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DE&I

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The scientific method is most effective when testing a hypothesis on data that best represents the population. While this might be a well-known requirement for ensuring sound science, the goal of true patient representation across clinical trials remains stubbornly elusive. Moreover, attempts at obtaining diversity in clinical trials have been half-hearted at best, often relegated to a secondary priority. Now, the Food and Drug Omnibus Reform Act (FDORA) will demand prioritization of representation in clinical trials, and companies that fall short will face repercussions.

So how can already heavily burdened companies take on this new challenge and successfully diversify their trials? Redoubling their commitment to advanced technology can help companies alleviate some of the expected financial and operational stressors, as well as help them accelerate and even increase efficiency in recruiting patients, including those who have been historically underrepresented.

I spoke with three industry leaders who are already leveraging technology to increase population representation in clinical trials. Jennifer Byrne is CEO of Javara, which provides clinical research access for patients at the point of care; Irfan Khan, MD, is CEO of Circuit Clinical, a community-based research organization; and Marie Rosenfeld is formerly senior vice president, head of clinical operations at Astellas Pharma. These seasoned professionals shared their views on how technology is key to cost-efficiently accomplishing diversity, equity, and inclusion (DE&I) goals.

Do you think FDORA will make a real impact on diversifying clinical research?

 At most companies, the top priorities when considering clinical trial design and conduct are focused on delivering scientifically valid data as quickly as possible while minimizing costs. The goal has always been to make decisions with the right data at the right time to best estimate the likely success of the product and inform future internal investment and the return on that investment.Diversity is often only discussed in later stages of development (Phase IIb/III clinical trials), and not at the top of a long list of priorities.

Would a company be willing to make a technology investment if the sole factor was to increase diversity in a clinical trial? Companies are already advertising, partnering with advocacy groups, and tracking a variety of data about patient recruitment at levels that we’ve never done before. While it’s difficult to predict whether additional, substantial investment in technology dedicated to helping meet DE&I targets would yield fruit, it’s possible. As with all investments, the return needs to outweigh the cost. I think the probability of adoption is higher when the technology can be applied across a company’s portfolio and supports other efforts. FDORA will certainly enhance the likelihood as companies will be incentivized to take this important step forward.

While regulations alone are unlikely to achieve desired solutions on their own, they work best when they outline what evolution is expected. By that bar, FDORA promises real change on an accelerated timeline. It isn’t that sponsors and sites haven’t known representation in research is a real and pressing concern—but this needed push provides the motivation to fix an inequity in ways that will pay dividends for all stakeholders.

It doesn’t take much evolution to graduate to a revolution. The movement of expanding clinical research into non-academic community healthcare pioneered by teams like Circuit, Javara, and many others provides a simple way to positively impact representation in research. By choosing to partner with health systems and physician groups that serve communities of color, we have been able to empower participation at rates more representative of the expected population. That finding is not unique to my team and suggests that when patients understand and trust the source of the information on clinical trial participation, their interest and support for participation improves dramatically.

 FDORA is unmatched in that it addresses two important realities facing our industry: diversity and modernization. When drugs are approved, we assume—falsely at times—that they have been accurately studied in the patients that will be using them. Unfortunately, that is not always the case. If the clinical trial population does not mirror the patient population it is intended to serve, we lack a clear understanding of how it will work when prescribed.

Consider, for example, Javara’s investigator-initiated, decentralized trial focused on surveillance—identifying symptoms and how they spread. Launched at the beginning of the COVID-19 pandemic (April 2020), within six weeks, we had 22,000 people enrolled, reporting daily symptomatology. What makes this number significant? Less than 1% were Hispanic, while representatively, that number should have been 12%. African-Americans comprised only 8%; they should have accounted for nearly 30%. The lack of population representation in this example was detrimental to the data and treatment options obtained through the study.

This issue is very real across clinical research and firm regulatory guidance is without doubt a key part of that solution. FDORA is different because it applies directly to all of us. I have no doubt about its ability to greatly impact diversity in clinical research.

How can technology, and specifically artificial intelligence (AI), help solve such a vexing issue?

I don’t think we have figured this out yet. AI has the potential to revolutionize how we think about clinical trial execution. I think improved DE&I in clinical trial settings will be a natural outcome of improved patient identification, utilization of digital technology, and strategic efforts to enable patient access to trials. But to see the full potential of AI in research, the entire industry needs to track closely with the modernization of healthcare. I think one of the things COVID taught us is that healthcare is still very personal and not every patient or physician wants to interact with technology the same way. This will continue to be a significant hurdle to more innovative applications of AI and technology in research.

First, it is important to define AI by today’s standards. For instance, are we talking machine learning algorithms that can do more and more with unstructured deep datasets like EMRs (electronic medical records)? Or are we thinking about generative AI tools that could potentially improve how we communicate messages across more and more specific audiences? I’m tech-adoptive by disposition and see much to love in gen-AI’s “100-foot wave.’” But I’m skeptical that any but the most niche solutions are ready for prime time when it comes to impacting something as difficult and bespoke as matching a patient to a trial and effectively engaging them. Instead, I think their greatest promise in today’s early phase is workflow automation for overworked teams trying to parallel process reams of information to understand, “who would truly benefit from this conversation about this clinical trial?” If we solve well for that phase and can improve research teams’ quality of life and quality of work [freeing them from more mundane tasks to manage a greater number of patients], then I think that’s real and relatively near impact.

 A new sector is emerging as research and care converge. I strongly believe this new space will enable us to work more effectively with the immense quantity of data available through health systems and use AI optimizations to reach those known patient populations. We start with such a wide funnel; this empowers us to get it down to where we can predict those known patients. I think AI can help us address the ethical, moral, and public health responsibility we must reach these patients with. Our inability to do so thus far is at the heart of the problem today.

A huge percentage of patients do not trust pharma or our healthcare system in general. By making a genuine, targeted effort to reach those patients, we can start building trust.

I also see AI [helping] to democratize research. It enables us to quickly look at the unmet needs of a patient population and then match those needs to a sponsor’s portfolio. It can also help ensure the fidelity of messaging and community outreach. At a recent community event, I found myself in conversation with the head of the local Black Chamber of Commerce. He exuberantly said, “Javara works with my doctor!” He continued saying how much he loved the fact that we seek individuals like him to participate in our programs. In the past, members of this community would likely have shared skepticism toward clinical research. To hear this individual express appreciation for our focus was a testament to the reliability deployed in our messaging. AI can—and is—playing a role in new processes and technologies that ensure clinical trial opportunities are offered to all patients.

But while technologies are important and make it easier to find and address certain patient populations, it is still vital that we focus on reaching patients at the point of care. It’s high-touch and how people become comfortable participating in research.

What are some of the hurdles to companies leveraging modern technology to solve this access issue?

 One of the challenges is around technology implementation. Sponsors need to enable sites and patients to utilize their own technology. The pharmaceutical industry has encountered challenges time and again from trying to force technology solutions on sites and patients. Companies build infrastructure for a perfect data flow that will theoretically save time and money, and then we try to force the solution on the sites and patients. Increasingly, this strategy results in costly workarounds for either the site or sponsor to map or fit the data to how it “needs to be” for the sponsor technology to work.

The key question has been, how to get the information sponsors need from site and patient data sources that already exist, supplementing only that data which is specific to the trial? AI has the potential to significantly change this paradigm. Sites say they are overburdened with process and technology. Trying to manage all the sponsor technology requirements not only steals attention away from patients, but discourages physicians from participating in research—ultimately, compounding the diversity problem.

 The biggest challenges are data access, data privacy, data security, and data respect. How do we leverage large healthcare datasets to engage patients at scale with a level of precision-matching previously impossible in brick-and-mortar sites using traditional radio and social media ads, yet still ensure patient privacy? Healthcare systems—particularly those that serve communities of color—are understandably cautious about granting access and under what circumstances. It comes down to trust and respect, as all partnerships eventually do. This caution is one of the great limiters for pure platform companies—the trust cycle is slower than the information and sales cycles.

Yet, scaled healthcare systems are now trying to monetize their datasets to technology vendors looking to train algorithms and to population health solution providers. Their view is that the “locked fortress” mentality has been holding innovation on behalf of patients back. At the same time, clinical trial sponsors seem more willing to accommodate trial sites and healthcare systems. Rightly so, “growth at any cost” has stopped being the focus while sustainable growth is receiving its due. The big challenge is determining how to partner with the places that patients trust, and who will fund that work given healthcare timelines are very different from those of institutional investors.

The biggest light bulb that has gone off recently in our organization is that prescribed technology strategies can be detrimental. Every situation is different. The technology we choose depends on the unique region and circumstance. For example, we have a provider partner with about 2.5 million patients and 2,500 physicians; in this situation, our clinical decision tool enables us to successfully enroll half of the patients for this trial. That’s almost unheard of. People like to say that physicians don’t refer their patients to trials, but they do. When we leverage technology to integrate clinical trials seamlessly into physicians’ daily workflows, that’s what happens.

Before we had data sources, I drove from site to site to find potential investigators. AI can be transformative for us in efficiently identifying the right fit for a trial. With modern predictive analytics, can we really lean in and look across those 2,500 physicians within our system? Can we dig deeper and get better at predicting the best sites for a study? Yes, and doing so would not only help transform trial execution but would also move the needle with diversity and population representation as well.

As with most technological processes, AI depends on data inputs. If we don’t have enough data—speaking the same language—going into the model, we will not get high-quality outputs.

One advantage we have seen through the Javara model is the ability to leverage the amount of standardized data accessible through our healthcare partners. If we didn’t have visibility into their EMR, we would face a much larger hurdle in implementing some of these valuable technologies.

Where will the industry be at the end of 2024? What progress will be made in five years?

 By the end of 2024 [and beyond], the industry will see significant investment in technology and AI. Better diversity in clinical trials will be a natural outcome of this investment. If we can develop solutions that improve access to patients and data, then we will naturally enhance inclusivity. Without an easy way to access a trial site or even to access information about a potential trial, we cannot move forward. Currently, sponsors rely on third-party patient screeners or multiple, stand-alone and costly databases of information. If we can leverage technology to get trial information to relevant patients and make it easier for patients to participate in those trials, then diversity will follow.

Over the next five years, I hope to see the corresponding changes to the regulations around clinical trial conduct. With all the advances in technology, we should be able to leverage the same types of solutions to better protect the rights of patients and ensure ethical and valid research. It’s hard to reconcile that we can leverage technology in novel ways, but we still get hung up on how to properly complete an Form FDA 1572.

We will see teams sharing their successes and their struggles more openly and more proactively. If one innovation team, sponsor, or site solves a problem in this area, there is no downside to sharing that knowledge, and plenty of collective good to be accomplished. This collaboration has already enjoyed a strong track record at conferences and informal communications, but I think we’ll see more people share prospective data by the end of the year on specific approaches they pioneered and won or learned from. I’m excited to share perspectives and evolve as a community.

The DE&I pioneers in our space lead fearlessly and by example, challenging—and empowering—us to think critically about this issue. AI is a powerful tool to support DE&I advancement, but it must be leveraged appropriately. Success depends heavily on how the product is built; factors such as who, what, when, where, and how all play a vital role in determining impact. Bias can and has been historically and inadvertently built into many systems, and AI is no exception. At Javara, we take a critical approach to evaluating the use and efficacy of such products. We know the only way to ensure equality of outcome is to ensure equality within the teams and organizations creating, testing, and making final decisions about products such as AI, and, unfortunately, this is not often the case.

The pulse oximeter, for example, is a great piece of technology, but it was not properly tested on melanated skin. Disparities in care based on race have emerged as a result. Similarly, built-in bias makes it hard for patients to navigate automated phone systems, depending, for example, on dialect or accent.

These are just two examples of the potentially detrimental impact of overlooked bias. Mindfulness is key to leveraging these tools for equity in clinical research. I believe some of the biggest concerns currently stem from human complacency, biases, and processes and can be exacerbated through AI. Those who have rushed to implement AI solutions without critical reflection about the potential for bias will likely be forced to reevaluate the role of these technologies in the near future.

Industry leaders discuss how technology can help companies adapt to the regulatory changes and accomplish their DE&I goals. 

New Legislation is Driving Diversity in Clinical Research

 held a roundtable event focused on the current state of sustainability in clinical research. A number of viewpoints were represented, including from contract research organizations (CROs), sponsors, and laboratories. Experts discussed a variety of topics, such as the carbon footprint of research, the use of technology, reaching underrepresented populations, and the clinical supply chain.

: What is the current state of sustainability in clinical research? What are currently our greatest challenges?

Bill Sterling, Senior Director of Environmental, Health and Safety, Q

: I think there’s growing enthusiasm. There’s certainly a lot of enthusiasm at Q

 for sustainability initiatives. There’s a sense of pride as employees start to see that this is a high priority and an uptake at the highest levels. That’s been exciting to see. I think things are really growing and expanding in this area. In general, laboratories can be particularly challenging in terms of finding ways to improve sustainability given labs require energy-intensive equipment like ultra-low temperature freezers and single-use plastics for safe sample collections.

Ingrid Oakley-Girvan, PhD, MPH, Senior VP of Research and Strategy, Medable:

 I’m really pleased by the number of our customers that are reaching out to ask us, “Do we have a sustainability plan? Have we started this process?” It’s really heartening to see that there is concern about that and assessing where the gaps are and areas with the greatest potential impact. If you’re just expending a little bit of energy in one place and you put all your eggs in that basket to try to solve that problem—water, or greenhouse gases, etc…from the sustainability metrics—then you can’t get that far. But if you know where there’s a big chunk, that’s the piece that you can focus on, and I think that’s part of why they’re asking us.

Cedric Burg, Chief Operating Officer, Innovaderm:

 I would agree. I would add, I think we’re not one of the big polluters, right? We’re not the mining industry or the oil and gas industry; but what our biggest impact is, apart from the facilities that you both were mentioning as well, I think is the travel. We do a lot of travel to run clinical trials. We organize those large investigative meetings, people flying in from all over the country or all over the world. And of course we’ve got CRAs (clinical research associates) going to sites quite a bit, and, not to forget, we’ve got patients going to sites as well.

I think our biggest challenge is that because we can do a lot of things, we’re still going to need to do quite a bit of travel, and the more trials we’ll do, the more travel. Unless we see a complete decarbonization of travel, we will still have that impact and have to find other ways to mitigate it.

Bryan Spielman, Chief Growth Officer, Advarra:

 It’s hard as a software company when you respond to surveys from pharma companies—we are not a manufacturer, we are not a lab, we don’t do that sort of stuff. But what we do have is a great deal of data that helps you determine what is the optimal site to perform a clinical trial, who are your best patients, what capabilities do those sites have, so that instead of going to 10 different places to hit all the different experiments that [patients] need to have done, [you discover] the one site that has all of them—so let’s direct trial activity to those places. I think using data, using analytics to help improve the operational efficiency of a trial is much better than putting my laptop on low power mode. It’s going to have a lot more of an impact, frankly. And those are some of the ways that we’re thinking about it.

With the complexity of rare diseases trials, sustainable practices might be less of a priority. Should companies face up to their environmental impact at the risk of thousands of rare disease patients potentially missing out on a therapy? What’s the trade-off? Is there a way to balance the two?

Dave Hanaman, President, Co-founder, and Chief Commercial Officer, Curavit Clinical Research: 

The direct answer in my view is no. The research, by definition, is the priority. Priority means “first”—there’s only one first we’re about: research. We can affect research. We are not going to invent the technologies that are going to save the planet. Now, that said, we can be efficient, we can be effective, we can choose sites that are efficient, and do a good job. Those are the things we can do. There is only one priority and that’s the research—and that’s for the patients.

We can and should do these other things, but not in any way, shape, or form that impacts the ultimate research.

 I think there’s also this assumption that if a patient with a rare disease enrolls in a clinical trial, somehow that has a greater carbon footprint than what they would do otherwise if the trial wasn’t available. I’m not sure that actually is an accurate assumption because [patients] could be traveling hither and thither looking for a cure. They could actually have a greater travel carbon footprint, for example, than if they just go to a specific trial site that might be in the same country versus multiple countries away.

Yes, there is a way to balance the two in terms of trade-offs. I think there’s impact in terms of human quality of life and longevity with regards to rare disease, but there’s also an impact to using dirty energy to achieve it. The emissions of greenhouse gas and other pollutants into the air have an enormous impact on human health. So it’s absolutely both. First and foremost, the reason to advance these ambitions is it’s the right thing to do. We all know that—we’re all here because we care about the environment.

There are certainly increasing regulations, and meeting those regulations is something that needs to be done. But, also, there are often cost savings that can be uncovered; often, it supports business growth. Our shareholders and our clients are pushing this program as well. It’s about aligning to a competitive market and, in addition, attracting and retaining good talent. I mentioned that a number of our employees get really excited when they’re involved in the laboratory green teams or when they see leadership championing sustainability ambition. So it has to be both.

Julia Sundari, Senior Director of Clinical Design and Analytics, IQVIA: 

I don’t think it always has to be a disadvantage to our businesses when we focus on sustainability. Because if you can be out on the forefront of this, then your sponsors are going to want to turn toward you if your carbon footprint is lower and if you can offer a clinical trial with a smaller carbon footprint and you have solutions to some of these things. [After all,] they’re looking to do that themselves.

So, from the sponsor down—everybody as a human being on the planet earth—we all really serve the end game of accomplishing this together, whether it’s at work or whether it’s personally in our lives.

 One of the longest-standing challenges in clinical trials over the years has been patient participation, especially within underrepresented populations. How can we address this to make trials more sustainable?

Greater diversity and better efficiencies are important pieces in our experience. [But] the dominant opportunity here is in speed. The faster we do something, the less energy we’re using in conducting it. And then, secondly, the adherence. The more patients we can keep in a trial, the better the adherence, the better the data, potentially, the shorter the trial, etc.

We want to make sure that we don’t forget the carbon impact. So while, absolutely, DCTs (decentralized clinical trials) can impact carbon from a positive effect, at times it’s neutral. If we’re just replacing an on-site visit with a home health nurse physically still traveling to the patient, then it may net the same amount of carbon being used. I think it’s certainly about the design, it’s about looking at how many of these things can we make virtual, how much of the data can actually be wearable, or other ways where it’s electronic data capture directly from the patient. And then, of course, the more we centralize, the more carbon neutral we become, because we hopefully don’t need to physically go to places to see that data and monitor that data.

I think along those lines, injecting technology into sites that have been less research-intensive is also important.

We are partnered with Novartis on its Beacon of Hope initiative, which is about creating centers of excellence at historical black colleges and university medical centers. Why is that good for sustainability? One, we talked about the idea of sustainability—about bringing underserved populations into the process. Two, it’s actually bringing the clinical trial closer to those underserved populations that live in those communities where those medical centers are educating doctors to be able to serve those communities. And I think it’s creating the “clinical research as a care option,” which is also a sustainability benefit.

Technology use is increasing rapidly in clinical trials, especially with the boom of AI and wealth of available data. How is it being used to make trials more sustainable?

 The way I see AI directly impacting the clinical trial space is around leveraging that information on our data. So targeting analytics and machine learning so that we can mine that incoming data remotely—physically have boots on the ground at the site but internally consuming that data and understanding where the risks are and correcting them more quickly, even in the middle of a trial. And being more environmentally sensitive to where we actually do need to send those monitors to the site.

I would say AI/machine learning has so many different applications across each of the areas, and anytime you’re driving efficiency, you’re probably doing something that’s more sustainable. With protocol design, for example, it’s making sure you get it right the first time so you can minimize the number of amendments and modifications that need to be made in the trial to the extent it’s feasible. There’s also quality monitoring, there’s the ability to ingest large amounts of data and determine which sites are our potentially problem sites, and then you deploy your CRAs to those sites as opposed to every single site, which is pretty inefficient.

There’s all sorts of applications, but I think the easiest one is trying to reduce the amount of bodies that you have to send out to various locations.

 I want to raise the other side of the issue. I think there’s also a big risk to using AI in our work, which can create a lot of work that is not necessarily needed. When I started, we had monitoring plans that were half-a-page or a page-long. Now we have 50 page-long, 80-page long monitoring plans. Because we can probably easily do it with AI, I’m afraid we’re going to turn out 180-page or 200-page documents, perfectly written and very well organized without all those typos, but that are really not going to help the monitor. That’s my worry. We also have to be careful about all the new technology because sometimes there’s a lot of hype around it.

 What are some roadblocks we are currently facing in the clinical supply chain? How can they be addressed?

 It’s really a challenge in terms of conserving energy in a laboratory. You’ve got a very large need for energy with regards to HVAC systems, fume hoods, clean air, single-use plastics, and so forth. One of the things that has been extremely helpful for us is partnering with a group called My Green Lab. It’s a nonprofit organization that essentially provides a scored self-assessment survey training across 14 different topics that map onto laboratories. Topics like cold storage, green chemistry, fume hood use, and plug load for equipment.

Starting in 2022, we launched into the My Green Lab certification process to assess our baseline practices. It created a lot of enthusiasm, a roadmap, and a structure for people to latch onto to learn about and engage various sustainability programs and projects. The most important thing is we’ve delivered some real carbon reductions and provided value for our clients. It means something to them for us to be able to say, “These sites are platinum, these sites are green-certified as the highest you can get—gold and bronze and so forth.” It gives sites a tangible roadmap and ambition to move upwards on the podium.

 That sounds like a great program. I know we’ve been working with some of our customers that want us to do what’s called a RAG, a red-amber-green assessment, every 60 days. This includes science-based target initiatives. If we’re sending out sensors, we’re thinking about the packaging, all the energy that goes into making those, how can our sensor vendors be more efficient? That’s a big space.

 In terms of sustainability goals, I find it almost funny the paradox, in my experience, of DCTs and remote data capture. The paradox is that there’s a lot of “stuff” now that you’ve got to get out to the patients. It used to be all the “stuff” was at the sites and the patients just had to get there. I think this logistics question is a really important one because the more we take advantage of these digital processes and technologies, the more we as CROs, sponsors, and companies take on the challenge of getting this stuff out to the participants in the trial.

There’s a couple of different ways to think about that. One is simply that policy and regulation can drive this a little bit. The other is thinking about medical grade products and the cost and the energy that it takes to build them. What is it we’re trying to collect from that patient? Do we need to spend ‘X’ and do it using an Apple Watch or do we have to spend ‘10X’ for some medical grade component that has a much heavier energy footprint and is thrown out after the trial?

There are so many products out there that don’t have to be reinvented for clinical trials, don’t have to be reinvented for care—but can simply be repurposed through algorithms. So just creatively thinking about simple ways to get simple products out into people’s hands.

What does having more sustainability in clinical trials look like from your perspective in five years? What do you hope the industry can accomplish by then?

I feel like we’re on sort of a wave where more and more companies are getting to the point where they’re willing to commit to their goals—their science-based target goals—and having those approved. I would say that would be the first step for wherever you sit in the business—to make sure that your company, your corporation, your part of this industry is focused on it.

I think for clinical trials at large, we touched on many of the things that I would say are probably the handholds or footholds. It’s digitizing for sure. It’s making sure that we’re using technology to our best as far as the end-to-end supply chain; making sure that we’re designing protocols as intelligently as possible; and that we aren’t incurring additional manufacturing, shipping, and travel wherever possible. However, how we can do those things most intelligently across the board is really the goal.

 And to build on that, Julia, you mentioned SBTI—the science-based target initiatives. I think transition to renewables at a number of our sites is important, either through purchase agreements or installations and packaging in kits that go out to patients. Reworking those so that they’re sustainable. I also spend a lot of time thinking about culture because I’m in environmental health and safety and sustainability; both of those things really depend on employees being energized and engaged and having a sense of ownership.

I see that happening already, but in five years, my hope is that it’s even stronger and it’s something that we see around the world. I think we have an opportunity as the younger population is coming into our workforce to take advantage of the fact that they really care about this.

 We’ve talked about a lot of technologies today, such as AI, that are creeping in upstream from us and already being adopted. So I think at a very simple level, those things will be flowing through to us and will be more sustainable as a result.

But I’m going to pick up on Bill, who mentioned the word culture. To me, the big driver here is that in five years, I actually think our industry is going to look a lot different, and I think it’s going to be because of, not the intersection, but maybe the collision of tech and health. We’re a small segment of the healthcare industry. I think the industry is going to be radically disrupted in the next three to five years by these technologies, but more importantly, by the culture and by the people that we’re going to have around us and part of us in designing these things.

I think it comes down to folks like us on this call designing these trials from first principles—not designing them the way we’ve always done and then shoehorning technology into them, but designing them from first principles with a knowledge of what actually can be done.

I think that in the next three to five years, we’ll see a massive shift in that mentality.

 I think more excitement around this as a sustainable industry is actually a good marketing position to have. Again, you want to attract younger people to clinical research, and they are attracted to sites that are greener and attracted to what’s viewed as a more sustainable process. I think that will help.

Industry leaders discuss the progress and path forward for the clinical trials enterprise in meeting its environmental mandate.

Status Check: Science & Sustainability

In a roundtable discussion at SCOPE Europe 2023 in Barcelona, Spain, industry leaders gathered to address the current landscape of data collection in clinical trials. Specifically, the roundtable focused on eCOAs, ePROs, and how they fit into a digital health world. A video of the entire discussion can be found below.

The participants in attendance were Estrella Garcia Alvarez, PhD, senior director, global clinical operations, R&D, Almirall SA, Scottie Kern, executive director, eCOA Consortium, Critical Path Institute, Bryan McDowell, vice president, eCOA clinical science & consulting, eCOA science, Clario, and Bart Roofthooft, head of digital health solutions, EBIS/DHCoE, Janssen R&D. The roundtable was moderated by Andy Studna, editor, 

To open the conversation, the first topic proposed was the industry’s current views on eCOAs/ePROs and digital health technologies (DHTs). There seemed to be a gray area around whether industry considers them to be similar entities, even though they both have the same goal of collecting high quality, patient-centric data.

Following, there was a healthy discussion around terminology in this space and the intensive work industry should be looking forward towards in order to achieve harmonization. The panel also touched on how they are seeing digital tools being used to drive patient centricity. To conclude, the participants gave their thoughts on what the audience should be keeping top of mind in this space as the calendar moves to 2024.

With the remaining time, the panel addressed one question from the audience on how patients are viewing the rapid technological advancements in clinical trials and if it is something they are truly comfortable with.

Industry leaders gathered to address the current landscape of data collection in clinical trials.

Latest Conference Coverage

SCOPE Europe 2023 Roundtable: eCOA- and ePRO-Centricity in a Digital Health World

During Cambridge Healthtech Institute’s 6th annual Summit for Clinical Ops Executives (SCOPE) Europe, which was held in Barcelona, Spain, Oct. 17-18, Andy Studna, editor of 

, was on-site to moderate a panel on the interplay of electronic clinical outcome assessment (eCOA), electronic patient-reported outcome (ePRO), and wider digital health technology (DHT) approaches in drug development.

The participants exchanged insights on a variety of topics, including terminology, regulatory obstacles, and what the future for this space could look like.

The following panel discussion has been edited for length and clarity. A video of the roundtable in its entirety can be found 

 It seems to me, through our contributed content at ACT, that the industry is viewing ePROs/eCOAs and DHTs as two completely separate entities, even though they are trying to achieve the same thing. Why is industry viewing these spaces so differently?

Bart Roofthooft, Head of Digital Health Solutions, EBIS/DHCoE, Janssen R&D:

 I think the ePRO world is already well established. So many of our preferred suppliers at J&J are able to provide ePRO capabilities with the right systems and right technologies. And ePRO is just one part of the broader eCOA system. So you have the ePROs, you have the ClinROs (clinician-reported outcomes), and ObsROs (observer-reported outcomes). But you also have the other side of digital health technologies, which is still a business that is growing and that we still need to understand a bit more. We need to understand the regulations that are coming out and we need to also see how they are impacting digital health and the end-to-end landscape. I love to talk about end-to-end because it speaks about the different technologies and different suppliers we are implementing. But it also looks at the impact that these digital health technologies are bringing. When it comes to connected devices or wearables in that sense, there’s still a lot to discover, still a lot to implement, and to assess. Also, how that helps to define the site and then the participant journey.

Scottie Kern, Executive Director, eCOA Consortium, Critical Path Institute:

 I agree with those that don’t think they’re the same, because they’re not. Digital health technologies have the capacity to administer and present an electronic clinical outcome assessment. It’s an assessment of a clinical outcome. eCOAs are not DHTs, but there is a crossover. And I think this terminology issue is fundamental to a lot of the challenges around adoption. They need to be considered differently and [while they have] similar skill sets to deal with implementation, they are not exactly the same thing.

Bryan McDowell, Vice President, eCOA Clinical Science & Consulting, eCOA Science, Clario:

 I think, potentially, they are the same thing, just to throw that into the mix. It’s just that one is better-defined than the other. I think we’re trying to separate out a couple of components where we know eCOA very well; we know the regulations are very strong around that, we know how to implement them, how to embed them, and we can move forward, whereas DHTs are still in their infancy.

We’re putting something comfortable to discuss back into one area and another piece into a different area that can cause confusion in discussions. We’ve seen the use of terminology being misused for a long time around areas such as defining decentralized clinical trials. So we’ve just got to be careful how we partition them out. [For example,] can we call eCOA a mature DHT?

Estrella Garcia Alvarez, PhD, Director, Global Clinical Operations, R&D, Almirall SA: 

I think that terminology is key. We need to agree on what each of the different elements that we have on our tables mean. The diversity is huge; ePRO, eCOAs, DHTs, etc. But we need to unify because otherwise we are losing time, in order to have everything fully integrated. I think we need to establish an agreement, because each company is talking about different things depending on their tool or the area where they are working.

At the end of the day, we need to be aligned together and fully agree on a common terminology to move on ahead to the future.

 How do we get aligned on the terminology in this space? And what barriers are preventing us from doing so?

 It’s a really important point. Based on that previous discussion, we don’t have alignment on the table here. I think there’s a couple of different factors to it. There’s definitely efforts to harmonize in the context of eCOA. Something that the eCOA Consortium has been involved with is developing a common lexicon. It took a lot of time and a lot of effort—contributions from sponsors and the PRO Consortium as well. We don’t really know if it’s being used.

The second component is that from a marketing perspective, for some technology providers, they want to create novel terms to describe what their technology does. It’s not helpful, because we end up thinking some things are different when they’re not.

I think the third most compelling issue is around the divergence in definition amongst the regulators, because ultimately, that’s what really matters. Where we have the issue of clinical outcome assessments is it being very clearly defined by FDA and biomarkers being very clearly defined by FDA, but those definitions don’t map exactly to what [the European Medicines Agency] has. And [the Innovative Medicines Initiative’s] Mobilize-D project is a great example of that. Because when they’re developing assessments and digital measures to assess mobility, they themselves define them as biomarkers. But when we look at them, they’re COAs. I feel very strongly that we need to start addressing this on a global scale.

 I fully agree with you. For us it’s not clear—imagine for the investigators and for the patient. I love technology; we try to innovate in Almirall SA in clinical trials in every single project and study, but at the end of the day, we have the patients in the core of everything we do. And if we are adding new hurdles to the investigators and site staff and to the patients, we are not going to complete our objective, which is developing drugs. I think that if we only talk about technology and how to integrate, and we don’t carefully analyze the impact on a daily basis at the hospitals and to the patients, we are going to be lost. We need to bring the hospitals on board and agree with them on the technology—the need to integrate and how we can save them the time in selecting the patients, collecting the data from the patients, etc. This is my particular point of view as a pure operations person.

 I agree, it would be great to get harmonized terminology, but we could spend 15 years trying to do that. Languages evolve into different areas and have different meanings of words. I think we’ve got to understand or be on the right page, at the very least, to have these discussions rather than [introduce] a clear, strong, one definition in a single language. That’s going to be quite difficult to achieve globally in a huge industry that spans across the world and that touches upon all the investigators, patients, etc. It’s a big challenge that we could spend too long working on rather than getting on with putting the right solutions in place to bring new drugs to the market.

Digital health is a broad term. It covers a lot of areas. You can speak about digital health capability, speak about digital health technology, or a digital system. It has different aspects depending on where the term of digital health is going to be used. Also, coming back to the operational impact to a patient or to a site, it’s really important to understand that journey from a participant perspective, [such as] implementing a tablet, a wearable, or a watch that may have additional hardware and software concerns. That also has a broader impact to the rollout of these technologies overall.

Aligning best practices in eCOA and ePRO within today’s evolving technology ecosystem.

Sirj Goswami, PhD

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