Why “Eligible Patients” Aren’t Always Enrollable Patients

The industry’s reliance on eligibility as a proxy for enrollment is understandable. Protocols are built around eligibility criteria. Feasibility assessments often rely on structured EHR data and historical counts. These approaches create a sense of certainty by producing clear numbers.

However, academic reviews of recruitment practices have shown that meeting eligibility thresholds does not reliably translate into successful enrollment, particularly when real-world care patterns diverge from protocol assumptions (NIH, NCBI Bookshelf).

Eligibility simplifies planning. It also masks downstream risk.

The Hidden Gap Between Eligibility and Enrollment

Several real-world factors sit between qualifying and enrolling, even when patients appear eligible.

Timing and disease progression

Eligibility is often assessed as a static attribute. In reality, eligibility is temporal. Patients move in and out of eligibility windows as disease progresses, treatments change, or clinical circumstances evolve.

Analyses of completed trials show that initial eligibility estimates frequently fail to account for this timing effect, contributing to enrollment shortfalls later in the study lifecycle (Desai et al., 2020).

Care pathway conflicts

Patients may meet eligibility criteria but are actively receiving care that conflicts with protocol participation—such as concurrent therapies, recent procedures, or care delivered outside the enrolling site.

These nuances are rarely captured in structured eligibility counts.

Operational and logistical friction

Visit burden, staffing constraints, and workflow limitations affect whether eligible patients can realistically participate. Reviews of recruitment barriers consistently identify operational burden as a major contributor to enrollment failure, even when eligibility criteria are met (NIH, NCBI Bookshelf).

Competing options

Eligibility does not guarantee availability. Patients may qualify for multiple studies, pursue alternative treatments, or decline participation altogether. None of these factors are visible in eligibility counts alone.

How Structured Data Overstates Eligibility

Structured EHR data is commonly used to estimate eligible populations, but it has well-documented limitations.

Diagnosis and procedure codes provide breadth, not depth. They often miss disease severity, progression, symptom burden, and clinical nuance that determine whether a patient truly aligns with protocol intent.

Peer-reviewed research has shown that reliance on coded data alone can substantially overestimate the number of patients who are realistically recruitable, particularly when compared with full clinical records (Idnay et al., 2023).

As a result, eligibility counts tend to be inflated relative to real enrollable populations.

What Actually Makes a Patient Enrollable

Enrollment success depends less on how many patients technically qualify and more on how well eligibility aligns with real-world care.

More reliable indicators of enrollability include:

• Clinical relevance to the protocol’s intent, not just criteria match

• Temporal alignment—identifying patients at the right moment in their care journey

• Continuity of care at the enrolling site

• Operational feasibility, including visit burden and staffing capacity

• Consistency between feasibility assumptions and recruitment execution

Reviews of completed trials suggest that contextual and longitudinal factors play a larger role in enrollment outcomes than eligibility counts alone, particularly in complex protocols (Idnay et al., 2023).

Why This Distinction Matters

When eligibility is mistaken for enrollability, downstream consequences follow:

• Overestimated cohorts

• Missed enrollment targets

• Late-stage protocol amendments

• Extended timelines and increased costs

• Frustration for sponsors, CROs, and sites alike

Public analyses of ClinicalTrials.gov data show that failure to meet enrollment targets remains one of the most common reasons trials are delayed or stopped early, even after feasibility assessments are completed (Desai et al., 2020).

This is not a recruitment execution problem, it’s a planning problem.

Reducing the Eligibility-Enrollability Gap

Teams that achieve more consistent enrollment outcomes tend to rethink how eligibility is evaluated:

• Incorporating clinical context earlier in feasibility

• Treating eligibility as dynamic rather than static

• Aligning feasibility and recruitment around the same underlying signals

• Revisiting assumptions as patient populations and care patterns evolve

At BEKhealth, this distinction between eligibility and enrollability is central to how BEKplatform is designed—bringing clinical context and longitudinal insight into early planning so feasibility reflects how patients actually move through care, not just how they appear in structured data.

The goal is not to eliminate uncertainty, but to reduce reliance on assumptions that research has repeatedly shown to be unreliable.

Frequently Asked Questions

What’s the difference between eligible and enrollable patients?
Eligible patients meet protocol criteria. Enrollable patients can realistically participate given timing, care context, and operational constraints.

Why don’t eligible patients enroll in trials?
Because eligibility alone does not account for disease progression, care pathways, operational burden, or patient availability (NIH, NCBI Bookshelf).

Do trials commonly overestimate patient availability?
Yes. Analyses of terminated and delayed trials consistently show that initial eligibility-based projections overstate real enrollment potential (Desai et al., 2020).

What data helps predict enrollability?
Longitudinal clinical context, temporal eligibility signals, and alignment with real-world care patterns (Idnay et al., 2023).

Enrollment Outcomes Are Shaped Long Before Recruitment Starts 

Enrollment success begins with how eligibility is understood. When teams distinguish between who qualifies and who can realistically participate, feasibility becomes more grounded, expectations become more accurate, and enrollment outcomes become better aligned with reality.