AI matching patients to clinical trials from healthcare data

AI-Powered Patient Recruitment & Intelligent Trial 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.

Industry

Industry

Pharmaceuticals, Life Sciences, Clinical Research, Healthcare AI

Project Type

Project Type

AI R&D, Clinical AI, NLP, Predictive Analytics, Healthcare Data Integration

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Project Challenges

Patient recruitment can be particularly difficult for trials involving complex eligibility criteria, rare diseases, and biomarker-defined populations. Clinical teams often need to review information across multiple healthcare systems and unstructured clinical records to determine whether patients qualify.

Complex Eligibility Assessment

Clinical trial protocols can involve hundreds of inclusion and exclusion criteria spanning structured and unstructured patient data.

Fragmented Patient Information

Relevant information was distributed across EHRs, laboratory systems, imaging repositories, genomics platforms, physician notes, medications, and patient histories.

Recruitment Uncertainty & Screen Failures

Limited visibility into eligible patient populations and high screen failure rates made it difficult to forecast recruitment and select the right sites.

Smart Solutions

We engineered a cloud-native AI platform that supports the patient recruitment lifecycle from trial feasibility and candidate discovery through enrollment and retention.

Intelligent Patient Discovery & Matching

Built AI capabilities to identify and prioritize patients most likely to meet complex clinical trial eligibility criteria across structured and unstructured healthcare data.

Precision Medicine & Explainable Eligibility

Integrated clinical, genomic, and biomarker data to support precision trials, with transparent eligibility reasoning and confidence scores for investigator validation.

Predictive Recruitment & Site Intelligence

Applied predictive analytics to forecast enrollment, identify high-potential sites, and anticipate recruitment bottlenecks and patient dropout risks.

Results & Impact

The platform helped clinical research teams reduce manual recruitment effort, improve candidate identification, and make recruitment decisions using predictive patient and site intelligence.

Faster Patient Recruitment

Reduced recruitment timelines by weeks to months through AI-assisted candidate discovery and automated eligibility assessment.

Higher-Confidence Candidate Matching

Prioritized patients with stronger eligibility confidence, helping reduce unnecessary screening and improve recruitment efficiency.

Smarter Trial & Site Planning

Enabled sponsors to assess eligible patient populations, forecast recruitment performance, and identify high-potential sites earlier in the clinical development process.

Explore Transformation Stories

Explore our case studies to see how Infocusp has helped businesses solve complex challenges into measurable success.

From Trial Data to Clinical Decisions Powered by AI

A global pharmaceutical organization needed to rapidly extract actionable insights from complex, multi-center clinical trial data while maintaining patient safety and regulatory compliance. We developed an AI-powered clinical trial intelligence platform that combines clinical data integration, safety signal detection, patient stratification, predictive modeling, and clinical analytics to transform fragmented trial data into actionable intelligence.

Accelerating Single-Cell Analysis with AI-Optimized, Resource-Efficient Computing

Analyzing millions of cells via single-cell RNA sequencing (scRNA-seq) is transforming drug discovery and precision medicine, but it is computationally costly and typically demands high-end GPUs. To address this, we built scaLR (Single-cell Analysis using Low Resource), an AI platform that minimizes the computational footprint of scRNA-seq without compromising annotation accuracy. Through smart feature selection, resource-efficient deep learning, and optimized processing, scaLR allows researchers to conduct large-scale cell annotation and biomarker discovery on standard hardware.
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We provide innovative and scalable IT solutions designed to streamline operations, enhance security, and drive business growth. From advanced software development to cloud and AI-driven technologies, we empower businesses to thrive in the digital era.

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