Clinical Trial Site Recruitment Intelligence POC

Predicting recruitment timelines, flagging slow-enrolling sites, and guiding smarter site allocation.

Clinical trial planning often depends on assumptions that change after study startup: site activation timing, enrollment pace, country mix, dropout risk, protocol complexity, and patient availability. This proof of concept frames those signals as a planning-intelligence problem rather than a pure reporting exercise.

The POC is a web-based decision-support concept that estimates how long individual sites may take to recruit enough patients, flags sites that appear likely to underperform, and supports better allocation or prioritization decisions. It is designed as an advisory-grade planning prototype, not a claim that AI can replace clinical operations judgment.

Business Problem

Clinical teams need earlier visibility when a site may miss recruitment expectations. Delayed signals can create downstream supply risk, inventory uncertainty, patient continuity concerns, and avoidable pressure on study timelines.

The practical challenge is not only predicting a date. It is giving planners, study teams, and supply stakeholders a clearer view of which sites may need action and which scenarios deserve attention.

What the POC Does

Forecasts site-level recruitment timelines using operational and planning assumptions.
Highlights slow-enrolling or high-risk sites before the issue becomes a late-cycle surprise.
Compares alternative allocation or prioritization scenarios across sites, countries, and recruitment assumptions.
Turns recruitment intelligence into planning discussion points that can be reviewed with clinical, supply, and leadership stakeholders.

Inputs Considered

Target enrollment by site or country.
Historical or assumed screening and enrollment rates.
Site activation timing and startup delays.
Dropout, screen-failure, and protocol complexity assumptions.
Country, region, therapeutic area, and site-capability attributes.
Supply planning considerations such as dosage, kit availability, shelf life, and replenishment lead time.

Outputs

Estimated recruitment completion timeline by site.
Slow-site flags and priority review indicators.
Scenario comparison for site allocation and recruitment acceleration.
Planning-ready narrative for supply risk, inventory exposure, and decision support.

Why It Matters

Recruitment uncertainty directly affects clinical supply planning. Better early signals can help teams reduce reactive expediting, improve scenario planning, and focus attention on the sites that matter most to timeline and supply assurance.

The strongest value is not a single forecast number. It is the ability to connect site behavior, enrollment assumptions, and supply decisions in a way that supports better governance.

SAP / Planning Relevance

For SAP planning professionals, this POC connects naturally to demand planning, supply planning, scenario management, and exception-based workflows. Site recruitment signals can inform forecast assumptions, country-level demand timing, inventory buffers, and clinical supply risk reviews.

The same planning discipline used in SAP IBP, APO, PP/DS, and S/4HANA contexts applies here: clean assumptions, traceable master data, exception logic, scenario comparison, and planner-ready decision support.

POC Status / Next Step

The concept is suitable for a lightweight prototype using safe simulated or anonymized data. A practical next step would be to define a small set of site attributes, enrollment assumptions, and output metrics, then validate the logic with clinical supply and study planning stakeholders before any broader implementation.

Need a practical perspective on clinical trial planning, SAP planning, or AI-enabled forecasting?

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