The Challenge
Clinical trials are the backbone of drug development. They are also one of the most expensive and frequently delayed phases in the pharmaceutical pipeline. Patient recruitment
sits at the center of this problem. Sponsors spend substantial time and money trying to identify eligible subjects, and yet enrollment shortfalls remain the leading cause of trial
delays and failures.
For healthcare providers managing millions of cancer patients across distributed systems, the scale of the problem compounds quickly. Matching a patient to a specific clinical trial
requires interpreting highly specific eligibility criteria, integrating data across multiple formats and source systems, and doing so at speed and
at scale.
$
0
B+
Annual clinical
trials market
0
%
Of trials delayed by
enrollment gaps
0
%
Of sites miss
recruitment target
0
%
Dev time reduction
with agentic AI
Why Traditional Recruitment Methods Fall Short
Clinical trials are the backbone of drug development. They are also one of the most expensive and frequently delayed phases in the pharmaceutical pipeline. Patient recruitment
sits at the center of this problem. Sponsors spend substantial time and money trying to identify eligible subjects, and yet enrollment shortfalls remain the leading cause of trial
delays and failures.
Strict eligibility criteria
Inclusion and exclusion criteria are highly specific and
written in complex clinical language. A single trial
protocol may contain dozens of interlocking conditions
joined by logical connectors, requiring precise
interpretation.
Fragmented patient data
Patient information is dispersed across EHRs, lab
systems, imaging platforms, clinical notes, and care plan
databases. No single system holds a complete picture of
a patient’s eligibility.
Recruitment process inefficiencies
Manual review processes are time-intensive,
inconsistent across sites, and difficult to scale. Sites
frequently operate without a structured workflow for
eligibility screening.
Awareness gaps
Both patients and providers frequently lack visibility into
active clinical trials relevant to a patient’s condition. Even
when trials exist, the path from awareness to enrollment
is rarely straightforward.
Logistical barriers
Trial location requirements, scheduling constraints, and
patient availability create friction that further depresses
enrollment rates
Trust and compliance
Recruitment efforts must navigate patient trust concerns
and regulatory compliance requirements, adding another
layer of complexity to an already fragmented process.
A Representative
Example from Practice
Study: Phase II Advanced Non-Small Cell Lung Cancer
Eligibility criterion excerpt “Participants with non-squamous or
squamous histology NSCLC with stage IIIB or stage IIIC disease
who are not candidates for surgical resection or definitive
chemoradiation per investigator assessment, or stage IV
(metastatic) disease who received no prior systemic treatment
for recurrent or metastatic NSCLC.”
Matching a real-world patient population to criteria of this complexity requires structured parsing,
entity resolution, and clinical reasoning. Manual methods produce inconsistent results. Rule-based
systems break down when criteria involve conditional logic and multi-concept entities.
A Unified Analytics Framework for Commercial Pharma Teams
Intuceo designed and built an agentic AI system specifically for clinical trial patient matching. The system uses a LangGraph-orchestrated multi-agent workflow to automate the full pipeline:
from parsing unstructured study protocols, to resolving clinical entities against medical terminologies, to executing a hybrid matching engine that combines structured query logic with large language model (LLM) reasoning.
The architecture is built to handle the real-world messiness of clinical data. It does not require clean, pre-formatted inputs. Instead, it processes raw ClinicalTrials.gov data and unstructured EHR records to surface eligible patients with precision and auditability
System Architecture Overview
- Fetches unstructured study data from ClinicalTrials.gov API
- Applies Natural Language Processing for intent recognition
- Converts raw trial text into structured JSON with attributes, entities, and values
- Validates and structures queries before downstream processing
- Interfaces with Azure MCP for secure patient data access
- Implements Secure Data Access Protocol (SDAP)
- Aggregates patient records from multiple source systems
- Returns normalized patient data for matching
- Executes hybrid SQL + LLM matching logic
- Applies AI-powered eligibility assessment against parsed criteria
- Generates confidence scores for each patient-trial pairing
- Surfaces matched patients with supporting reasoning
- Formats output for clinical researcher consumption
- Generates longitudinal patient summaries
- Produces ranked, prioritized match results
- Delivers structured reports to the Matching Results Dashboard
Entity Resolution: Bridging Clinical Language and Structured Data
One of the most technically demanding components of the solution is entity resolution. Clinical trial protocols use natural language to describe conditions, procedures, and patient characteristics. EHR systems store equivalent information as coded data using vocabularies like SNOMED CT. Aligning these two representations is essential for accurate matching and historically requires extensive manual
curation.
Intuceo solved this by deploying a GPT-based Agent that maps extracted entities to SNOMED CT codes via direct API interaction. This eliminates the brittleness of rule-based terminology mapping and enables the system to handle novel entity combinations without requiring manual coding updates. The result is a dynamic, self-adapting entity resolution layer that bridges trial criteria and EHR data at scale.
AI Matching Engine: Structured and Semantic Matching Combined
The core matching engine runs two parallel tracks. Structured matching executes rule-based SQL query templates against demographic data and quantifiable lab values, where precision and determinism are essential. Semantic matching applies vector similarity search and LLM-driven analysis across clinical notes and unstructured patient records, where context and nuance matter.
The engine does not simply return a yes or no answer. For each candidate patient, it generates a longitudinal summary, explains the basis for the match, and provides a confidence score. Clinical researchers reviewing results can understand exactly why a patient was surfaced, which supports both
operational trust and regulatory auditability.
Technical Stack
The solution is built on a modern, cloud-native architecture designed for regulated healthcare environments. Each component was selected to support compliance requirements, data security, and the orchestration demands of a multi-agent agentic workflow.
| Component | Technology / Description |
|---|---|
| Orchestration Framework | LangGraph - manages agent graph execution, state transitions, and inter-agent communication across the multi-agent workflow |
| Programming Language | Python - primary language for agent logic, data processing pipelines, and API integrations |
| Language Model Layer | LLM (Commercial / Local) - supports both cloud-hosted commercial models and locally deployed models for air-gapped or compliance-restricted environments |
| Database | Microsoft SQL Server (MSSQL) - structured patient data storage, SQL query template execution for deterministic matching |
| AI & Cognitive Services | Azure OpenAI - GPT models for entity resolution, semantic matching, and longitudinal patient summary generation |
| Cloud Data Layer | Azure MCP (Model Context Protocol) - secure patient record access, multi-source data aggregation, HIPAA-compliant data handling |
| Terminology & Coding | SNOMED CT API - real-time entity-to-code resolution for clinical terminology standardization |
| Trial Data Source | ClinicalTrials.gov API - structured and unstructured trial protocol ingestion for automatic criteria parsing |
Orchestration Framework
Technology / Description
LangGraph - manages agent graph execution, state transitions, and inter-agent communication across the multi-agent workflow.
Programming Language
Technology / Description
Python - primary language for agent logic, data processing pipelines, and API integrations.
Language Model Layer
Technology / Description
LLM (Commercial / Local) - supports both cloud-hosted commercial models and locally deployed models for air-gapped or compliance-restricted environments.
Database
Technology / Description
Microsoft SQL Server (MSSQL) - structured patient data storage, SQL query template execution for deterministic matching.
AI & Cognitive Services
Technology / Description
Azure OpenAI - GPT models for entity resolution, semantic matching, and longitudinal patient summary generation.
Cloud Data Layer
Technology / Description
Azure MCP (Model Context Protocol) - secure patient record access, multi-source data aggregation, HIPAA-compliant data handling.
Terminology & Coding
Technology / Description
SNOMED CT API - real-time entity-to-code resolution for clinical terminology standardization.
Trial Data Source
Technology / Description
ClinicalTrials.gov API - structured and unstructured trial protocol ingestion for automatic criteria parsing.
Security and Compliance
The Azure MCP Data Layer is architected to meet the requirements of healthcare data environments. Patient record access enforces role-based access controls, all data in transit and at rest is encrypted, and audit logging captures every data retrieval and processing event for compliance review. The system is designed to operate within HIPAA-governed data environments without requiring changes to
existing security infrastructure.
Benefits and Outcomes
- 50% reduction in application development time. Reusable agentic components and LangGraph's structured orchestration layer significantly accelerate the build cycle compared to custom, point-solution development approaches.
- Higher patient matching accuracy against inclusion and exclusion criteria. The hybrid engine resolves entity terminology gaps and handles multi-concept eligibility conditions that rule-based systems routinely fail to process correctly
- Matching rationale generated for every result. Clinical researchers receive a confidence score and a plain-language explanation for each candidate, supporting faster review decisions and auditability in regulated environments.
Ready to accelerate your clinical trial enrollment?
Intuceo’s PhD-led engineering team applies the same rigorous, data-architecture-first approach to your clinical operations
challenges.
Ready to accelerate your clinical trial enrollment?
Intuceo’s PhD-led engineering team applies the same rigorous, data-architecture-first approach to your clinical operations
challenges.
