Global pharma company cuts clinical trial matching from weeks to hours with AI-powered enrollment platform

A leading life sciences company faced persistent bottlenecks in clinical trial enrollment due to manual, error-prone patient-to-trial matching processes. Clinical sites operated on disconnected data, inconsistent workflows, and static eligibility checks, resulting in 2–4 week matching cycles and missed enrollment windows. The existing systems could not handle high-dimensional datasets including genomic data from NGS panels and rare disease cohorts, and lacked real-time visibility into trial availability or evolving inclusion criteria.

Ideas2IT collaborated with MolecularMatch to build a precision-driven trial match platform integrating molecular, clinical, and operational data into one intelligent workflow. The platform features a proprietary matching engine combining clinical, genomic, and histological data for high-specificity trial ranking, a continuously refreshed live search engine, and advanced filters based on tumor type, mutation profile, ECOG performance, and prior treatment history. The system was deployed on cloud-native AWS infrastructure with REST APIs delivering trial recommendations directly into care team systems.

Matching time was reduced from 2–4 weeks to under 8 hours, enabling same-day enrollment decisions across oncology and immunotherapy trials. Match accuracy increased through integrated genomic and clinical data, and the platform expanded visibility into rare and niche study protocols. The platform now powers real-time enrollment support, bringing more eligible patients into life-saving studies across multiple sites.

• Combining clinical and genomic context together is essential — matching precision increases only when both data layers are used simultaneously.
• Real-time infrastructure is non-negotiable for modern recruitment; static workflows cannot keep pace with evolving trial criteria and patient data.
• API-based embedding into existing care workflows drives daily adoption far more effectively than standalone dashboards.