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Improving representativeness in trial recruitment: A data-driven approach
Pooyan Khajehpour1, Jonathan Amar1, Krisda H Chaiyachati1
1Verily Life Sciences, South San Francisco, CA, USA.
Contemporary Clinical Trials Communications
|April 9, 2026
Summary
This study proposes a data-driven method using reinforcement learning to optimize digital campaign budgets for clinical trial recruitment. This approach aims to equitably increase participant accrual across diverse demographic groups, improving clinical research diversity.
Area of Science:
- Clinical Research
- Health Equity
- Data Science
Background:
- Under-representation of diverse subgroups in clinical studies poses risks to patient health outcomes.
- Clinical trial recruitment increasingly depends on digital marketing strategies.
- Current budget management may not adequately address representativeness goals.
Purpose of the Study:
- To propose a data-driven approach for managing digital campaign budgets in clinical research.
- To integrate representativeness goals into participant recruitment strategies.
- To facilitate the enrollment of diverse populations in clinical studies.
Main Methods:
- Application of a reinforcement learning approach.
- Utilizing a restless multi-armed bandit model for dynamic resource allocation.
- Developing sequential resource allocation strategies based on cohort data.
Main Results:
- The proposed method generates strategies to maximize accrual numbers equitably across target cohorts.
- Enables dynamic allocation of resources to digital marketing channels.
- Provides a framework for data-driven management of clinical study accrual.
Conclusions:
- This novel methodological approach can enhance equity and diversity in clinical research.
- Offers a data-driven strategy to manage resource allocation for improved participant recruitment.
- Addresses the persistent challenge of under-representation in clinical trials.
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