R&D departments often receive reactive briefs from commercial teams, leading to generic products. The goal should be to 'leapfrog the brief' by conducting deep user research independently. This allows R&D to proactively propose innovative solutions based on future user needs, rather than just executing marketing's requests.

Before any technical work begins, a lab must decide whether to build a custom data solution or purchase a vendor tool. This choice hinges on anticipating future growth and changing needs, even when those needs are not fully clear at the outset.

Despite an existing academic natural history study (Procstar) for Stargardt disease, AAVantgarde invested in running its own. This gave them a more rigorous and consistent dataset, collected with modern instruments over a shorter period, highlighting the strategic value of controlling baseline data for future pivotal trials.

To combat the data equity problem where wearable users are often affluent, ŌURA actively partners with research organizations. By donating thousands of rings for studies on specific groups (e.g., women with diabetes), they acquire diverse datasets essential for building inclusive and accurate health algorithms.

Standard retina clinics lack expertise in testing blind patients. Ray Therapeutics proactively built its own Vision Research and Assessment Institute two years before its trial to run natural history studies with low-vision experts. This allowed them to define appropriate endpoints and reduce data variability.

A key advantage of in-house genomic assays, like MSK's, is the ability to rapidly iterate based on direct feedback from practicing clinicians. This agile development cycle allows the test to be continuously updated with new genes and regions of interest, keeping it at the cutting edge of clinical and research needs.

ŌURA rejects a one-size-fits-women approach. Instead of a single tailored algorithm, they take the "hard road" by building distinct, customized models for different physiological states like perimenopause, pregnancy, and hormonal birth control. This deeper level of personalization is key to advancing the field.

Abridge's secret weapon for building clinically relevant products is the "clinician scientist" role. These are team members with clinical backgrounds (e.g., MDs) who are also deeply technical. By embedding them in product teams, the company ensures that clinical usefulness and safety are baked into development and evaluation from day one.

Simply patching existing Electronic Health Records is insufficient. The next generation must be architected from the ground up with three core principles: offline functionality for resilience, a mobile-native experience, and generative AI at their core.