How Digital Twins Impact Recruiting During Research

One of the most discussed use cases in pharma and biotech for AI is the creation of digital twins. By using massive amounts and data, the technology can create a digital recreation of a real person, which can then be used for various research purposes.

Pharmaceutical Executive spoke with Hamish Brocklebank, founder of Brox.AI, about some of the ways that digital twins are being used within the industry, and where the technology could potentially head in the coming years.

According to him, these twins have a variety of uses. One of the more commonly discussed use cases is for pure research. In this instance, a researcher creates a digital twin and then uses AI to simulate the effect of various molecules and conditions. However, Brocklebank explains that there are other use cases which may provide just as much, if not more, value.

Pharmaceutical Executive: How do digital twins impact recruiting for research?
Hamish Brocklebank: Recruiting these participants is genuinely difficult — many of them are hard to reach. A good example is a panel we have been building over the past month in oncology. I cannot name the specific drug, but we are building a panel of physicians who prescribe a highly specialized therapy for pancreatic cancer. There are perhaps a thousand of these doctors in the United States, and we have recruited 80 of them — roughly 8% of the entire eligible population. We think that is a significant achievement for a niche like this.

So how do we do it, and why do doctors participate?

The first reason is compensation. To be direct: our business model allows us to pay physicians significantly more than the industry standard. Whereas a typical research firm might pay an oncologist $500 for an hour of their time, we can economically justify paying $2,000 or more, because each clone we build can be used repeatedly across multiple engagements. Building a high-quality clone requires roughly four to five hours of a participant’s time across structured interviews, so the investment on both sides is meaningful.

The second incentive is equity participation. For our physician panels — and we also run a high-net-worth consumer panel, though that is less relevant here — we give participants the option to receive share appreciation rights in our company instead of, or in addition to, monetary compensation. Many doctors find this appealing because it gives them a way to participate in the growth of the AI ecosystem more broadly.

The third element is the panel application itself. We built a product called Melivora specifically for our physician panel. Through it, participants schedule both human-led interviews with our team and AI-led interview sessions. After their clone is built, they can actually query their own digital clone and benchmark themselves against other physicians in their cohort — seeing how their prescribing behavior compares to peers, for example. A meaningful number of doctors engage with the platform simply out of curiosity, independent of the financial incentives.

Taken together — strong compensation, equity upside, and a genuinely engaging product — those three things are how we have built and retained our panels. We apply the same approach to our patient panels and general consumer panels.

One final point I want to emphasize: we operate on a triple opt-in consent model for everything. That means any participant can have all of their data removed at any time, even after we have compensated them, with no questions asked. In the pharmaceutical space, where the sensitivity of the data we collect is significant, data security, privacy, and ethics are not afterthoughts — they are foundational to everything we do. Every dataset we use is designed to be not only grounded, real, and high quality, but also to carry the highest possible standard of consent.