Contributed Commentary by Jenna Morris, Anju 

April 11, 2025 | Clinical trial complexity is at an all-time high, with today’s clinical trials being more complex in their design and execution, whilst also demanding more sophisticated data management, regulatory navigation, and operational coordination than trials conducted a decade ago. 

It is no surprise that the role of clinical feasibility has evolved from educated guesswork into a data-driven systematic approach. But with so many variables, including unpredictable patient engagement, global regulatory demands, site competition, political unrest and inevitable human error, to strive for perfection is unrealistic. Modern feasibility is not about perfection, but being less wrong, faster. 

Modern Feasibility: The Data Advantage 

Clinical Feasibility assesses the viability of a trial design in real-world settings. Taking the time to review the plan for a trial and asking, “How can this be done?” “Where should this be done?” and “How can we do it faster/better?” Historically, feasibility teams relied on intuition, experience, and anecdotal evidence, but in the last 10-20 years, the integration of data-driven insights has revolutionized the feasibility landscape. 

Healthcare data is noted to be the fastest growing source of data worldwide, with the compound annual growth rate of healthcare data forecasted to reach 36% by 2025. Indeed, the last 10 years has seen the incorporation of claims data, electronic health records, diversity data, socioeconomic data and others, and in coming years we will inevitably see further expansion as clinical roles become increasingly data-driven. 

Feasibility today is a sophisticated process, where teams apply operational expertise and experience with the ever-growing broad range of internal and external data sources. The overall goal being to save time, resources, and ultimately, lives. In order to be “less wrong, faster” feasibility teams need to consider a number of key elements: 

• Patient Recruitment: It is critical to understand your target patient profile. What is the global incidence of the patient subpopulation, treatment pathways, standard of care and other potential barriers to recruitment. What enrolment benchmarks have we seen in similar trials, and does this reflect in our expectations? 

• Site and Investigator Selection: Which sites and investigators have experience and capacity to recruit? What is the demand for resource at those sites currently? Do we have historical performance data, or access to EHR or diversity data for these sites? 

• Regulatory Considerations: What regulatory approvals are needed and what are the start-up timelines? Are there and ethics or IRB considerations that might slow down the study?  

• Timeline Assessment: Are the projected timelines realistic? Have we considered the current and future competitive landscape? Do we have contingency plans in place? 

Clinical trials are notoriously expensive and time-consuming, with even small mistakes leading to delays, increased costs and failed studies. With only 10-12% of drugs entering clinical trials making it to market, effective, data-driven clinical trial planning and execution is fundamental to success, identifying challenges and allowing clinical teams to course correct. 

More Data, More Problems? 

Access to vast amounts of data presents both opportunities and challenges. On one hand, access to more data allows for deeper insights and more precise decision-making, but the sheer amount of data can be overwhelming, leading to analysis paralysis and the inability to extract meaningful insights. Not all data is captured equally, nor is it always fit for purpose, and sometimes the “noise” drowns out the valuable insights. 

Then there’s the issue of data quality. Having all available data is promising, but data can be inaccurate, outdated and/or incomplete, and in combining datasets, we see issues with reconciliation, duplication and naming conventions to name a few. You might have enrolment data from a previous study, but it isn’t reflective of the current landscape due to new clinical trials starting, changes in treatment protocols, socioeconomic challenges, and many other factors.    

Last but certainly not least, patient engagement. Even armed with the arsenal of data we have today, patient recruitment remains a complex and unpredictable process, owing to both the complex and unpredictable nature of human behaviour and the inherent difficulties of the patient journey, from symptom onset, diagnosis and effective treatment. Data can tell us where the patients might be, but it can’t make them walk into a clinic. Patient motivation, awareness and willingness to participate remain ongoing industry challenges, despite the enormity of patient data now available. 

The Art of Being Less Wrong 

Being “less wrong” isn’t a natural way to think for a scientist, but in our highly complex clinical environment filled with endless variables, many of which are out of our control, it’s an inevitability we have to accept. Clinical trial feasibility doesn’t demand perfection but is about reducing uncertainties and improving trial designs based on relevant data and real-world experience.