Would You Let a Loved One Join a Trial?

Reema Alsayegh’s career has moved across academia, consulting, global pharmaceuticals, and now biotech. It has consistently returned to the same place: the patient. During the COVID-19 pandemic, she worked with mobile research networks visiting long-term care facilities and delivering mRNA treatments, seeing treated patients face to face every day. 

Today, at Immunovant, she works on the research and design side of clinical development. There, she shapes the protocols that bring medicines to the people who need them. Her focus on patient recruitment and engagement grew out of a recurring gap she saw across healthcare. Patients having to fight for the treatments and the care they require. 

She has since built a practice around advocating for those patients. Supporting the sites that treat them and acting as a reality check on what participants can feasibly be asked to do. Beyond her operational remit, she mentors young women entering STEM and clinical research, and she argues that leaders at every level owe the industry the same investment.

How would you describe your work to someone outside clinical research?

Reema Alsayegh explains why patient advocacy begins long before a participant is ever enrolled.

“Everyone outside the industry has a somewhat stigmatised understanding of clinical research. People think of guinea pigs, or of testing. I would say that I work to bring medicines to the people who need them. I work on the research and design side, which means I get to work on the protocol development that creates the protocols that bring those medicines to the patients who need them.

We start as early as protocol development. I provide feedback on what is realistic for patients and what is simply too much of a barrier. Fifteen needle pokes in one visit is ridiculous, for example. Or even the number of times a patient would have to come into the clinic. I often do a reality check on what patients can feasibly do.

Ultimately, we do not have our hands on those patients. It is our sites doing that. So we provide whatever support and feedback we can for our sites to ensure it is a positive experience for everyone. From our investigators to our clinical trial sites, all the way down to the patient. That is my overall goal.”

What is the biggest misconception about why studies struggle to enrol participants?

Reema Alsayegh argues that enrolment falters when participants are treated as data points rather than individuals.

“A theme I have seen is that patients often feel like numbers. Especially in these big sponsor trials, they do not feel valued as individual patients. What I have seen that has the most impact on stronger, more successful recruitment is making participants feel special. Making them feel heard and seen as an individual patient rather than as a member of the overall clinical trial.

The solutions I bring are driven towards helping those patients feel seen. To feel understood in what they are actually going through and in their symptom management, and feel like they are driving towards a greater good rather than just signing up for something and having to come into a clinic every week to collect data.

Most people have no relationship with clinical trials until they absolutely need one. By that point, you have exhausted all other options, so a trial is hope. That hope is very vulnerable. Patients are putting themselves on the line of potentially receiving a placebo in order to find a treatment for their disease and for others. And I want to make that as positive an experience as possible.”

Where does AI genuinely improve recruitment, and where does it miss the mark?

Separating practical utility from the industry buzzword, Reema Alsayegh identifies the decisions that still require a human.

“AI right now is the buzzword. We are seeing it in recruitment vendors, and we are seeing it all over the place. AI’s strength is in collating and separating data. That might be heat mapping where patients are located so you can target advertising in those places. Or collating where sites are located and running a very rough feasibility on them.

What I have seen is that the more value-driven decisions, such as site selection or the types of advertising we use to target the recruitment of participants, still need to be very specialised. And AI has not been strong in those places. I have seen AI-created patient recruitment advertising, and it just misses the mark on what our target demographic and those participants really look like.

I want patients to see our advertisements and see themselves, as though they are looking at themselves in the mirror. Or to feel seen and understood as a patient with that disease. AI tends to deliver what you would consider standard marketing, without that nuance around the disease state and the patient population.”

What changes will have the biggest impact on participation over the next three to five years?

Reema Alsayegh describes the shift towards decentralised trials, and the data trade-offs that come with it.

“I am seeing a huge push in decentralisation and digitalisation of clinical trials, which is long overdue. It really drives that convenience factor for our patients. That means more visits from home via telehealth, collecting some data from home, providing patients with scales so they can take their own weight measurements, and much more training around self-injection and how to prepare and administer drugs at home.

People are busier. The idea of coming into a clinic once a week or once a month is getting more and more difficult. The more we can cater around their lifestyle and make it more convenient, the higher the numbers you are going to see in terms of retention and recruitment. I also expect more mobile imaging centres and stronger online pre-screening to help alleviate some of the site burden.

There is a downside to everything, and data is the first thing that suffers. What that means is that the data points collected in clinic become more key. For a GLP-1 study, a weight measurement is very key. So the ones we collect in clinic become that much more important than the ones we collect at home. Those are more for monitoring and trend analysis. There are ways to accommodate for that through the measures and the stop gaps you build into the protocol design.”

What is the one difficult question every clinical leader should ask before launching a study?

Reema Alsayegh returns to the personal test that has governed her decision-making throughout her career.

“Something that has driven my career is that I always put myself in our patients’ shoes and ask: would I do this trial, or would I let a loved one do this trial? That has governed my decision-making on everything.

I think if everyone did that, we would see less of a need for patients to advocate so heavily for themselves. What we often hear is that patients are exhausted by the process of finding care and getting the care they deserve. If we could change our healthcare system to offer that naturally from the very beginning, as early as clinical trials, that would be a better process for everybody involved.

I would also urge leaders not to forget to mentor and to share their expertise and the learnings from their career. The way I give back is by mentoring young women who want to come into STEM and into clinical research. Getting to shape our next generation of leaders is not only rewarding. It is also a way to leave a lasting legacy in your industry.”

Join the Conversation: The Ortus Club’s Executive Network

As Reema Alsayegh has explained, the hardest problems in clinical research are rarely technical. Enrolment, retention, and trust are human problems, solved at the intersection of protocol design, site support, regulatory constraint, and marketing craft. Leaders working across those boundaries need somewhere to test their assumptions against peers facing the same pressures.

Her call for breadth over narrow specialism reflects a broader reality. As biotech teams get smaller and more nimble, leaders are expected to step into regulatory, quality, and commercial territory that used to belong to somebody else. That kind of expansion is far easier when it is informed by dialogue with people who have already made the same move.

At The Ortus Club, we host curated executive roundtables that bring together senior healthcare and life sciences leaders facing exactly these challenges. Step away from the buzzwords and into the kind of open, high-value conversation that turns patient-centricity from a stated value into an operating model.

FAQs

Q: Why do clinical trials struggle to enrol participants?

A: A significant factor is that participants can feel like numbers rather than individuals, particularly in large sponsor trials. Recruitment improves when patients feel heard, understood in their symptom experience, and connected to a wider purpose.

Q: What is a decentralised clinical trial?

A: It is a trial model that moves activity out of the clinic and towards the participant, using telehealth visits, at-home data collection, self-administered drug regimens, and mobile imaging to reduce the burden of participation.

Q: Where is AI actually useful in patient recruitment?

A: AI performs well at collating and separating data, such as heat mapping patient populations for targeted advertising or running rough site feasibility. It is weaker on value-driven decisions such as site selection and disease-specific creative, which still require specialist human judgement.

Q: What skills will the next generation of clinical research leaders need?

A: A broader understanding of end-to-end trial delivery rather than a single area of subject matter expertise. In smaller biotech teams, leaders are expected to contribute across regulatory, quality, marketing, and AI-related questions and to draw on the collective knowledge of the team.

Q: How does at-home data collection affect data quality?

A: It shifts weight onto the data points still collected in clinic, which become the primary source for key endpoints. At-home measurements are typically used for monitoring and trend analysis, with the trade-off managed through stop gaps built into the protocol design.