Clinical Researcher—June 2025 (Volume 39, Issue 3)
VOICES FROM THE FIELD
Edited by Gary W. Cramer, Managing Editor for ACRP
As a follow-up to Clinical Trials Day 2025, held on May 20 with the theme of being “Powered By Purpose,” a collection of industry thought leaders shared their perspectives with ACRP on their clinical research powers, and on what they see as being the greatest current challenges and opportunities for the enterprise.
My clinical research power is…
Bernard Vrijens, CEO and Scientific Lead at AARDEX
…the ability to transform medication adherence from a hidden risk into a measurable, manageable asset. Advancements in industry technology, such as digital adherence monitoring systems and data analytics, empower sponsors and contract research organizations (CROs) to reduce variability in drug exposure. This allows for more accurate information and smarter and more efficient decision making by teams as they strive to achieve the best possible outcomes for clinical trial participants.
Jo Marshall, EVP Data Services and Advanced Analytics at Coronado Research
…clinical data strategy and insights—the ability to assess and guide pharmaceutical and biotechnology companies on how to collect and manage the data generated throughout their development programs. This ensures the data are working in the best way to achieve their business goals—whether through using the right technology, maximizing process efficiency to ensure data are available when needed, or helping others understand the value in the data and reduce the risks in collecting them.
Sam Whitaker, Co-Founder and CEO of Mural Health
…designing technology that makes the participant experience radically easier—and doing it at scale. The goal is to remove friction from the trial experience, but it’s about more than ease; it’s about dignity. When you give people tools that show respect for their time and circumstances, participation goes up, and so does retention. Our power is staying focused on that human element while delivering the operational infrastructure modern trials demand.
Mathilde Tournay, Trial Design Lead at One2Treat
…bridging the gap between statistical rigor and clinical development. As a biostatistician, I bring deep expertise in data and complex statistical methods together with a solid understanding of science and medicine to ensure that clinical evidence is not just methodologically sound but also meaningful. My goal is to transform data into actionable insights that truly support better decisions for patients and clinicians alike.
Ricky Lakhani, Chief Product Officer at PHARMASEAL
…turning clinical complexity into scalable, user-friendly innovation by bridging the needs of the clinical research industry with technical strategy to deliver impactful software solutions. My work aligns cross-functional needs—scientific, operational, and business—in technology products that drive measurable improvements in trial execution. I also accelerate innovation across the clinical research ecosystem, empowering teams to move faster, smarter, and more confidently as they bring better therapies to patients, sooner.
Sarah Tucker, Chief Operating Officer at Phastar
…driving operational excellence to deliver measurable results. With 25 years of experience in pharma and CROs, I’ve developed the ability to navigate complexity, build high-performing teams, and ensure that quality, compliance, and delivery are always aligned. By focusing on both internal and external stakeholders, and consistently prioritizing customer satisfaction, we’re able to drive better outcomes. This creates a culture of collaboration that not only strengthens partnerships with clients but also fosters a high-performance environment within our teams, ensuring long-term success for all.
Patrick Hughes, Chief Commercial Officer and Co-Founder, CluePoints
…helping people to recognize the power of integrated data review to gain the best possible insights from clinical research through analytics and data interrogation. The future looks incredible because of the advanced statistics we have at our disposal and the machine learning that we are putting on top of that. For example, medical coding has historically been a resource-intensive, inefficient, manual process. With machine learning, we can achieve 99% accuracy, increasing efficiency and saving millions of dollars.
The greatest challenge I see to clinical research right now is…
Bernard Vrijens: …the gap between protocol design and patient behavior, especially around medication adherence in clinical trials. Even the best or most world-changing molecule can fail if drug intake is inconsistent or unknown. Due to the use of archaic, biased, and outdated methods for compliance monitoring—such as patient self-reports and pill counting—all too often, poor medication adherence goes undetected within clinical research. Failing to monitor medication adherence effectively undermines the efficacy and safety of new treatment developments and increases the risk of trial failure. This can lead to delayed access to treatment for the patients who need it most.
Jo Marshall: …shifting the clinical trial process to keep up with the changing drug landscape. The traditional model of running clinical trials worked for blockbuster drugs but is holding back the development of personalized medicine and treatments for rare diseases, which need a more targeted approach based on individual needs. Availability to data is still siloed, sharing data between systems and stakeholders is cumbersome, and moving data through the development process is linear and unwieldy, meaning decisions are taken slowly and costs are high. My colleagues and I are working on resolving this challenge and helping customers to optimize the clinical development process by leveraging all the data available using artificial intelligence (AI), machine learning, and leading technologies.
Sam Whitaker: …that we still treat protocol efficiency and participant experience like they’re separate goals. They’re not. When participants are left to figure out financial issues or logistical hurdles on their own, it creates a burden for them and for sites, because when participants struggle, sites can’t operate efficiently, either. Yet trials are still too often designed with a narrow focus on operational needs and regulatory compliance, and minimal focus on the participant experience. We need a mindset shift: one that prioritizes both smart technology and human kindness. Not all solutions offer both.
Mathilde Tournay: …designing trials that can manage growing complexity while staying focused on patient needs. As trials evolve to include more data, endpoints, and subgroups, it becomes harder to reflect the diverse preferences of the people they’re meant to serve. Too often, key outcomes are chosen without patient input, leading to evidence that misses the mark on real-life impact.
Ricky Lakhani: …technology adoption and data interoperability. Despite advances in digital solutions, many companies still rely on outdated systems or manual processes. Resistance to change, lack of standardized platforms, and limited interoperability between technology vendors further slow adoption. As a result, workflows continue to be disjointed, increasing the administrative burden on study teams and reducing overall efficiency. Compounding this challenge is the fragmentation of data sources. Clinical trials generate data from numerous sources. These data sources are often siloed, lacking common standards or seamless integration. Without interoperability, reconciling and analyzing data becomes time-consuming and error-prone, delaying key trial milestones. The challenges of technology adoption and fragmented data create another challenge—hindering the effective use of AI systems, which rely on large volumes of clean, structured, and interoperable data to generate accurate insights, automate processes, and support decision-making. However, when trial data are spread across disparate systems with inconsistent formats, missing fields, or poor integration, AI algorithms struggle to access the high-quality inputs they need.
Sarah Tucker: …maintaining scientific and operational rigor amid increasing pressure to accelerate. As innovation surges and patient needs grow, the demand for speed can outpace the infrastructure required to ensure quality and reliability. Without sustained investment in our people, systems, and oversight, we risk compromising the integrity of the work itself. At the same time, global political and economic uncertainty could disrupt funding, regulatory alignment, and talent mobility—factors essential to sustaining the momentum we’ve built since the pandemic. Navigating this environment requires deliberate, forward-looking leadership that safeguards both pace and precision.
Patrick Hughes: …that the industry is still held back by inefficient processes and workforce silos. These outdated ways of working are struggling more and more to cope with the increasing volumes and complexity of clinical research data. We need to push stakeholders to recognize archaic processes and identify areas where we could do things more efficiently and effectively. Integrated data review can help to overcome some of these challenges. By starting with quality and allowing everyone to work in one system, we can break down historic silos. However, we also need effective change management to improve data review, increase insights, and gain efficiencies.
The greatest opportunity I see for clinical research in the near future is…
Bernard Vrijens: …the leveraging of real-time adherence data. The increased use of this type of technology will bring personalized trial support, improved protocol fidelity, and de-risked development programs. With tools which offer real-time, user-friendly adherence dashboards, we can bring a new layer of intelligence to clinical trials. These technological developments aid in understanding medication-taking behavior and empowering patients as they go through the clinical trial process, ultimately turning uncertainty into insight.
Jo Marshall: …that technology and AI bring significant advantages to the drug development process but humans in the loop are needed to fully utilize these opportunities. Understanding what innovation will generate the best return will be key to ensuring that transformation is successful in ensuring quality and increasing efficiency. Digital innovation experts can work with you to understand your unique needs and identify where technology and AI can have the greatest impact, as well as helping with change management. Too often we see solutions being implemented without proper consideration, which results in cost increases in an arena where managing cost is essential to maximize the availability of essential treatments. Experts will also be needed to validate and quality control output from digital innovations, ensuring accuracy, reliability, and ethical compliance.
Sam Whitaker: …getting real alignment on what it means to be participant-first—and finally building around it as an industry. We have the technology to remove barriers—to make payments easy, travel manageable, and communication clear. The opportunity now is less about invention and more about intention. It’s about choosing to design trials that are respectful and easier to participate in. The future of clinical research belongs to the organizations that realize participant experience is not a soft metric, but a strategic advantage.
Mathilde Tournay: …embedding patient preferences very early into the design of trials—starting with how outcomes are selected and prioritized. Tools that enable this shift will drive more meaningful evidence generation, reduce trial inefficiencies, and accelerate the delivery of treatments that patients truly value. We must do better at integrating patient priorities from the outset and making full use of the data we collect to ensure research reflects what matters most to those living with the condition.
Ricky Lakhani: …leveraging AI to transform the way research is conducted to bring novel therapies to market faster. As the industry continues to modernize in its use of digital approaches, AI can serve as a catalyst for unifying disjointed systems and unlocking the value of complex, siloed data. By applying machine learning to harmonize disparate data sources, AI can reduce manual reconciliation efforts, enhance data quality, and accelerate insights across trial operations. With clean, structured, and interoperable data, AI has the potential to optimize site selection, predict patient enrollment, identify protocol deviations, and automate routine tasks—dramatically improving trial speed and accuracy. Natural language processing can extract key information from unstructured sources, while predictive analytics can proactively flag risks before they disrupt study timelines. This transformation depends on continued investment in interoperable technologies and a willingness to embrace AI-enabled workflows. For organizations ready to evolve, AI offers not just automation, but strategic intelligence—reshaping clinical research from reactive to proactive, and from complex to connected. The opportunity lies in using AI not as a standalone tool, but as an integrated layer that enhances every part of the clinical trial lifecycle.
Sarah Tucker: …lies in transforming clinical research into a more adaptive, data-driven, and patient-centric enterprise. Advances in digital health, decentralized models, and real-world data are enabling us to design studies that are not only faster and more cost-effective, but also more inclusive and reflective of diverse patient populations. Specialist biometrics and data science CROs operate at the intersection of science, execution, and technology—uniquely positioned to help sponsors leverage these innovations to deliver smarter, more representative trials. Our role is not just to deploy people, process, and tools, but to embed them within scalable, quality-driven operations that consistently deliver better outcomes for patients and greater value for clients. It’s about accelerating with intention—combining speed, precision, and impact.
Patrick Hughes: …involves harnessing the power of advanced statistics—which we already have—and layering on machine learning and AI to maximize clinical research insights. We need to invest in solving the problems the industry is facing. This is not talking hypothetically about the potential of AI or broad brush strokes. It is about developing real-life use cases which demonstrate how technology can do the heavy lifting to allow people to invest in critical thinking. Just as we replaced antiquated processes like 100% source data verification with risk-based quality management, we need to look at what is outdated and outmoded and identify where machine learning and AI can help us to do better.
Organized and promoted by ACRP since 2014, Clinical Trials Day (May 20) is a joyful opportunity to pause in reflection, recognition, and admiration of all that has been accomplished thanks to clinical trials and the people behind them.
