Phlow AI
Clinical Trials

Clinical Trial Navigator

Intelligent matching system connecting patients to relevant clinical trials.

3x
More eligible patients identified
5K+
Active Trials
92%
Match Accuracy
40%
Faster Enrollment
01

The Challenge

Clinical trials struggle to enroll enough patients, with 80% failing to meet enrollment timelines. Eligible patients often never learn about relevant trials.

Matching patients to trials requires understanding complex eligibility criteria and comparing against detailed medical histories. Manual matching is slow and error-prone.

Patients who could benefit from experimental treatments miss opportunities simply because the matching process cannot scale.

The treatments exist. The patients exist. We just cannot connect them fast enough.

80%
Trials miss enrollment goals
30%
Manual matching accuracy
02

The Solution

We developed an intelligent matching system that analyzes trial eligibility criteria and patient records to identify suitable matches with high accuracy.

The system handles complex inclusion/exclusion criteria, temporal requirements, and geographic constraints automatically.

Both patients and research coordinators can use the platform to discover matching opportunities.

Key Features

  • Natural language eligibility criteria parsing
  • Patient record analysis against criteria
  • Geographic and logistics matching
  • Match confidence scoring
  • Coordinator workflow integration
03

Implementation

Technical Approach

  • Claude API for criteria understanding
  • ClinicalTrials.gov API integration
  • FHIR-compatible patient data ingestion
  • PostgreSQL for match tracking
  • React patient and coordinator portals

Change Management

  • Research coordinator training
  • Patient consent and privacy workflows
  • Validation against manual matching
  • Continuous feedback loop from coordinators
04

Results & Impact

3x
More Matches
5K+
Active Trials
92%
Match Accuracy
40%
Faster Enrollment
  • Dramatically more eligible patients identified per trial
  • Enrollment timelines improved significantly
  • Coordinators spend time on patient relationships, not searching
  • Patients discover relevant trials they would have missed

Technology Stack

Claude APIPythonReactPostgreSQLFHIR