Accelerating clinical trial recruitment with intelligent patient matching
Patient recruitment is one of the most challenging stages of clinical development, particularly for precision medicine and rare disease trials. We built an AI-powered recruitment and intelligent matching platform that turns fragmented healthcare data into actionable recruitment intelligence.
About the client
A pharmaceutical and clinical research organization working across complex clinical trials, including precision medicine and rare disease studies.
The recruitment process required teams to assess patient information across multiple healthcare systems and clinical records against complex trial eligibility criteria. This made candidate identification time-consuming and created uncertainty around recruitment potential and site performance.
The organization needed an AI-powered platform to identify suitable candidates, support eligibility assessment, and provide greater visibility into recruitment planning.
The business challenge
Patient recruitment becomes particularly challenging when trials involve complex eligibility criteria, rare diseases, and biomarker-defined populations. Relevant patient information is often distributed across structured healthcare systems and unstructured clinical records.
The client needed a scalable approach to identify eligible patients more efficiently while improving recruitment forecasting and reducing unnecessary screening.
Key Challenges
- Assessing complex inclusion and exclusion criteria across patient data.
- Consolidating fragmented information from multiple healthcare systems.
- Extracting relevant information from unstructured clinical records.
- Identifying eligible patients for precision and rare disease trials.
- Reducing uncertainty around patient availability and recruitment potential.
- Improving site selection and minimizing screen failures.
How we solved it
We engineered a cloud-native AI platform supporting the patient recruitment lifecycle from trial feasibility and candidate discovery through enrollment and retention.
The platform combined clinical AI, NLP, predictive analytics, and healthcare data integration to identify suitable candidates, provide transparent eligibility assessments, and forecast recruitment performance.
Solution Highlights
- Identified and prioritized patients likely to meet complex trial eligibility criteria across healthcare data.
- Evaluated patient information against trial criteria to support faster candidate screening.
- Integrated clinical, genomic, and biomarker data for targeted trial recruitment.
- Provided eligibility reasoning and confidence scores for investigator validation.
- Forecasted enrollment performance, recruitment bottlenecks, and patient dropout risks.
- Assessed eligible patient populations to support feasibility analysis and high-potential site selection.
Business outcomes
The platform helped clinical research teams reduce manual recruitment effort, improve candidate identification, and make recruitment decisions using predictive patient and site intelligence.
AI-assisted matching and recruitment analytics provided greater visibility into eligible patient populations and recruitment potential earlier in the clinical development process.
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Faster patient recruitment
Reduce timelines by weeks to months.
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Higher-confidence matching
Prioritize better eligible candidates.
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Reduced manual screening
Automate data review and eligibility checks.
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Improved recruitment visibility
Greater insight into eligible patient populations.
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Smarter site selection
Identify high-potential sites.
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Proactive recruitment planning
Forecast enrollment and identify risks early.
How might this challenge look in your industry?
The underlying challenge of identifying the right candidates from fragmented data, automating eligibility assessment, and improving recruitment intelligence extends across industries where organizations need to match complex requirements with large, distributed datasets.
- Pharmaceuticals
- Identifying eligible patients and optimizing recruitment for clinical trials.
- Healthcare
- Matching patients with appropriate care programs, specialists, or clinical services.
- Insurance
- Assessing customer and risk information against complex policy and eligibility requirements.
- Financial Services
- Matching customers with suitable financial products based on multiple eligibility factors.
- HR & Recruitment
- Matching candidates with roles based on skills, experience, and job requirements.
- Life Sciences
- Identifying relevant research cohorts from complex biological and clinical datasets.
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Facing a similar challenge?
Whether you're optimizing clinical trial recruitment, matching complex datasets, or building AI systems for healthcare and life sciences, we can help engineer the data integration, AI models, NLP, and predictive infrastructure required to turn fragmented information into actionable intelligence.
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