Clinical Research

This AI solution optimizes clinical research by enhancing patient matching and trial optimization. It accelerates the development of new therapies and improves the efficiency of clinical studies.

Project Overview

Industry: Healthcare & Life Sciences (Pharmaceuticals, Clinical Trials, Academic Research)

Scope: Multi-site clinical research network with 500+ ongoing trials

Project Duration: 9 months

Team Size: 3 Data Scientists, 2 Clinical Research Coordinators, 1 Regulatory Specialist

Business Challenge

The client faced major inefficiencies in managing clinical trials:

Difficulty identifying and recruiting eligible patients quickly
High dropout rates due to poor patient-trial fit
Manual matching processes consuming staff resources
Delayed trial timelines leading to increased costs and slower drug development
Compliance risks from inconsistent documentation and reporting

Our Approach

We developed an AI-powered clinical research optimization platform that streamlines patient-trial matching and enhances trial operations. The solution emphasized:

Speed: Accelerating patient recruitment and enrollment
Accuracy: Matching patients to trials with higher eligibility precision
Retention: Improving patient engagement and reducing dropout rates
Compliance: Ensuring regulatory and documentation standards are met

AI-Powered Clinical Research Optimization

NLP models to extract eligibility criteria from trial protocols
Patient-trial matching using EHR data, demographics, and clinical history
Predictive analytics to identify patients most likely to complete trials
Automated documentation workflows for regulatory reporting
Dashboards for trial managers to track enrollment and performance

Implementation Process

Phase 1: Data integration from EHR, trial registries, and research protocols
Phase 2: Model development for eligibility extraction and patient matching
Phase 3: Pilot testing with oncology and cardiovascular trials
Phase 4: Full deployment across multiple therapeutic areas and trial sites

Quality Assurance

Clinical validation of AI patient matching accuracy
Monitoring recruitment rates and dropout trends
Regulatory compliance audits for data handling and reporting
Feedback loops with investigators and research coordinators

Results

Productivity Improvements

Time spent on patient eligibility screening reduced by 60%
Faster trial startup timelines across therapeutic areas
Reduced manual workload for research coordinators

Trial Performance

Patient recruitment rates improved by 35%
Trial dropout rates reduced by 20%
Increased diversity in patient recruitment due to broader matching criteria

Business Impact

$25M annual savings from faster trial timelines and reduced delays
Accelerated drug development and market entry
Improved sponsor satisfaction and trial site reputation

Technical Implementation

Models: NLP for protocol parsing, predictive analytics for patient-trial fit
Data Sources: EHR, lab results, trial registries, genomic data
System Integration: APIs with clinical trial management systems (CTMS) and EHR
Automation Layer: Enrollment tracking, compliance reporting, and patient communication

Key Features

AI-driven patient-trial matching with real-time eligibility scoring
Automated extraction of criteria from trial protocols
Predictive models for patient adherence and trial completion likelihood
Trial performance dashboards with recruitment KPIs
Compliance-ready reporting for regulators and sponsors

Client Feedback

“”

Patient recruitment was always our biggest bottleneck. With the AI system, we can now identify eligible participants in hours instead of weeks, and our trial timelines are finally under control.

Implementation Timeline

Before Implementation

Manual eligibility screening taking weeks per trial
High dropout rates due to poor patient-trial fit
Slow recruitment leading to delayed trial timelines
High operational costs from inefficient workflows

After Implementation

60% faster eligibility screening
35% faster recruitment
20% lower dropout rates
$25M annual savings from improved efficiency

Quality Control Process

Continuous benchmarking against traditional recruitment methods
Regular audits to ensure compliance with FDA, EMA, and HIPAA standards
Patient feedback collection to enhance engagement strategies
Model retraining based on real-world recruitment and trial outcomes

Implementation Challenges

Data standardization across different hospitals and EHR systems
Ensuring fairness and diversity in AI-driven patient matching
Managing regulatory complexity in international trials
Building trust with investigators and research staff in AI recommendations

Continuous Improvement

Monthly updates to eligibility extraction models with new trial protocols
Expansion to rare disease and precision medicine trials
Improved prediction models for patient adherence and retention
Integration with wearable and remote monitoring devices for trial data capture

Future Enhancements

Integration with global trial registries for broader patient access
AI-driven adaptive trial design based on real-time outcomes
Expansion of recruitment to include underrepresented populations
Predictive analytics for trial site performance optimization