The key public health failure during the pandemic was not initial uncertainty, but the systemic inability to execute rapid experiments. Basic, knowable questions about transmission, masks, and safe distances went unanswered because of a failure to generate data through randomized trials.
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Critical knowledge on how to run clinical trials is not formalized in textbooks or courses but is passed down through a slow apprenticeship model. This limits the spread of best practices and forces even highly educated scientists to "fly blind" when entering the industry, perpetuating inefficiencies.
Establishing causation for a complex societal issue requires more than a single data set. The best approach is to build a "collage of evidence." This involves finding natural experiments—like states that enacted a policy before a national ruling—to test the hypothesis under different conditions and strengthen the causal claim.
Top-down mandates from authorities have a history of being flawed, from the food pyramid to the FDA's stance on opioids. True progress emerges not from command-and-control edicts but from a decentralized system that allows for thousands of experiments. Protecting the freedom for most to fail is what allows a few breakthrough ideas to succeed and benefit everyone.
A COVID-19 trial struggled for patients because its sign-up form had 400 questions; the only person who could edit the PHP file was a grad student. This illustrates how tiny, absurd operational inefficiencies, trapped in silos, can accumulate and severely hinder massive, capital-intensive research projects.
The most valuable lessons in clinical trial design come from understanding what went wrong. By analyzing the protocols of failed studies, researchers can identify hidden biases, flawed methodologies, and uncontrolled variables, learning precisely what to avoid in their own work.
The traditional drug-centric trial model is failing. The next evolution is trials designed to validate the *decision-making process* itself, using platforms to assign the best therapy to heterogeneous patient groups, rather than testing one drug on a narrow population.
Despite strong observational evidence from Israel suggesting early allergen exposure was beneficial, medical guidelines didn't change. It required the "gold standard" of a randomized controlled trial (the LEAP study) to definitively prove the link and force institutions to formally reverse their harmful avoidance recommendations.
Widespread adoption of preventive health measures faces a major political hurdle. Politicians on four-year election cycles are incentivized to fund programs with immediate effects, rather than long-term prevention initiatives that may take 20-30 years to show results.
Reflecting on his PhD, Terry Rosen emphasizes that experiments that fail are often the most telling. Instead of discarding negative results, scientists should analyze them deeply. Understanding *why* something didn't work provides critical insights that are essential for iteration and eventual success.
In high-stakes fields like medtech, the "fail fast" startup mantra is irresponsible. The goal should be to "learn fast" instead—maximizing learning cycles internally through research and simulation to de-risk products before they have real-world consequences for patient safety.
