Is Everyone Just Guessing on Their Feasibility Analysis?

In a world being increasingly driven by data, it is surprising to hear in casual conversations with other research professionals that most enrollment feasibility analyses are overly optimistic “best guesses”, relying heavily on busy coordinators’ and principal investigators’ knowledge of their patient population.

Not to discredit any investigator or staff member, but getting a gut-feel answer that is an accurate enrollment estimate seems difficult, to say the least. Additionally, some sponsors and CROs often give as little as 24 to 48 hours for sites to respond to quite detailed feasibility questionnaires, adding more pressure to respond quickly without critically evaluating the protocol and accessible patient population.

While patient recruitment technologies are gaining popularity in assisting sites with finding research candidates, there remains a lack of data-driven methodologies used by research sites to accurately estimate enrollment counts prior to ever taking on a study. For healthy population studies, there is less risk of underperforming, as I/E criteria are typically very basic for these studies.

However, most trial protocols consist of extremely complex I/E criteria, often including time-based requirements, surgically confirmed conditions, and other rigorous parameters. We might as well throw a dart at a board of numbers and report it as fact, as one site employee stated in conversation when describing the act of predicting enrollment during feasibility analysis.

A case study published in last year’s SCRS July Newsletter¹ illustrated the ability of an innovative technology solution to reduce a simple patient search yielding 45,000 potential study candidates down to 1,874 high-quality, prequalified candidates after considering complex protocol I/E criteria limitations. What if this site’s feasibility submission had been based on the initial search criteria, yielding 45,000 potential candidates only to come up with 1,874 viable candidates once enrollment initiated? Not only would the site have spent countless hours filtering through patients to find ones that actually met the detailed I/E criteria of the study, but the site could also have:

• failed to reach their enrollment targets,
• incurred a large financial burden by accepting a study they could not properly enroll, and
• damaged their relationship with the study sponsor, impacting future business opportunities. 

Not only is protocol I/E criteria becoming more difficult for potential candidates to meet, but new laws and regulations² also continue to be released from Congress and the FDA requiring enhanced diversity and inclusion in trials³.  Without data-driven feasibility technologies being used to set research sites up for success, these commonly encountered obstacles will only be magnified as research complexity increases.

This same congressional release from December 23rd, 2022 discusses “using digital health technologies to help improve recruitment and participation in clinical trials”; although, it fails to mention the need for data-driven methods to assist with answering feasibility questionnaires.²

The issue of poor enrollment performance is not a one-sided problem. Finding qualified candidates more efficiently is indeed a large part of the solution, but we also need to approach the problem by looking upstream. Research sites must be able to set themselves up for successful trial execution by accurately estimating their enrollment potential and no longer using best guesses to complete feasibility questionnaires.

It is also worth noting that some sites already using innovative technologies for enrollment predictions have admitted to being penalized by sponsors for providing lower counts, despite them being more accurate than their peers’ estimates. Therefore, it is imperative that sponsors start working with sites using these technologies to set realistic enrollment targets that can actually be met. 

When all parties involved have the same goal of enrolling patients to accelerate medical innovation, it is important that they all have the same approach to solving the problem. The use of technology solutions leveraging patient medical history to perform feasibility analyses will allow:

• Sites to have confidence in their ability to succeed in the studies they choose to pursue.
• Sponsors to get an early indication of their need to pursue a protocol amendment, re-calculate the number of sites needed to hit their enrollment targets, and reassess their enrollment targets altogether.
• Studies to be executed closer to the sponsors’ planned costs and timeline.

Shifting the way an entire industry thinks about how feasibility should be performed will take some time, but the early adopters of this new data-driven feasibility standard will undoubtedly experience improved study performance.

By Aspen Insights

1. Aspen Insights. Society for Clinical Research Sites. July 12, 2022. Accessed May 26, 2023. https://myscrs.wpenginepowered.com/resources/supercharging-the-chart-review-process-with-ai-technology/.
2. H.R.2617 – Consolidated Appropriations Act, 2023. Congress.gov. December 22, 2022. Accessed May 26, 2023. https://www.congress.gov/bill/117th-congress/house-bill/2617.
3. NIH-wide strategic plan for diversity, equity, inclusion, and accessibility (deia). National Institutes of Health. March 20, 2023. Accessed May 26, 2023. https://www.nih.gov/about-nih/nih-wide-strategic-plan-diversity-equity-inclusion-accessibility-deia.