# Clinical Trials AI Platform: 85% Faster Screening

> Manual literature review, patient matching, and drug safety monitoring consuming thousands of hours across research teams.

- Canonical: https://suvegasoft.co.uk/case-studies/clinical-trials/
- Client: Global Pharmaceutical Company
- Industry: Healthcare & Life Sciences
- Timeline: 6 months delivery
- Technologies: LangGraph, Braintrust, OpenAI, NVIDIA NIMs, React/Next.js
- Published: 2025-01-15T00:00:00.000Z

**Key results:** 85% reduction in screening time · 8x faster patient matching · 6x pharmacovigilance throughput · FDA 21 CFR Part 11 compliant

## Challenge

A global pharmaceutical company faced significant operational bottlenecks across their clinical trials process. Research teams were spending thousands of hours on manual tasks:

- **Literature Review**: Manually screening papers against PICO criteria
- **Patient Matching**: Slow, error-prone matching of patients to trials
- **Drug Safety Monitoring**: Labour-intensive pharmacovigilance processes
- **EHR Data Access**: Limited ability to test with realistic patient data
- **Patient Recruitment**: Manual outreach and coordination

The compliance requirements made automation particularly challenging—any solution needed to meet FDA 21 CFR Part 11, HIPAA, and GxP standards.

## Solution

We built an end-to-end agentic AI platform covering 5 critical workflows:

### 1. Literature Review Agent

Automated screening of research papers using PICO (Population, Intervention, Comparison, Outcome) criteria. The agent reads abstracts, extracts relevant information, and flags papers for human review.

### 2. Synthetic EHR Data Generation

Created realistic synthetic patient records for testing and validation—enabling development without exposing real patient data.

### 3. Intelligent Patient-Trial Matching

AI-powered matching engine that analyses patient profiles against trial eligibility criteria, dramatically reducing time-to-enrolment.

### 4. Agentic Patient Recruitment Assistant

Automated outreach and scheduling coordination, handling routine communications while escalating complex cases to human coordinators.

### 5. Pharmacovigilance with Naranjo ADR Scale

Automated adverse drug reaction assessment using the Naranjo scale, processing safety reports at 6x previous throughput.

### Architecture Highlights

- **LangGraph** for complex multi-step agent orchestration
- **Braintrust** for continuous evaluation and quality monitoring
- **NVIDIA NIMs** for high-performance inference
- Full audit trail for regulatory compliance

## Results

The platform transformed clinical trial operations:

| Metric                    | Before     | After     | Improvement       |
| ------------------------- | ---------- | --------- | ----------------- |
| Literature screening time | Weeks      | Days      | **85% reduction** |
| Patient matching speed    | 8 days avg | 1 day avg | **8x faster**     |
| Pharmacovigilance reports | 50/week    | 300/week  | **6x throughput** |

### Compliance Achievement

- Passed FDA 21 CFR Part 11 compliance review
- Full HIPAA compliance maintained
- GxP documentation standards met
- Complete audit trail for all AI decisions

## Technical Details

### Tech Stack

- **Orchestration**: LangGraph for multi-agent workflows
- **Evaluation**: Braintrust for continuous quality monitoring
- **LLMs**: OpenAI GPT-4, NVIDIA NIMs for specialized tasks
- **Frontend**: React/Next.js dashboard for trial coordinators
- **Infrastructure**: HIPAA-compliant cloud deployment

### Key Design Decisions

1. **Human-in-the-loop**: Critical decisions always flagged for human review
2. **Explainability**: Every AI recommendation includes reasoning chain
3. **Audit Trail**: Complete logging for regulatory compliance
4. **Graceful Degradation**: System falls back to manual workflows if AI confidence is low

## Project Details

- **Duration**: 6 months from kickoff to production
- **Team**: 4 engineers (2 AI/ML, 1 full-stack, 1 DevOps)
- **Status**: Live in production with ongoing support
- **Compliance**: HIPAA, FDA 21 CFR Part 11, GxP

***

_Interested in AI for clinical trials or life sciences? [Contact us](/contact) to discuss your use case._