Using Behavioral Science to Improve Clinical Trials

Marc Eigner, executive chairman of the board at Datacubed, details the ways his company is improving clinical trials.

Marc Eigner

Marc Eigner
Executive chairman of the board
Datacubed Health

Datacubed Health, an e-clinical technology company, recently announced the appointment of Marc Eigner as executive chairman of the board. Eigner spoke with Medical Device & Technology about why he decided to take the position, along with how new technologies are helping to improve clinical trials.

(MDT:) What are the issues that pharma companies are facing with clinical trials?

Eigner: By the time a company brings a molecule or device into clinical trials, it’s already gone through so many hurdles just to get to that point. The percentage that get through is so small already, so if you think about any issues with the logistical or operational side of clinical trials, it’s very costly for pharma. By far, the biggest issues going on now are patient recruitment and retention. Prior to COVID-19, there were already issues with recruitment that stems from people not knowing about trials, logistical issues with getting them into clinics, etc. After COVID, diversity of the types of potential patients recruited is also being recognized as an issue. COVID really demonstrated to the public why we need more diversity in clinical trials because even if there was no scientific basis for wondering if vaccines worked on different populations differently, people realized they weren’t being tested in diverse populations. There was a public curiosity of the safety and efficacy of vaccines and it really brought that issue to light.

Another issue is the immense amount of data that must be managed as trials get more and more decentralized. The data is being collected all different places and not necessarily from inside a clinician’s office. It is now on somebody’s phone, in their house, while they’re moving, in transit, and all different places. Clinical trial data was already a challenge to gather and collected correctly and make sure that it’s not being lost and being validated. Adding the decentralized component takes this a step further in challenge.

It sounds very basic. Making sure you find the right people, making sure you’re doing the right thing, and then getting the data back to the company. It turns out, however, that these are probably the biggest issues with drug development across the industry.

(MDT:) What sorts of technologies are you using to make these trials more efficient?

Eigner: If you think about the decentralized clinical trial world, the way to really make sure that the data is captured in an efficient manner and goes back to the sponsor of the trial is by having tools and technology that make that process seamless. What we have to offer is a product that allows for e-clinical outcomes assessment. It allows the patients to provide their outcomes and it gets back to the sponsor. That helps solve issue number two that we talked about in the previous question.

If you think about a tool that a patient is already using to report these outcomes, if you can do both patient reported outcomes and actually make sure that the patient is engaged and doing what they’re supposed to be doing in the trial, it’s really something that’s perfect for the same piece of technology.

Our app engages the patient, reminds them of what they’re supposed to be doing, and it gathers the outcome from the patient. After all that, it sends it back to the sponsor of the trial in a format that’s validated and able to be aggregated into the data analysis.

What we’re seeing at Datacubed is that having a very patient-centric, easy to use tool actually increases compliance and diversity. Within our application, patients can personalize it so that the little icon that represents the person so that it looks like you. In a lot of behavior science, if patients see an icon that looks like them, they’re more likely to be involved, engaged in the trial, and report their outcomes.

(MDT:) What made you want to take your career in this direction?

Eigner: I spent a long time being an entrepreneur, which I loved, but at some point, the 60 hours weeks and travel was a bit much. I decided that a board position was the right way to go. I like the clinical space a lot because we really saw both the pro and cons during COVID. The speed at which the vaccine was released was amazing. I understand that people don’t want to talk about the plus sides of COVID because there’s not a lot on the plus side, but it was an amazing thing to get those vaccines out to the general public as fast as they were.

We also started to see some of the challenges of clinical trials, like the diversity issue I previously brought up. I decided I wanted to back, get on a board, and use all of the experience that I have to really help entrepreneurs to really help get patient help. I decided to be a board member of a clinical software company. I picked Datacubed because I loved the idea of combining behavioral science with clinical technology. I knew right away that behaviorial science would solve the top issues with clinical trials.

(MDT:) Can these technologies and solutions be applied anywhere else in the drug development process?

Eigner: Maybe not as much in the drug development process, but in the end-to-end process of healthcare. We definitely see a use for these technologies for patient adherence. If you asked me what the biggest issues in healthcare, as opposed to specifically in clinical trials, I would say that number one is patient adherence. I have high cholesterol, I work in the healthcare industry, but I sometimes just don’t take my medicine. Why? I have three kids, I’m on four boards, and for whatever reason, I don’t always take my pills. If you think about all of the work that went into getting these pills to market, we fixed my cholesterol problem and yet I don’t take these products sometimes.

Making easier ways for patients to take their pills, and record that they’re pills and outcomes post taking their pills, patient adherence is the next big challenge to solve.