greater insights into a clinical intervention’s long-term impacts. As data from multiple sources coalesces, it could inform more efficient trial design, creating a truly beneficial feedback loop. 

One of the most interesting areas where we see this play out is cell and gene therapy approval and evaluation. These paradigm-shifting interventions typically carry near-curative short-term effectiveness benefits in very small patient populations, with large open questions about long-term effectiveness and safety. Accordingly, we see FDA requiring up to 15 years of detailed follow-up for patients receiving these products.

The concept of coalescing our various data sources is explored in-depth in a recent JAMA publication that discusses the imperative to modernize the underlying data infrastructure to institutionalize this shift in clinical research activities. At Highlander Health, we have kicked off a series of grant projects that seek to quantify the investment needed to modernize said data infrastructure. We look forward to providing more updates on these projects in the coming months. In our next “Mosaic Medicine” blog, we will highlight one of the biggest challenges associated with modernizing our data infrastructure: the rise of data mosaicism.

Read Part 1 of the Mosaic Medicine series here.