Clinical trial recruitment remains one of the biggest challenges in life sciences. Traditional recruitment often depends on referrals, site databases, and manual screening, which can make it difficult to identify eligible patients at the right time.
Clinical trial recruitment can become more proactive when real-world data is used to understand a patient's longitudinal healthcare journey. Data from electronic health records, claims, laboratory results, registries, and diagnostic reports can help research teams identify potential participants earlier.
The use of agentic AI in clinical trials can take this process further. Instead of simply matching patients with trial criteria, AI systems can support pre-screening, monitor relevant data, and help teams prioritize potential candidates. This can reduce manual work for research coordinators and improve the quality of referrals.
Real-world data in clinical trials can also support precision recruitment. For oncology and rare disease studies, combining clinical information with genomic and biomarker data can help identify patients who are more closely aligned with specific study requirements.
As clinical research becomes increasingly data-driven, organizations need connected systems that can bring together clinical, genomic, and operational data. This creates opportunities to improve patient identification while reducing unnecessary screening and administrative effort.
ClairLabs explores how combining agentic AI with real-world data can help move clinical trial recruitment from a reactive process toward more intelligent and proactive patient identification.
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