Overview
What is AI clinical-trial patient matching?
Clinical-trial patient matching uses natural-language processing over the EHR to compare each patient against a study's inclusion and exclusion criteria — surfacing eligible candidates that manual screening routinely misses.
Because eligibility criteria are complex and much of the relevant evidence lives in unstructured notes, manual matching is slow and incomplete. AI screens the whole population continuously, flags likely matches for clinician confirmation, and turns enrollment from a bottleneck into a pipeline.
What the system analyzes
The Challenge
Enrollment is the leading cause of trial delay
About 80% of clinical trials fail to enroll on time, and manual matching misses an estimated 60\u201370% of eligible patients because the evidence is buried in fragmented records.
Slow Manual Screening
Coordinators read charts one by one against complex criteria — a process that can't keep pace with enrollment targets.
Missed Eligible Patients
Much eligibility evidence sits in unstructured notes, so 60–70% of qualifying patients are never identified.
Enrollment Delays
~80% of trials miss enrollment timelines, delaying results and inflating study cost.
Under-Represented Populations
Manual outreach skews enrollment, undermining the diversity that makes results generalizable.
Education AI Solutions
How Solnix matches patients to trials
Criteria Translation
Convert protocol inclusion/exclusion criteria into machine-checkable logic.
EHR Screening
Screen structured data and unstructured notes across the population in real time.
Candidate Ranking
Rank likely matches with the supporting evidence surfaced for review.
Clinician Confirmation
Route ranked candidates to clinicians/coordinators for eligibility confirmation.
Outreach & Enrollment
Support consented outreach and track enrollment progress by site.
Diversity Monitoring
Monitor enrollment demographics to support representative studies.
End-to-End Implementation
How Solnix implements trial matching
A phased rollout from protocol translation through validated, real-time screening across sites \u2014 proving match rate and time-to-enrollment gains before scaling.
Discovery & AI Opportunity Mapping
We start by understanding your operations, data landscape, and goals, then map where AI delivers measurable value and where it does not. Every engagement begins with a prioritized opportunity backlog, not a technology pitch.
Data Foundation & Readiness
AI is only as good as the data behind it. We assess data quality, connect fragmented sources, and build the secure, governed pipelines that production AI depends on, with privacy and compliance designed in from the start.
Model & Agent Development
We build the models, retrieval systems, and AI agents tailored to your use cases, selecting the right approach (fine-tuning, RAG, multi-agent orchestration) for accuracy, cost, and latency, and validating against your real-world edge cases.
Integration & Workflow Embedding
AI only creates value when it lives inside the tools your teams already use. We embed models and agents into existing systems, surfaces, and workflows, so adoption is natural and human-in-the-loop controls stay in place.
Deployment, Security & Compliance
We deploy to production with the security, monitoring, and compliance controls enterprises require, including bias and fairness testing, audit logging, and the observability needed to operate AI responsibly at scale.
Optimization & Continuous Improvement
AI systems improve with use. We measure outcomes against the goals set in Phase 01, retrain and tune from live feedback, and expand to the next set of use cases, turning a single deployment into a compounding capability.
Key Capabilities
Key capabilities
NLP Over Clinical Notes
Extract eligibility evidence from unstructured text.
Benefits
Real-Time Eligibility
Continuously re-screen as new data arrives.
Benefits
Evidence Surfacing
Show why each patient matched.
Benefits
Site Dashboards
Track candidates and enrollment by site.
Benefits
Diversity Analytics
Measure representativeness of enrollment.
Benefits
Privacy by Design
Screen within compliant infrastructure.
Benefits
Who Benefits
Outcomes for sponsors, sites, and patients
For Sponsors & CROs
Faster, more predictable enrollment.
For Sites & Coordinators
Less manual chart review.
For Patients
More access to relevant trials.
Education Segments
Explore related healthcare AI
Clinical Documentation AI
Structured documentation feeds richer eligibility screening.
Drug Discovery Screening
Connect enrollment to the broader R&D pipeline.
Care Gap Identification
Reuse population screening for preventive care.
Patient Triage & Navigation
Route interested patients through intake and navigation.
Enterprise Automation
Automate coordinator and site operations.
Agent Ecosystem
Build agents for screening, outreach, and tracking.
Methodology
How Solnix builds for healthcare
01, Clinical Workflow Assessment
01, Clinical Workflow Assessment
We shadow real clinical and administrative workflows to design AI around how teams actually work.
02, HIPAA-Compliant Architecture
02, HIPAA-Compliant Architecture
All PHI is processed within HIPAA-compliant infrastructure under signed BAAs, with encryption and access logging.
03, EHR & System Integration
03, EHR & System Integration
We integrate with Epic, Cerner, and Athenahealth via FHIR R4 / HL7 so AI lives inside existing workflows.
04, Clinical Validation
04, Clinical Validation
Systems are validated against retrospective data with clinician oversight before production.
05, Continuous Monitoring
05, Continuous Monitoring
Post-deployment monitoring tracks performance and outcomes; drift triggers retraining.
FAQ
Questions from research and site leaders
Get Started
Enroll the Patients You're Already Missing.
Talk to a Solnix specialist about a trial-matching pilot scoped to your protocols, sites, and EHR.
Talk to a Clinical Research AI Specialist \u2192