A study's aggregate enrollment number can look reasonably on track while masking a specific site that's genuinely struggling and without site-level diagnostics, that underperforming site often just gets watched rather than actively supported, until the aggregate number itself starts slipping. Here are six signs a specific site needs coaching, and what that coaching should actually target.
A site generating plenty of initial interest but converting few of those leads to an actual screening visit likely has friction somewhere in the pre-screening or scheduling process, not a demand problem. Coaching here should focus on the specific point where leads stall.
A site with a much higher screen-fail rate than comparable sites may be interpreting eligibility criteria differently, or pre-screening less carefully before bringing patients in. This is a specific, addressable training gap, not a reflection of the local patient population alone.
If a site's eConsent completion rate is notably lower than comparable sites, the issue may be how consent is being presented or supported locally, rather than genuine patient reluctance. This is worth investigating specifically rather than assuming it reflects unavoidable local factors.
A site that takes meaningfully longer than others to move a screened patient to randomization may have a specific internal bottleneck — scheduling, staff availability, an internal approval step — worth identifying directly rather than attributing to generic site variability.
A site that enrolled well early but has since slowed significantly may have exhausted its most accessible patient pool and needs support identifying new referral pathways, rather than simply being expected to maintain an early pace indefinitely.
If a site isn't using recruitment materials, referral tools, or support resources made available to all sites, that's a specific, addressable gap the resources may not be well understood, or the site may not realize particular tools are available to them.
| Signal | Portfolio View | Site-Level Diagnosis |
|---|---|---|
| Lead-to-screen conversion | Aggregate rate looks fine | A specific site's conversion gap is visible |
| Screen-fail rate | Averaged across all sites | One outlier site's rate stands out |
| Consent completion | Blended into overall rate | A lagging site is identifiable |
| Screening-to-randomization time | Averaged | A specific bottleneck site is visible |
| Enrollment trend | Aggregate pace looks acceptable | A flattening site is caught before it drags the total |
| Resource engagement | Not visible at all in aggregate metrics | Specific non-engagement is identifiable |
Coaching a struggling site early, once a specific diagnosis is available, is considerably more effective than waiting until the aggregate enrollment number is visibly behind and then trying to identify which sites are responsible after the fact.
Because pre-screen, consent, and enrollment funnel data live within one connected system, site-level performance across each funnel stage is visible directly, rather than requiring separate manual analysis per site. This supports identifying which specific stage — screening, consent, or scheduling — is actually driving a given site's underperformance, rather than treating "enrollment is slow" as one undifferentiated problem.
An aggregate enrollment number tells you whether the study overall is on track. It doesn't tell you which specific site needs help, or with what. That level of diagnosis is what turns a general concern into an actionable coaching conversation.
See how Cloudbyz's Patient Recruitment platform surfaces site-level enrollment diagnostics — book a demo