Case study 01 — Multi-site network — United States

Recruitment and operations automation: turning pre-screen failures into a second chance.

Our recent engagement with one of the largest clinical research site networks in the country — 30+ sites, one shared candidate pool. Recruitment, CRM and CTMS were connected into one continuous workflow, and the enrollment that had been quietly leaking out of pre-screening started coming back.

1 in 3

Participants who failed initial screening were matched to another active study.

Outreach volume per day — from 500 calls to 3,000, with no extra coordinators.

Participants prescreened each week against protocol criteria.

35%

Faster time-to-first-patient across the network.

Context

Not a reinvention. A connection.

The network already had a strong operational foundation. As it grew, the opportunity was to connect existing processes more effectively rather than replace them.

Streamline participant recruitment across a growing national footprint.
Improve visibility across sites so performance is comparable, not anecdotal.
Reduce repetitive administrative work for coordinators.
Build a secure foundation for responsible AI inside the network's own infrastructure.

Organization: One of the largest clinical research site networks in the US — headquartered in Houston, Texas, 30+ sites and growing
Scope: Pre-screening, referral routing and recruitment operations
Systems: CTMS, call-centre queue, referral sources, study eligibility criteria
Engagement: Discovery, workflow rebuild, automation, measurement layer

Our approach

Build on what already works.

Instead of adding another standalone platform, we created a connected ecosystem where recruitment, CRM and CTMS operate as one continuous workflow. Every improvement fit existing operations — teams never had to adapt to generic software.

Connected

Systems that talk to each other — recruitment, CRM and CTMS as one workflow.

Continuous

One end-to-end flow from first contact through study completion.

Secure

Client-owned infrastructure; participant data never leaves their environment.

Human-centered

Designed around how coordinators actually work, not around generic software.

The problem

The pipe was leaking at exactly the point no one was watching.

In most networks a pre-screen failure is a dead end: once excluded, always excluded. The candidate is interested, reachable and already in the system — and then they are gone.

The leaky pipe, fixed — a failed pre-screen is re-checked against every active trial and, when matched, re-enters pre-screening instead of being written off.

A candidate who failed pre-screening for one study was closed out — no one checked whether they qualified for any of the other trials running in the same network.
Coordinators re-keyed the same candidate details across the call queue, the CTMS and study-specific trackers, which slowed response times and introduced errors.
Drop-off was known in aggregate but not by stage, so the network could not tell whether the leak was in contact, pre-screen, consent or screening.
Nothing about a redirected or recovered candidate was recorded, so any recruitment lift stayed anecdotal.

What we built

One automation in pre-screening, and the operations around it made coherent.

An automated matching engine on the pre-screen step

Every failed pre-screen is immediately re-evaluated against the eligibility criteria of every other active trial in the network. Matches re-enter pre-screening for that study within the same workflow.

One candidate record across the operation

Referral intake, call-centre activity and CTMS state were consolidated so a coordinator sees one history per candidate instead of three partial ones — no re-keying between systems.

Stage-level funnel instrumentation

Lead, contacted, pre-screen, consent, screened, randomized — each transition timestamped and attributed to a site, so drop-off is visible where it happens rather than at the end.

A measurement layer on every redirect

Each redirected candidate is tagged from failure to re-entry, so recovered enrollment is reported as its own number and can be audited afterwards.

Guardrails

Only genuine pre-screen failures are redirected — consent withdrawals, screen failures and post-randomization drop-off are untouched.
Eligibility logic stays with the study team; the engine executes criteria, it does not invent them.
Every automated decision is logged with its inputs, so the workflow can be reviewed and reconstructed.

Connected clinical operations

One record. Every workflow.

Outreach and prescreening scale only if the operations behind them keep up. The participant record, the CRM and the CTMS were wired together so information is captured once and shared everywhere.

Patient Relationship Management (PRM)

Single participant record from first contact through study completion
Automated updates and reminders at each study milestone
Full visibility into consent status, preferences and communication history

CRM enhancement

Unified visibility across every site in the network
Workflows tailored to the network's operating model
Better collaboration across recruitment teams

CTMS integration

Information captured once
Shared seamlessly across workflows
Duplicate admin effort removed

Impact

Recovered enrollment, reported rather than assumed.

Roughly one in three candidates who failed their initial pre-screen qualified for another active trial in the network. Because every redirect is tagged, the network can state how much of its enrollment came from candidates it used to lose — and repeat the measurement study after study.

500 → 3,000

Outreach calls per day

Improvement in connect rate

Participants prescreened per week

Additional coordinators hired

35%

Faster time-to-first-patient

20+

Admin hours reclaimed each week

And one platform: every site running the same standardized workflows, with recovered enrollment reported as its own number rather than assumed.

Before

Pre-screen fail → closed. No cross-study check, no record.

After

Pre-screen fail → matched against every active trial → re-entry.

Ongoing

Stage-level funnel metrics per site, comparable across studies.

Take it with you

Get the full case study as a PDF.

The complete write-up: funnel baselines, the automation design, the guardrails we kept in place, and how the recovered enrollment is measured. Useful if you need to bring it to an operations or feasibility review.

Questions first? contact@matechnologies.net

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