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1rem

Strategies for ensuring that innovation with PROs remains parallel with advancements in disease detection and progression.

Modernizing Patient-Reported Outcomes:  Bridging the Gap Between Legacy Measures and Contemporary Healthcare

Study uncovers insights on the impact on safety, patient enrollment/retention, and compliance.

Benchmarking Risks Across Therapeutic Areas

The challenges, opportunities, and strategic outlook for oncology research centers. 

Scaling eSource-Enabled Clinical Trials: Hospital Perspectives

Exploring the different stages in the development of a digital measure, and the activities and steps key to achieving meaningful impact for patients. 

Digital Measures of Health: What’s in it for Patients? 

Understanding the benefits and concerns associated with implementing datasets into clinical trial workflows.

The Backbone of Innovation: High Quality Datasets

In this video interview, Rob Jones, product manager, TMF practice area, Pharmalex, talks AI and how change is needed for the clinical trials industry to fully understand its potential with automation.

A Roadmap for the Use of Artificial Intelligence in Managing Trial Master Files

, Rob Jones, product manager, TMF practice area, Pharmalex, discussed challenges in the trial master file (TMF) space, highlighting outdated regulations such as ICH E6(R3). When it comes to innovation and compliance in the space, he stressed the importance of a risk-based approach, focusing on critical records and leveraging artificial intelligence for big data analysis. Moving forward, Jones believes the TMF community's collaborative nature can be viewed as a strength in navigating regulatory changes and technological advancements.

ACT: How are recent regulatory changes allowing for more advances with technology and AI?

The short answer: they haven't. That's the short answer of it is they haven't really embraced this idea of AI. They're talking about it now. They're talking about these new technologies, but we're still in a world where we don't want to commit to this idea of with a TMF, you have to manage it electronically. You can run an entire trial today in paper and the regulations for that, the requirements for that, are down here, whereas if you say, “Oh, I'm doing it electronically,” they're up here. The simple answer is they haven't, but that doesn't mean that they've also said we can't use them. What they've started to do is tease with that idea of AI coming in, tease with that idea of new ways of working, and I think our industry is ripe for opportunity. Within our TMF service group alone, we're managing thousands, tens of thousands documents a day. You scale that up by weeks, months, years, we're talking millions of data points. That's ripe for AI, that's ripe for all these things that we've seen and I think the thing we need to look at, really, is just sensible ways of how to use it. On one side, we can really look at having a look and saying, “Okay, what about automation? What about processes and new solutions.” Electronic TMFs were an interesting new technology in that the fear mongering of the 90s was computers were going to steal all of our jobs. I actually feel there's probably more jobs in TMF today because of the electronic TMFs than there were when it was paper. We've already brought in different ways that we're using AI to help identify records as they're uploaded, and give suggestions to people of: “We think you should file it here, or this looks similar to these documents you've filed over here.” Using it in that way has those benefits for us, and again, there's nothing that says we can't utilize these.

The second biggest area is big data. Again, millions of data points here, that's before we even start coming into metadata and all the other stuff that we collect, so these things that are coming up now in in a lot of other industries, of AI agents, being able to ask, “How's this going in this country? What am I missing over here?” and having something that has that holistic view that can guide you, it's not going to replace anybody, by any stretch, but it's definitely going to help us. I think really, there's a real opportunity here for someone who's willing to push that boundary, someone who's really willing to be a first mover. AI isn't scary, the current trend going around that I've seen on LinkedIn is everyone's turning themselves into action figures. That's not scary. There's fun to be had, and there's interesting ways we can use it. I think the thing that we're going to have to bring in, which is the scary part, is a lot more logical thinking. A lot of the time we've been used to old ways of computerized system validation, which is: “I've got requirement, I have got an expectation of when I push this button. This happens.” Can't test AI in those ways. Some things you can, but there's a lot of things we can’t, so it's going to be coming up with new ways to test it, new ways to utilize it, obviously, thinking about the security and the privacy and those things are going to be paramount for us in this industry and clinical trials, but there's real opportunity here. Again, although the recent regulations don't suddenly open flood gates and don't give us a step by step on how to use it, they also don't say we shouldn't use it, and they also don't prohibit it, and I think it really is waiting for that person to take that step forwards and say, “No, this could make a difference,” and go for it.

Videos

In this video interview, Derek Ansel, vice president, therapeutic strategy lead, rare disease, Worldwide Clinical Trials, discusses how artificial intelligence can aid in bringing therapies to rare disease patients faster. 

Leading the Technology and Innovation Front Through Rare Disease Research

, Derek Ansel, vice president, therapeutic strategy lead, rare disease, Worldwide Clinical Trials, discussed the unique challenges in rare disease R&D, including competing priorities among researchers, difficulty in diagnosing and treating patients, and defining endpoints due to varying symptoms. Ansel also touched on the potential of technology and artificial intelligence to address some of these challenges and improve rare disease research.

ACT: Looking forward, what advancements or changes would you like to see in the rare disease space to make research more efficient and effective?

I think fixing all of the items that I've already mentioned would be of utmost importance. Again, increasing greater global collaboration across the various stakeholders, I think, will always be critical. Rare is challenging. Rare is difficult because of, in essence, what a rare disease is, but I think if we can lay the framework around that global collaboration and working together really helps the next disease community pick up where other leaders have laid the ground there. I'm really encouraged by the use of technology and of AI (artificial intelligence), I'm very much of a skeptic when it comes to that type of technology for various reasons, if you’re interested, happy to discuss more, but I think for a community largely that's been built by technology in its very essence, parents and caregivers have created drugs because of the challenges we see in rare disease, communities have been born because of the internet and the ability to connect directly with other patients as a necessity, because those communities otherwise wouldn't exist. Rare disease, I feel, really leads the technology and innovation front in rare disease research, so I'm really excited to see how they are going to be using AI, whether that's helping diagnose patients faster by looking at pictures of children with dysmorphic features to help get a diagnosis earlier in life, or by using AI, there are various companies out there that are linking the various approved drugs out there. Could they be used to support and treat and increase quality of life in patients who otherwise wouldn't have access to those drugs, and that's really being leveraged by AI, instead of having humans mine through those various datasets themselves in a very manual way, can leverage those technologies to connect to researchers and connect patients to drugs faster.

In this video interview, Derek Ansel, vice president, therapeutic strategy lead, rare disease, Worldwide Clinical Trials, talks collaboration and maximizing resources among the various stakeholders in rare disease research.

The Role of Collaboration in Advancing Rare Disease Research

ACT: Is there anything you would recommend to help advance rare disease research from a policy or strategic perspective?

Another great question. I think it's really about collaboration. Now more than ever, I think the rare disease community and its various stakeholders need to really join forces to support and maximize really every resource available to the community. As I mentioned earlier, if you think about finances, there’s only so much money to go around. For example, if rare disease advocacy organizations could join arms, link together, and share additional funding to support research initiatives or research projects or clinical trials or registries that overlap with other groups might warrant financial benefit to those groups. I think coming together also supports conversations with regulatory authorities. By having a unified front, by working together, there's greater strength in greater numbers, and I think that's particularly true for regulatory authorities. From data sharing infrastructures, I think it's important that even our biotech partners and pharmaceutical companies, who spend a lot of money, time and energy developing programs in are disease, have some sort of exit strategy, whether, if the drug is not as safe and efficacious as we thought it was, using that data and other venues, are there patient advocacy organizations who would ingest that data and can use it to support subsequent clinical trials, so that another biotech company who's looking to research in the same disease doesn't have to start from square one at the very beginning. I think the lens of true collaboration, at least in industry, could really benefit all communities, I think, for drug development, at least for the time being.

In this video interview, Thierry Escudier, portfolio lead, Pistoia Alliance, highlights the need for maintaining data privacy in a highly regulated clinical research industry.

Maintaining Regulatory Adherence with Clinical Technology

, Theirry Escudier, Portfolio Lead, Pistoia Alliance, discussed the alliance’s newest initiative to assess the carbon footprint of clinical trials. The initiative aims to answer whether decentralized clinical trials positively or negatively impact carbon footprints. Escudier also discussed how clinical research stakeholders are slowly beginning to adopt more sustainability practices, focusing on reducing waste and costs.

ACT: What would you say to stakeholders who are still unsure of implementing more clinical technology?

I think it's very much beyond, I would say, the technology itself because, as we can see we are able to speak to each other. I think we have, maybe the Atlantic Ocean between the two of us, and we are still able to interact as we were in the same room, so the technology is there. It's more the question of data privacy in particular, I would say, for patients in using those technologies, we have to ensure that the data privacy is respected. In particular, that for the pharmaceutical sponsor, they shouldn't know anything about the patient identity that should be disclosed and things like this, so there is some of the regulatory, ethics, also difference between, I would say, all countries. Even I would say we have the global ICH E6 that is defining the way clinical studies should be run, but each country may have this their own ethics, let's say mindset, as well as data privacy and therefore, of course, there is sometimes also that fear from the pharmaceutical sponsor side is that, if I'm doing something brand new, is it going to be accepted by the regulatory when I will submit my dossier? That's why, very often the technology is very much, I would say, on line, with the technology of daily life, like even you can look to your bank account, I would say, using the web and so on, and you can interact with friends, but then, as you know, clinical trials are so regulated for the sake of protecting the patient and also the quality of data, that sometimes pharma, I think, is a little bit anxious to move towards those technologies until there is maybe some new guidance. As everyone had to deal with the pandemic crisis, with COVID, of course, there was an increasing speed of trying to adopt some technology, because no one was able to travel anymore, even the patients, they were, I would say, less accepting to travel to the to the sites. The monitors were not able to go anymore to the to the sites to review the source documents, so there was some attempt to try to look to the source documents, but depending on the countries, it might be very difficult. Now that we are back in what I would call a normal situation, after, indeed the very high increase of using technology during the pandemic, there was almost not a shift, but okay, let's come back to what we know, what we master, and let's see how the regulatory will help us, so that's why some of the recent guidance, the one from the EMA and the FDA regarding decentralized components is helping very much the pharmaceutical industry, as well as the CRO service provider and the tech provider to be a little bit more comfortable in the way they can think on implementing those new technologies, but yes, clinical research is a specific word, with a lot of concern for protecting patient data and making sure that you have no deviation, that you have a harmony between standardization across all sides, and of course, depending on the country, it might be very different.

In this video interview, Rachael Higgins, chief commercial officer, PicnicHealth, discusses industry’s next steps in being able to maximize the benefits of virtual models.

Executing with Clinical Technology

, Rachael Higgins, chief commercial officer, PicnicHealth, discussed the company’s recently released 

2025 Annual State of Observational Research Report

. She highlighted some of the key findings including the persistence of more traditional research methods as well as how patient retention and incomplete data are creating challenges for the industry.

ACT: Looking forward, what do you think are the next steps for industry in closing the gap between the need for technology innovation and actually being able to execute with it?

I think it all is going to come down to education and getting our customers comfortable with how new models actually work. It is embracing new technologies that are out there, perhaps it's doing a pilot or doing a softer launch of it, but that way to be able to see that. Number one, it's really not that scary. Number one, it's going to solve your data depth issue, it's going to solve your patient retention issue, it's going to solve your budget issues, just giving it a test trial, if you will. I think that that really is the only way that we're going to be able to do it. When I looked at the results of this report, I saw that 61% of them are already using some type of virtual model in AI, and to me, that's a huge opportunity, because, there's 40 something percent of them that haven't, but I think it's because they truly don't understand what they are, but being open to that, really understanding that there isn't just one way to do things, there are so many other ways to do that, to do what it is to get to your final outcome. Then also, I think, working with regulators, so that regulators become more and more comfortable with the ability to leverage AI and some of the other technologies to get to those endpoints as quickly as possible, to again bring medicines to patients, which is what we're looking to do.

In this video interview, Rachael Higgins, chief commercial officer, PicnicHealth, highlights how addressing these challenges can aid industry in moving more towards the future.

Addressing Data and Patient Retention Challenges

ACT: How can industry address some of the challenges holding it back from moving towards the future such as incomplete data and patient retention?

I think for companies, the life science partners that we do have, I think they truly need to not only say that they're open to things like virtual trials, but actually to be able to embrace them and actually take that leap to be able to do more of that, because that really is the only way to truly do things like getting access to the universal patient record by having patients consent directly, allowing you to get access to their medical record. It is a way that's no burden, or less burden to the patient, and then also give something back to the patient. Things like allowing patients to get access to their medical records and to be able to share their medical records, to be involved in the research, things like that are really going to certainly help them be able to scale up in the ways that they need to be able to do. When you think about all of our customers who are out there and the challenges that they have, I think they're all solvable. I think it's just getting them comfortable in the fact that the old way doesn't have to be the only way, right? There are certainly many ways to skin a cat, and I think these new ways certainly have proven to be effective. I think, again, it's just taking that leap. And then certainly, when you think about where the future is actually going to, the R&D costs are rising tremendously year over year. You see it in the news constantly with our life science partners having to make very significant cuts with not only their staff, thinking about their pipeline, what it is they're going to do, so they have to do more with less, and again, leveraging some of these types of models, virtual opportunities, leveraging direct connection to the patients, leveraging AI, that's going to be able to address their problems, which is going to allow them to scale in the future, which is really what everybody wants, which, again, at the end of the day, is going to be able to bring medicines to patients, which is really what we all want.

In this video interview, Rachael Higgins, chief commercial officer, PicnicHealth, discusses findings from the report and the identification of current challenges being faced by the industry.

Implementing Virtual Models in Clinical Trials: Results from PicnicHealth’s Annual State of Observational Research Report

ACT: Could you tell our audience a bit about the 

 and highlight some of its most noteworthy findings?

The research report that PicnicHealth ran basically shows that the industry is definitely at a turning point. The stats show that 80% of the leaders that we interviewed are either using or they're open to virtual models. I think the exact number was 78% and I think this is happening because they are number one, getting pressures from the regulators, payers, and patients for number one, real-world data. They also are leveraging AI and tech-enabled solutions to be able to deliver this in a more systematic, efficient way. If I look at all the stats that were out there, I think one of the major stats that came out is data completeness, and this is the industry's biggest hurdle, 64% of them said that incomplete data is their top challenge and if you think about traditional methods, like a site-based study or a traditional chart review, it just isn't able to deliver that data completeness or capture the entire patient record, if you will, which is why they're actually looking for other options for that. Another area, which again, is no surprise, is patient retention, the loss to follow up with any trial, is one of the biggest issues for life science partners, and they are looking for ways to actually solve that, so having a direct connection to the patient is one of the ways that can increase keeping patients engaged, which then, in turn, would, obviously, enhance the retention. Another thing that we highlighted was 60% of leaders actually flagged that long-term follow up studies was a top priority for 2025 and we know that there are so many pressures and demands on life sciences with all of the cost-cutting measures that are happening, so they're looking for innovative ways to do that. Then last, but certainly not least is AI. If you think about some of the releases from JP Morgan, if you think about some of the questions that folks had answers with our survey, they are having a lot of pressure to leverage AI, not because it's the fun thing to do, but really because it is an innovative, efficient way to really be able to hone in on what it is that you're looking for and then get those answers really quickly. I think 61% of them are either using it or open to actually being able to do that.

In this video interview, Rachael Higgins, chief commercial officer, PicnicHealth, discusses findings from the report and the industry's willingness to adopt technology.

In this video interview, Cameron Breze, product manager, Inovalon, discusses the need for accurate information in creating useful clinical data.

The Role of Quality Data in Utilizing Machine Learning Models

, Cameron Breze, product manager, Inovalon, discussed challenges in patient recruitment for clinical trials. Traditional recruitment methods, such as manual outreach, are now less efficient due to accessibility issues. Breze highlighted technology as a way to address recruitment challenges, which can be leveraged to automate and streamline processes, reducing labor intensity and errors.

ACT: With so many data sources now in the mix in clinical research, how important is the role of quality data in identifying patient populations?

Quality data is something that is top of mind for everyone in the in the data technology space. Many people like to use the age-old adage of garbage in garbage out when it comes to machine learning models and a lot of the data modeling that's available where if you're feeding it with inaccurate information, you're going to get a result that really can't be transposed and moved and translated into anything that's really applicable and that ends up being a waste of institutional resources and not the best way to serve patients. I can share a little anecdote as well on that. I published a research paper with the MIMIC for critical care database, where we looked at resuscitation, so fluid and IV administration in the hospital with organ failure, and found out through lots of computational modeling that essentially, we had a great record of the fluids that were put in, but not a great record of the fluids put out, so the clinical staff were not recording that, not because they were missing it or they were making some error, but in the scheme of making that particular research impactful, if you don't have that information, you can't make designations on that person's fluid status, and seeing that in aggregate across hundreds of thousands of patients, you get to see what a potential impact that could have had, had that policy or procedure been updated or changed, so I think a lot of those decisions help inform the way that site staff are trained and data is collected over time. Beyond the basics of setting up a system that is going to collect quality data and allow the researchers to interact with it, there's a lot of data vetting that has to go through, so relying on subject matter experts and folks with experience in those particular areas, it's an interesting intersection, where sometimes the person who may be best able to speak to the quality of the data might come from a tech background or a healthcare background where you wouldn't expect that to be the case, typically, so combining interests and having that overlap and Venn diagram and cross pollination of ideas is something that inspires me to work every day and have that overlap and really collaborative effort with a lot of people that I work with and a lot of my other peers in the industry.

In this video interview, Yael Elish, founder, CEO, StuffThatWorks; and Julie Ross, president, CEO, Advanced Clinical, discuss how newly emerging data from patients can help the industry improve its recruitment strategies. 

Leading with the Patient Voice

, Yael Elish, founder, CEO, StuffThatWorks; and Julie Ross, president, CEO, Advanced Clinical, discussed results from the Barriers in Clinical Trials survey conducted by StuffThatWorks. Key challenges identified by the survey include lack of physician communication, patient education, and the complex recruitment process. Elish and Ross highlighted how the findings can be used to streamline processes and leverage patient data to improve clinical trial recruitment.

ACT: What implications will the Barriers in Clinical Trials 

 results have on the clinical research industry?

We've known for decades that patient recruitment is the number one challenge with clinical trials. We talk about the data, we see the data. We now have data directly from patients through the StuffThatWorks platform where you can engage with patients directly that are saying, “I, no matter where I live, want to participate in clinical trials. I have a need. I've exhausted other avenues. My physicians are not bringing these opportunities to me. When I raise it, physicians will impact the way that I view the clinical trial.” We have enough data now and enough direct patient insights that it is time for us to change how we're running clinical trials. It is time for us to lead with the patient's voice and restructure the way we conduct clinical trials. Looking at it through the lens of the patient, I want, and I believe and trust in my primary I want opportunities, no matter where I live, to clinical participate in clinical trials. It's upon the industry to solve for that, and the time is now.

: I second that very much, and I think that one of the biggest problems is the disconnect between access to patients and access to patient data. There is a total lack of access to patients. Every time you have to recruit patients from scratch, there's no way to really identify or advertise to patients online, because Google, Facebook, they self-regulate, and then you have to screen patients all over again because you don't really have their data. They're coming without data, so they have to be screened, and then their medical records have to be downloaded, and the information and screeners and the whole process. If you have access to patients at scale, and together with their data, this is a kind of game over, because you now know already who is eligible, you know where, and you just need to reach out to them and as we said before, I think, be in a respectful, ongoing conversation with patients and be involved. Have the primary care physician, the doctor, the immediate doctor be on board with the process, because that's probably the key issue here.

In this video interview, Dipanwita Das, CEO & co-founder, Sorcero; Sujay Jadhav, CEO, Verana Health; and Kimberly Tableman, founder & CEO, ESPERO, highlight areas around clinical data that could have a large impact on the industry in 2025.  

Industry Leaders Share Top Trends in Clinical Research for 2025

 Effective and well-designed clinical trials is essential for the commercialization stage, and particularly when it comes to making sure that the drugs and therapies, as designed and commercialized, are getting to the right patients, which means the links between the clinical stage and the commercial stage, that's becoming stronger, and the gap between those two things are becoming much less. The first area where we are going to see a lot of investment rightly, is in data strategy and in integration of data itself. First and foremost, the industry has to be prepared for increasing volumes of real-world data and real-world evidence getting integrated in a real-time process into measuring the effectiveness of trials, as well as adapting or modifying them. We're definitely going to see a lot more on data standardization. That's actually a problem. Before you can do AI (artificial intelligence), you need to get your data into order, check the quality of it, the governance of it, the updating of it, and so forth, so making sure that that organizations have enough data infrastructure to handle that sheer diversity of data sources, which, by the way, we see every day, the diversity of data sources, the unstructured nature of it, the non-standardization of it, can be a really, really huge problem. Then in 2025 I predict we're going to see a lot of investment, of course, also in patient diversity and inclusion, where you're strengthening strategies for diverse patient recruitment, looking at more decentralization so you can reach people where they're at, you want to reduce the barriers that a typical patient will face in getting to a trial and adhering to that regimen through the length of the trial. You're also going to see a lot on building relationships with underserved communities, and I think this is important in working with local health systems, with nonprofits to access the communities which might have a disease prevalence that isn't reflected. Last, but certainly not the least is regulatory preparedness. Regulations are getting more complex and more prescriptive and more demanding, but monitoring FDA guidance on novel trial designs and DCT approaches, staying current with international regulations, so that you have a very smooth commercialization process, as well as data privacy. As we use more AI to analyze data that has PHI and does not, making sure that you're able to actually align with those data privacy requirements, which is essential for trust is going to be very important. I could probably go on, but these are some of the key items that I expect to see a lot of progress being made on.

 I would say, as we move into 2025 there's two generic themes that I see in terms of how real-world evidence and real-world data is going to be utilized in clinical research. I think one is around the data and expanding the type of data that we're using for clinical research. The second one is what I like to bucket it under, helping productionize, using external/synthetic control arms in the overall process. When you're looking at the data side of the house, historically, a lot of the data that has been used for clinical research, a little bit more mainstream, has been more structured data, seeing a good movement into leveraging unstructured data. There's different types of unstructured data, things such as physician notes, as an example, is an unstructured data source that we are leveraging in terms of helping facilitate improved clinical research. There's also other unstructured data sources, different data sources like imaging data as well, and we're starting to use, and see usage of imaging data quite a bit in terms of helping identify patients with certain diseases, understanding their disease progression as well, and then rolling that into clinical research. The good thing about imaging data is it's like an unbiased data source. From that perspective, the credibility, the quality of including that is very, very good.

The second bucket is around external control arms, synthetic control arms. It's been around for quite a while, overarching use of this for the particular components of the clinical trial where they're actually not getting the treatment, so the non-treatment arms are leveraging real-world data to understand and compare and contrast to the treatment arms that you put forward there. Historically, recruiting patients where you're not providing them the treatment, as you can imagine, is very, very expensive and very time consuming overall. I think that's the area where you look at synthetic control arms, and leveraging real-world data can help be a good proxy, or can help augment and allow you to compare and contrast to the treatment arm. The good thing with real-world data and leveraging it for these type of control arms is that the volume is very, very large, and the quality and the longitudinality of the data is pretty good as well. If anything, the compare and contrast, the treatment arm versus a non-treatment arm can actually be a high-quality fashion as well. These are some of the key trends that we're seeing as we move into 2025 in terms of helping improve clinical research by leveraging real-world data.

 I think one of the things we're really seeing right now is related to data interoperability, so we have historically had a set of data standards, particularly getting data submission ready from a CDISC perspective, but we have also now seen with the FDA putting forth the guidance on the ICH M11 standardization around the data for protocol collection as well, so that data, and then there's an opportunity, really, from an interoperability perspective, to map all the way through, so you have the protocol now in the ICH M11 format, you can map that to CDISC. We've done that as well as USDM, and we have for the schedule of activities, mapped all the way through to this, to the actual SOA itself, so the ability to then, from a FHIR perspective, be able to share that data, which this has all been things that the industry has been talking about for a number of years, and it is now finally coming to fruition. The benefit of that data interoperability is supporting generative AI. It's really allowing you to use data in downstream systems to really automate that digital data flow. Again, stuff that we've been talking about but haven't really been able to fully recognize, I think we're going to see that happen in 2025.

Data Management

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