The 7-Day Enrollment Recovery Map

“Enrollment Is Behind Plan” Is Not a Diagnosis.

It tells you there is a problem.

It does not tell you what the problem is.

Consider two studies that are both 30% behind enrollment plan.

In Study A, not enough potentially eligible patients are being identified.

In Study B, plenty of candidates are being identified — but coordinators are screening large numbers of low-probability patients.

The enrollment dashboard may show the same outcome.

The solution is completely different.

Study A may need better cohort access, referral pathways, or recruitment reach.

Study B may need better pre-screening, eligibility evidence, protocol interpretation, or candidate prioritization.

Now consider Study C. The right patients exist. The sites are finding them. They are likely eligible.

But:

outreach takes too long,
historical medical evidence is unavailable,
the participant cannot easily travel,
reimbursement requires out-of-pocket spending,
or the trial experience creates too much friction.

Again:

Same enrollment symptom. Different operating constraint.

Adding more sites to all three studies may only make one of them better.

5. Core Concept

Find the Constraint Before You Scale the Response.

A useful enrollment recovery process should answer five questions:

Where are patients dropping?

Why are they dropping?

How long does each transition take?

Who owns the delayed transition?

Would adding volume actually improve randomization?

The 7-Day Enrollment Recovery Map is designed to answer those questions quickly.

6. The 7-Day Enrollment Recovery Map

DAY 1 — Reconstruct the Real Enrollment Funnel

Objective

Stop looking only at enrolled/randomized totals.

Map the participant journey from the earliest available candidate signal.

Build this funnel

Potential candidate identified

→ Candidate reviewed

→ Contact attempted

→ Contact successful

→ Pre-screen completed

→ Likely eligible

→ Consent initiated

→ Consent completed

→ Formal screening

→ Screen passed

→ Randomized/enrolled

For each stage record

Number entering stage Number progressing Number dropping Median elapsed time Primary owner Primary reason for failure

Questions to ask

Can we measure every transition?

Where is the largest absolute drop?

Where is the lowest conversion rate?

Where is the longest waiting time? Which stages depend on spreadsheets, email, or coordinator memory?

Day 1 red flag

If the team cannot reconstruct this funnel, the study has an observability problem before it has a recruitment problem.

Day 1 output

Participant Enrollment Funnel Map

7. DAY 2 — Separate Candidate Volume From Candidate Quality

Objective

Determine whether the problem is insufficient reach or poor match quality.

A study may be generating large numbers of “leads” while creating little real enrollment value.

Examine

Candidates identified per week

Candidates reviewed per coordinator

Pre-screen pass rate

Formal screen-failure rate

Reason for screen failure

Source of candidate

Site or referral source

Ask

Which sources produce the highest percentage of genuinely eligible candidates?

Are coordinators repeatedly reviewing clearly unsuitable patients?

Are recruitment campaigns optimized around lead volume instead of randomized-patient yield? Are eligibility signals available before coordinator review?

Can important protocol criteria be determined from available clinical data?

Day 2 diagnostic

If candidate volume is low:

Supply constraint

Potential response: increase cohort reach, referral sources, provider relationships, patient recruitment activity or geography.

If candidate volume is high but qualification is low:

Match-quality constraint

Potential response: improve eligibility logic, source-data access, pre-screening or candidate prioritization.

Day 2 red flag

Recruitment performance is reported as “leads generated” without tracking randomized patients by source.

Day 2 output

Candidate Source × Conversion Matrix

8. DAY 3 — Audit Screen Failures

Objective

Determine which screen failures are clinically unavoidable and which may be operationally preventable.

Do not treat “screen failure” as one category.

Break failures into categories

True clinical ineligibility The participant genuinely does not meet protocol criteria. Missing evidence Eligibility could not be confirmed because records, labs, imaging, medication history, biomarker results, or other evidence were unavailable.

Protocol interpretation Candidate was advanced because inclusion/exclusion criteria were misunderstood or inconsistently interpreted.

Timing/window failure The participant was otherwise suitable but missed an operational or clinical window.

Participant burden/refusal The participant decided participation was too difficult.

Travel/logistics Distance, transportation, accommodation, childcare or work commitments blocked participation.

Consent friction The participant did not complete or understand consent.

Operational delay Follow-up, scheduling, documentation or site responsiveness caused the candidate to fall out.

Ask

What percentage of failures are genuinely unavoidable?

What percentage could have been detected before formal screening?

What percentage result from missing information?

Which failures cluster around particular sites?

Which failures repeat across multiple studies?

Day 3 red flag

The organization has a screen-failure percentage but cannot produce a reliable screen-failure reason distribution.

Day 3 output

Preventable vs Unavoidable Screen-Failure Map

9. DAY 4 — Measure Site Follow-Up and Coordinator Friction

Objective

Determine whether qualified candidates are losing momentum after identification.

Measure

Time from candidate identified → first review

Time from review → contact attempt

Time from contact → pre-screen

Time from likely eligible → consent

Time from consent → screening

Ask

Are coordinators receiving candidate alerts quickly?

Does candidate information arrive in one place?

Do coordinators have enough evidence to take action?

Are staff checking multiple systems?

Is follow-up dependent on manual reminders?

Do sites know which candidates deserve priority?

Is workload evenly distributed?

Look for this pattern

A study may have enough candidates and acceptable eligibility rates but still enroll slowly because each candidate spends days sitting between workflow owners.

This is handoff latency.

Day 4 red flag

A likely-eligible candidate can sit untouched for days because no workflow explicitly owns the next action. Day 4 output

Site Response-Time Heatmap

10. DAY 5 — Audit Consent, Data Access and Participant Burden

Objective

Identify whether good candidates are being lost after initial interest.

Consent questions

Can the patient understand the study easily?

Can consent happen remotely when appropriate?

Can the team see consent status without checking separate systems?

Is re-consent straightforward?

Are consent permissions aligned with downstream data use?

Data questions

Are external records required?

How are those records obtained?

Does the site rely on patient-supplied documents?

Are coordinators manually downloading, uploading or re-entering clinical information?

Does unavailable evidence delay screening?

Participant burden questions

How many visits?

How much travel?

How much time away from work?

Childcare? Parking?

Accommodation?

Device burden?

Repeated questionnaires?

At-home tasks?

Out-of-pocket expense?

Day 5 red flag

The trial is clinically appropriate for the patient but operationally impractical for them.

Day 5 output

Participant Friction Map

11. DAY 6 — Audit Reimbursement and Payment Friction

Objective

Determine whether participation economics are creating avoidable friction.

Payment problems do not always begin inside the payment platform.

The delay may occur before the rail is triggered.

Map the workflow

Visit/milestone completed

→ Completion verified

→ Payment eligibility confirmed

→ Supporting documentation approved

→ Payment instruction created

→ Funds disbursed → Participant notified

Measure

Median time from milestone completion to payment

Percentage requiring manual exceptions

Percentage requiring receipts or additional documentation

Site staff time spent handling reimbursement questions

Number of participant payment complaints

Number of payment-related escalations

Ask

Does a patient need to finance trial participation?

Are travel costs prepaid or reimbursed later?

Does the site know payment status?

Can the patient know payment status?

Can EDC/eCOA/visit status automatically trigger downstream workflow?

Day 6 red flag

The disbursement platform can pay quickly, but operational approval takes days or weeks.

Day 6 output

Milestone-to-Payment Flow Map

12. DAY 7 — Identify the Constraint and Choose the Recovery Action

Objective

Do not finish the week with 25 improvement ideas.

Identify the one or two constraints most responsible for enrollment underperformance. Classify the primary problem

A. Patient Supply Constraint

Not enough potential candidates are entering the funnel.

Possible response:

More referral sources New provider networks Additional patient recruitment channels Geographic expansion Selective site addition

B. Match Quality Constraint

Many candidates enter but few are plausibly eligible.

Possible response:

Protocol-to-patient matching Improved cohort criteria Better source-data access AI-assisted pre-screening Candidate prioritization

C. Site Conversion Constraint

Good candidates are identified but sites move them slowly.

Possible response:

Workflow automation Prioritization Site performance intervention Coordinator support Handoff redesign

D. Eligibility Evidence Constraint

Candidates cannot progress because required evidence is missing.

Possible response: EHR/source-data integrations FHIR workflows Provider data access Pre-screen evidence verification

E. Consent / Participant Experience Constraint

Candidates are interested but drop during consent or participation planning.

Possible response:

Consent redesign Remote consent Patient navigation Travel support Better communication

F. Payment/Reimbursement Constraint

Participant financial burden or payment friction creates dissatisfaction, delay or dropout.

Possible response:

Milestone-triggered payments Prepayment Travel orchestration Payment automation Better status visibility

13. The Recovery Decision Matrix

If candidate supply is low AND downstream conversion is strong

Consider adding:

sites,
referral sources,
patient-recruitment channels,
geographies. This is a genuine capacity/supply problem.

If candidate supply is high BUT pre-screen conversion is low

Do not simply add more candidate volume.

Investigate:

match quality,
eligibility logic,
source data,
recruitment targeting.

If pre-screen conversion is good BUT formal screen failures are high

Investigate:

missing clinical evidence,
protocol interpretation,
screening windows,
qualification logic.

If likely eligible candidates are not reaching consent/screening quickly

Investigate:

site response,
coordinator workload,
recontact,
scheduling,
handoffs.

If consented candidates fail to progress Investigate:

participant burden,
missing records,
travel,
study complexity,
scheduling.

If enrolled participants complain or drop after milestones

Investigate:

reimbursement,
payment timing,
communication,
ongoing participant burden.

Before You Add More Capacity, Identify the Constraint.

Adding sites works when you have a capacity problem.

It is much less effective when the real problem is:

candidate quality,
screen failures,
site latency,
missing clinical evidence,
consent friction,
or participant burden.

Use the complete 7-Day Recovery Map with your study team before escalating recruitment spend.

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