uch has been studied and written about the recruitment funnel in clinical trials—from patient identification to enrollment through completion—and stages where high percentages of patients fall out or discontinue in the process. A recent pilot study helps explain why a very high percentage of identified and interested patients drop out of the funnel early on, prior to completing pre-screening. This article summarizes the results of the pilot study, examines the implications of the “last mile” leak, and highlights the urgent need for further research quantifying the magnitude of the problem and identifying opportunities for new solutions.

The “Leaky Pipe”

The “leaky pipe” framework has been utilized extensively to capture inefficiencies in the patient recruitment and retention process. The framework assists clinical trial sponsors in identifying challenges and opportunities to increase incoming prospects and mitigate losses in a manner that is both cost-effective and ethical. Prior research has identified numerous leaks in the pipe which impede patient recruitment, including patients lost between pre-screening and informed consent and between randomization and trial completion. The “leaky pipe” framework also assists clinical teams in anticipating factors influencing participation such as inconvenience, financial barriers, and ineffective communication.

The “last mile” in patient recruitment refers to the step in the process when an interested and inquiring patient is referred to a clinical trial site for final pre-screening prior to consent. Although a very high proportion of prospective and interested patients is lost between identification and pre-screening, the factors contributing to this “last mile” are poorly understood (Harper, 2020). The major challenges patients face when attempting to be referred to a clinical trial include never receiving responses from the trial site, not having a next step, and not having the right trial information. While initiatives have been launched to address the “last mile” of recruitment for patients by expanding access to testing, they largely fail to tackle the crucial issue of poor communication and follow-up with patients eager to participate in clinical trials.

More effective communication and streamlined referral processes are urgently needed to ensure that both current and future patients have access to these vital treatment options. Without these improvements, we can neither optimize clinical trial timelines and budgets nor accelerate time to market for new therapies, which are critical for addressing unmet medical needs.

The Unexplained Gap

Today, patient recruitment is one of the biggest challenges that clinical research professionals encounter in any clinical trial. According to research conducted by the Tufts Center for the Study of Drug Development (Tufts CSDD), only 60% of activated investigative sites in any trial enroll a patient, and nearly 30% of randomized patients drop out prior to study completion. As a result, phase 2 and 3 clinical trials today take on average 36% more time to complete than those conducted 10 years ago. Recruitment challenges are associated with patient burden, fear of receiving a placebo, stringent eligibility criteria, and high competition for patients between clinical trials.

Patients are increasingly becoming aware of clinical trials and are proactively seeking trial options. According to a 2023 Perceptions & Insights Study by the Center for Information and Study on Clinical Research Participation (CISCRP), 91% of people who had never participated in a clinical trial expressed interest in doing so. The same study found that 36% of respondents wanted to learn about clinical trials online (CISCRP, 2023). Further research has shown that when patients are offered the opportunity to participate in a trial, 55% agree to enroll.

Although patients are interested and motivated to join clinical trials, 69% of patients identified and interested in participating are never pre-screened. There is little to no data helping to explain this phenomenon. For this reason, Ancora.ai and Tufts CSDD collaborated on a pilot study focusing specifically on cancer patients.

Methods

Ancora.ai is a digital, patient-facing clinical trial finder tool that enables cancer patients, caregivers, and healthcare professionals to easily find relevant trials. Ancora.ai also assists patients in being referred to those trials. From September 2021 to June 2024, Ancora.ai gathered data from their trial referral activities across 17 countries. These patient referrals included patients at different stages of various types of cancer and involved trials across industry and academic sponsors. The data included details such as the sponsor, clinical trial site, principal investigator contact, and timeline from the initial patient inquiry about a trial to the resolution of the referral request. Additionally, the patients’ experiences were documented.

Pilot Study Results

Out of the 133 cancer patients that Ancora.ai supported, only 46 received a response when using contact information provided on ClinicalTrials.gov, yielding a 35% response rate. When Ancora.ai reached out to clinical trial personnel via LinkedIn and other channels in addition to ClinicalTrials.gov, the response rate improved to 47%, with 62 patients receiving an initial response to their referral inquiry.

Of the 62 patients who received an initial response, 53 received “actionable” information about the clinical trial and next steps to take in the referral process. Only 26 of these 53 patients, or 49%, received clinical trial information that was accurate or correct, allowing them to proceed to the next step. Ultimately, among these 26 patients, 9 decided not to proceed, 3 failed the study pre-screen, 7 are in progress, and 7 were successfully referred. Notably, 2 of these 7 successful referrals were due to Ancora.ai finding contact information outside of ClinicalTrials.gov.

Nine patients received a “non-actionable” response indicating that the clinical trial was no longer actively recruiting and offering no additional information. All non-actionable responses were from industry-sponsored trials.

In sum, when patients received a response with accurate information on next steps, only 7 out of the 26 interested patients (or 27%) were successfully referred to a clinical trial.

Discussion

The results of this pilot study characterize the magnitude of the “last mile” problem in cancer clinical trials. Half of all patients initially interested in a clinical trial did not receive a follow-up response from the investigative site. Among those who did receive a response, less than half received information that was timely, accurate, and actionable.

This loss represents a missed patient engagement opportunity that is addressable and contributes to longer clinical trial durations. “Last mile” inefficiencies are likely also contributing to unplanned and unbudgeted protocol amendments implemented to improve recruitment effectiveness.

There is a critical need to raise awareness of the “last mile” problem across the clinical research community. By acknowledging the prevalence and consequences of the issue, stakeholders can prioritize efforts to address it effectively. As a first step, clinical research sponsors need to provide more timely and accurate information (i.e., site contact, clinical trial background and requirements, open enrollment period) on ClinicalTrials.gov and public-facing communications.

Similarly, there is an opportunity to raise awareness and education among site personnel to prepare them to respond to patient inquiries promptly and fully. Redesigning the referral processes and dedicating personnel to managing patient referrals will likely contribute to more successful enrollment.

Implementing targeted strategies, raising awareness, and investing in process redesign and dedicated resources are essential steps towards enhancing patient recruitment and referral processes. By addressing these challenges, the clinical research community can repair the “last mile” leak in the recruitment funnel and enable more patients to pass through the pre-screening stage and on to consent and enrollment.

Next Steps

Tufts CSDD and Ancora.ai will be initiating a larger empirical study to gather more robust data characterizing the magnitude of the “last mile” problem across disease conditions and identifying improvement opportunities. This study will include an assessment of the percentage of unanswered patient inquiries, the average response time, and the quality of the investigative site responses.

We invite sponsors and contract research organizations to join us in this working group study to evaluate the “last mile” challenge, update the legacy recruitment funnel framework, and ultimately diversify and accelerate patient recruitment in clinical trials.

For additional information about this pilot study and future planned studies, please contact Ruby Madison Ford (ruby.ford@tufts.edu) and Danielle Ralic (danielle@ancora.ai).