Closing the Enrollment Gap: Strategies for Under-Enrolling Sites
Published June 2025 — A practical guide to identifying early indicators of enrollment shortfalls and implementing targeted interventions to bring underperforming sites back on track without compromising data integrity.
The Under-Enrollment Crisis
Under-enrollment is not an exception in clinical trials — it is the norm. Industry analyses consistently report that 37% of clinical trial sites fail to meet their enrollment targets, with the average site achieving only 63% of its projected enrollment commitment. The consequences ripple across the entire trial: timelines extend, budgets inflate, competitive windows narrow, and in the most severe cases, studies are terminated before reaching statistical power.
What makes under-enrollment particularly frustrating for sponsors and operations teams is that it is rarely a surprise. Enrollment shortfalls develop gradually, with early warning signals visible weeks or months before a site falls critically behind. The problem is not a lack of signals — it is the absence of systematic frameworks for detecting, interpreting, and acting on those signals in time to change outcomes.
This white paper provides a practical, data-informed guide to managing under-enrolling sites. It covers the identification of early indicators, the classification of root causes, the design and implementation of targeted interventions, and the decision frameworks for escalation — including the difficult decision of when to replace rather than remediate an underperforming site.
Early Warning Indicators
Enrollment underperformance does not emerge suddenly. Our analysis of 200+ site-level enrollment trajectories reveals consistent patterns of early warning signals that precede significant enrollment shortfalls.
Screening Velocity Decline (Weeks 2-4)
The earliest and most reliable indicator of future enrollment shortfall is a decline in screening velocity during the first 2-4 weeks after site activation. Sites that screen fewer than 40% of their projected patients in the first month have a 78% probability of falling below 50% of their total enrollment target. This metric is available almost immediately after enrollment opens and provides the longest possible intervention window.
Elevated Screen Failure Rates (Weeks 3-6)
A screen failure rate exceeding the therapeutic area benchmark by more than 15 percentage points is a strong signal that either the site's patient population does not align with the protocol's eligibility criteria or that the site's pre-screening processes are ineffective. In both cases, the root cause must be identified quickly — the distinction between a population mismatch (structural) and a process failure (correctable) determines whether intervention or replacement is the appropriate response.
Referral Pipeline Stagnation (Weeks 4-8)
Sites that rely on external referrals — as opposed to mining their own patient databases — are vulnerable to referral pipeline stagnation. If a site's referral volume does not increase or stabilize after the initial activation period, it suggests that outreach to referring physicians has been insufficient, the site's therapeutic area reputation is weaker than assumed, or competing trials at nearby sites are capturing the same referral sources.
Protocol Amendment Sensitivity (Any Time)
Sites that experience disproportionate enrollment declines following protocol amendments — even minor ones — are revealing underlying operational fragility. A well-functioning site should be able to absorb minor protocol changes without significant enrollment disruption. Sites that cannot are typically operating at the margins of their capacity and should be monitored closely.
Coordinator Turnover (Any Time)
Study coordinator turnover is one of the strongest predictors of enrollment disruption. Sites that lose a primary coordinator mid-study experience an average enrollment decline of 43% in the two months following the transition. This metric is often overlooked because it is qualitative and not captured in standard monitoring reports, but it should be tracked as a leading indicator.
Root Cause Classification
Not all enrollment shortfalls are created equal, and not all respond to the same interventions. Effective enrollment management requires accurate root cause classification before any intervention is initiated. Applying the wrong intervention to a correctly diagnosed problem wastes time and resources; applying an intervention to a misdiagnosed problem can actively worsen outcomes.
Our classification framework organizes root causes into four categories based on two dimensions: whether the cause is structural or operational, and whether it is addressable through site-level intervention or requires study-level action.
Population Mismatch
The site’s catchment area does not contain a sufficient concentration of eligible patients. This is often discovered when screen failure rates are high due to patients failing biomarker, staging, or treatment history criteria. Population mismatch is typically not correctable through site-level intervention and usually requires site replacement or strategic activation of supplemental sites in higher-prevalence regions.
Protocol Barrier
The protocol’s eligibility criteria, visit schedule, or procedural requirements create barriers that systematically exclude otherwise eligible patients or deter participation. This manifests as high screen failure rates or consent withdrawal rates that are consistent across multiple sites. Protocol barriers require study-level intervention — typically through protocol amendments that relax overly restrictive criteria or reduce patient burden.
Process Failure
The site has adequate patient access but is failing to convert that access into enrollment due to operational deficiencies. Common process failures include inadequate pre-screening procedures, slow consent processes, poor coordination between referring physicians and the research team, and insufficient follow-up on potential referrals. Process failures are the most amenable to targeted intervention and typically respond well to operational coaching, workflow redesign, and resource augmentation.
Competitive Displacement
Enrollment is being captured by competing clinical trials at the same or nearby sites. This is an increasingly common root cause as the number of active trials grows. Competitive displacement requires a combination of strategic communication to highlight the study’s differentiated value, acceleration of enrollment timelines at competitive sites, and in some cases, geographic expansion to regions with less trial density.
Targeted Intervention Strategies
Once the root cause is classified, targeted interventions can be deployed. The key principle is proportionality — the intervention should match the severity and type of the enrollment shortfall.
For sites with process failures, dedicated operational support including workflow optimization, pre-screening protocol refinement, and enrollment best practice training. Sites receiving operational coaching improved enrollment rates by an average of 35% within 6 weeks.
Deploying additional coordinator support to sites that are capacity-constrained but have strong patient access. This is particularly effective for sites that have demonstrated initial enrollment success but have plateaued due to staffing limitations.
Structured outreach programs that expand a site's referral network by engaging community physicians, specialty clinics, and patient advocacy groups. Most effective when combined with physician education events that increase awareness of the trial among potential referring providers.
Targeted digital and community-based patient recruitment campaigns focused on the specific geographic catchment area of underperforming sites. These campaigns use disease-specific messaging developed in collaboration with the site's clinical team to ensure alignment with local patient populations.
When competitive displacement is identified, strategies include accelerating the site's enrollment timeline, enhancing the patient value proposition through travel reimbursement or flexible scheduling, and working with investigators to ensure the study is being presented favorably relative to competing options.
When root cause analysis indicates that a site's enrollment shortfall is structural and not correctable through intervention, the most effective strategy is proactive replacement. Our data shows that early replacement — within the first 8 weeks of enrollment — recovers an average of 4.2 months compared to allowing a structurally compromised site to continue.
The Remediate vs. Replace Decision
One of the most consequential decisions in enrollment management is determining when to continue investing in an underperforming site versus replacing it. This decision is often delayed due to sunk cost bias, relationship considerations, and the logistical complexity of site replacement. However, our analysis indicates that the cost of delayed replacement significantly exceeds the cost of early action.
We recommend the following decision framework: If the root cause is operational and the site has adequate patient access, remediate with targeted intervention and monitor for improvement within a 6-week window. If the root cause is structural — population mismatch, geographic isolation from eligible patients, or insurmountable competitive displacement — initiate replacement immediately. The data is unambiguous: sites with structural enrollment barriers almost never recover to target, and the months spent waiting for improvement that will not come represent irrecoverable timeline loss.
A critical nuance: the 6-week remediation window must be accompanied by predefined success criteria. A vague commitment to “monitor and reassess” without quantitative benchmarks invariably leads to decision paralysis. We recommend defining a minimum enrollment rate that the site must achieve within the remediation period — typically 60% of the original per-site target rate — as the threshold for continuation versus replacement.
Measured Outcomes
Across 65 trials where the enrollment gap management framework was applied, the following outcomes were observed.
The proportion of sites falling below 50% of their enrollment commitment was reduced from 37% to 18% through the combination of early detection, root cause classification, and targeted intervention.
Sites that were replaced within 8 weeks of enrollment shortfall detection recovered an average of 4.2 months of timeline compared to sites where replacement was delayed beyond 12 weeks.
Sites with operational process failures that received targeted coaching improved their enrollment rate by an average of 35% within 6 weeks, with 72% of coached sites ultimately meeting or exceeding their enrollment targets.
The combined effect of reduced timeline extensions, fewer site replacements (through better initial selection), and optimized resource allocation generated average cost savings of $1.4 million per pivotal trial.
Conclusions
Enrollment shortfalls are not inevitable failures — they are manageable operational challenges that respond to systematic detection, accurate diagnosis, and targeted intervention. The framework presented in this paper provides clinical operations teams with a structured, data-informed approach to identifying under-enrollment risk early, classifying root causes accurately, deploying proportionate interventions, and making timely replacement decisions when remediation is not viable.
The most important shift required is cultural: moving from a reactive posture where enrollment problems are addressed after they become critical to a proactive posture where early warning signals trigger immediate investigation and response. Organizations that embed this proactive approach into their operational culture will not eliminate enrollment shortfalls entirely — but they will reduce their frequency, mitigate their severity, and recover from them faster than organizations that continue to manage enrollment retrospectively.
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