Can We Predict Trial Success? From ‘Feasibility’ to Predictive ‘Readiness’

Brian S. McGowan, PhD, FACEHP, Chief Learning Officer and Co-Founder, ArcheMedX, Inc.

So much has been written about site feasibility over the past decade—even a cursory review of Applied Clinical Trials magazine, for instance, will identify ~20 articles, press releases, and interviews describing site feasibility services, solutions, toolkits, and best practices. And this is just a small snapshot of the “research” and promotion of site feasibility that overwhelms our community. With all that has been written and presented, it seems logical to ask if these site feasibility efforts have provided meaningful benefits.

Perhaps not surprisingly, current site and trial performance data provide a striking answer:

• 70% of trials experience start-up delays
• 80% of trials fail to meet on-time enrollment
• 45% of trials miss original projected timelines

If the goal of site feasibility is to “predict” if a site will be successful in conducting a study and the performance data suggests that sites continue to struggle, then maybe it’s time to rethink our principle approach to predicting performance. To be clear, we need to continue to refine and enhance the predictive validity of site feasibility, but there are other evidence-based predictive measures of change that should be immediately used by clinical research professionals to minimize start-up delays, accelerate enrollment, and optimize trial performance.

In each of my prior columns, I’ve drawn lessons directly from cognitive science or behavior science to suggest new ways of approaching clinical trial planning and execution. For this column, I summarize what learning science has taught us about drivers, or predictors, of change—and how we get from learning to doing.

Credit:Co-created with ChatGPT

From learning to doing: Evidence-based predictors of performance

To summarize merely 50 years of evidence: learning science has demonstrated six characteristics of a learner in a training experience that are highly predictive of application of learning (i.e., behavior change). The more these characteristics are surfaced during a training experience, the more likely performance will improve. In other words, we know definitively that how and what a learner thinks while learning is actually our most accurate predictor of change. So what are these predictive characteristics?

1. Confidence (Self-efficacy)

Learner confidence, or self-efficacy, reflects the belief in one’s ability to execute specific tasks or behaviors. Bandura’s social cognitive theory emphasizes self-efficacy as a central predictor of behavior change, as individuals are more likely to implement new practices when they believe they can succeed. Clinical trial professionals with accurately placed confidence tend to be more proactive and persistent in applying their skills, which leads to sustained improvements in trial execution.

2. Reflection

Reflection involves the process of evaluating experiences and recognizing areas for improvement. Schön’s work on reflective practice underscores that reflective learners tend to bridge the gap between knowledge acquisition and practical application, as they continually integrate new insights into their professional identity. Reflection within training strengthens a clinician’s ability to adapt and apply new practices effectively.

3. Curiosity

Curiosity drives individuals to explore, seek out new information, and remain engaged. Curiosity has been linked to greater persistence in learning and problem-solving. In training, curiosity encourages clinicians to go beyond basic knowledge acquisition, leading to deeper assimilation and broader application of new skills.

4. Grit (resilience)

Duckworth’s research on grit—defined as perseverance and passion for long-term goals—demonstrates its role in achieving sustained behavior change, even under challenging conditions. Clinical research professionals with high levels of grit are better equipped to navigate difficulties and persist in adopting new behaviors. Demonstrating grit within trial training can, therefore, help professionals remain committed to change despite obstacles.

5. Intention to change (commitment to change)

Ajzen demonstrated that an individual’s intention to change is one of the strongest predictors of actual behavior change. Site training programs that encourage participants to set specific goals or commitments can foster stronger intentions to implement what they have learned. This behavioral intention often translates into meaningful change when clinicians return to practice.

6. Self-regulation

Self-regulation, the capacity to monitor and manage one’s learning process, plays a critical role in behavior change. Zimmerman showed that self-monitoring, self awareness, and strategic adjustments enable learners to incorporate new knowledge effectively. Site staff who are skilled in self-regulation are better able to apply new techniques consistently and refine their skills over time.

Importantly, these are characteristics of how and what a person thinks as they learn. In prior columns we highlighted the differences in “I-Frame” (the individual) vs “S-Frame” (the system) approaches to change management. Here is another example of just why the “I-Frame” is so critical—execution of a trial protocol ultimately comes down to an individual screening a patient, an individual providing care, and an individual deciding to diligently follow the varied and complex steps in a modern clinical protocol. Therefore, it is the individual or team’s readiness to perform that predicts trial success. And confidence, reflection, curiosity, grit, intention, and self-regulation are the well-established predictive drivers of readiness to perform.

Moving to feasibility plus predictive readiness

Site feasibility, as an “S-Frame” intervention, has a critical place in planning and conducting clinical trials, but it is simply not a strong predictor of trial success. The success of a study is more than having adequate logistics, resources, or experience—that’s not how performance works. To maximize our ability to predict trial success, we must consider the actual predictive drivers of behavior change. By focusing on these six training-based predictors, we can design training programs that not only convey knowledge but also foster lasting change. Ultimately, the purpose of trial start-up and site training is to empower professionals to act, transforming insights into better trial enrollment and execution, and accelerating advancements in patient care.

Brian S. McGowan, PhD, FACEHP, is Chief Learning Officer and Co-Founder, ArcheMedX, Inc.