While longer wear-time is a key market goal, it creates a development bottleneck. A clinical trial for a 30-day device inherently takes at least 30 days plus analysis time. This slows iteration to a crawl and makes it imperative to develop reliable lab tests that can serve as a proxy for real-world use.

There is no inherent conflict between speed and quality. High-quality studies prevent costly setbacks and generate reliable data, ultimately accelerating research programs. A low-quality study is what truly delays timelines by producing unusable or misleading results.

Many medtech companies design large trials where a tiny, clinically meaningless response can be statistically significant. Dr. Holman advises entrepreneurs to instead run rigorous trials that prove genuine clinical value, arguing that credible data is the ultimate moat, even if it carries a higher risk of failure.

When a billion-dollar drug trial fails, society learns nothing from the operational process. The detailed documentation of regulatory interactions, manufacturing, and trial design—the "lab notes" of clinical development—is locked away as a trade secret and effectively destroyed, preventing collective industry learning.

Progress in drug development often hides inside failures. A therapy that fails in one clinical trial can provide critical scientific learnings. One company leveraged insights from a failed study to redesign a subsequent trial, which was successful and led to the drug's approval.

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 process of testing drugs in humans—clinical development—is a massive, under-studied bottleneck, accounting for 70% of drug development costs. Despite its importance, there is surprisingly little public knowledge, academic research, or even basic documentation on how to improve this crucial stage.

With over 5,000 oncology drugs in development and a 9-out-of-10 failure rate, the current model of running large, sequential clinical trials is not viable. New diagnostic platforms are essential to select drugs and patient populations more intelligently and much earlier in the process.

In high-stakes regulated fields, documentation like FMEAs is not red tape. It's a critical tool for understanding failure modes, mitigating risks, and ensuring product viability and patient safety, especially for a startup where one recall can be fatal.