AI-Powered Clinical Trial Match Platform: How Ideas2IT Reduced Enrollment Time from Weeks to Hours

From Manual Matching to Real-Time Precision: A Case Study in Clinical Trial Enrollment Acceleration

One-liner summary:

A global pharmaceutical company partnered with Ideas2IT to build an AI-powered trial matching platform that reduced patient-trial matching time from weeks to hours, increasing enrollment accuracy across oncology and rare disease studies.

The Problem with the Status Quo

A leading life sciences company faced a persistent bottleneck in clinical trial enrollment. Patient-trial matching was manual, error-prone, and slow. Clinical sites operated on disconnected data, inconsistent workflows, and static eligibility checks, resulting in missed enrollment windows and delayed trial timelines.

With expanding research portfolios in oncology and immunotherapy, the need was clear: improve precision and scale enrollment without adding operational burden.

Where the Gaps Were

The client’s existing systems could not handle high-dimensional, fast-changing datasets, especially genomic data from NGS panels and rare disease cohorts.

Challenges included:

2–4 week matching cycles based on static rule sets and manual data review
Low accuracy in identifying eligible patients for complex trial protocols
No live visibility into trial availability or evolving inclusion criteria
Inability to factor in real-time indicators like ECOG scores, comorbidities, or resistance biomarkers

This led to slower trial activation, fewer patient enrollments, and missed opportunities for care delivery and research impact

What We Delivered

Ideas2IT collaborated with MolecularMatch, a clinical informatics leader, to build a precision-driven trial match platform integrating molecular, clinical, and operational data into one intelligent workflow.

Core Capabilities and Architecture:

Matching Algorithm: Proprietary engine combining clinical, genomic, and histological data to deliver high-specificity trial ranking
Live Search Engine: Continuously refreshed trial database matched against real-time patient records
Advanced Filters: Match refinement based on tumor type, mutation profile, ECOG performance, and prior treatment history
Cloud-Native Infrastructure: Dockerized microservices on AWS supporting multi-site scalability and secure deployment
Workflow Integration: REST APIs deliver trial recommendations directly into care team systems and clinical platforms

This system enabled trial-matching intelligence to flow from genomic labs to care teams within hours.

Outcomes We Achieved

Metric	Outcome
Matching time	Reduced from 2–4 weeks to under 8 hours
Match accuracy	Increased by using integrated genomic and clinical data
Trial visibility	Broader access to rare and niche study protocols
Enrollment speed	Faster identification and recruitment across sites

The platform now powers real-time enrollment support across oncology and immunotherapy trials bringing more eligible patients into life-saving studies.

Key Takeaways

Combine clinical and genomic context. Matching precision increases only when both data layers are used together
Real-time infrastructure is essential. Static workflows are too slow for modern recruitment targets
APIs outperform dashboards. Embedded integration into care workflows is what drives daily adoption
Rare trials need expert systems. Specialized protocols require deep, contextual intelligence

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