Azra AI has launched an Agentic AI Clinical Research Platform built on its Real-Time Care Orchestration Engine, aiming to streamline clinical trial workflows by integrating real-time patient data from electronic health records (EHRs) into a Unified Patient Intelligence Layer. The platform is designed to bridge gaps between health systems and pharmaceutical companies, enabling faster trial feasibility assessments, automated patient pre-screening, and proactive enrollment. It is already deployed across hundreds of U.S. health systems, including several in the top 10.
Overview
The platform leverages Azra AI’s existing infrastructure for enterprise-wide clinical intelligence and care orchestration. By ingesting pathology and radiology reports the moment they enter the EHR, the system applies real-time AI models to detect and characterize conditions in oncology, cardiology, and neurology. This immediate processing allows the identification of trial-eligible patients seven days earlier than traditional methods.
A key component is the Unified Patient Intelligence Layer, which harmonizes fragmented EHR data into a single source of truth. This layer supports multiple functionalities across the clinical trial lifecycle, from feasibility to enrollment and reporting.
What it does
Accelerated Feasibility via Conversational UI: Researchers can interact with the platform using natural language through a Conversational User Interface (CUI). Complex clinical protocols can be loaded to assess site viability in seconds, eliminating the need for manual database queries.
Agentic Pre-Screening and Enrollment: Autonomous AI agents continuously scan structured and unstructured clinical data to automate pre-screening. These agents identify potential trial matches and ensure eligible patients are flagged at the point of care.
Proactive Trajectory Tracking & Progression Risk: The platform monitors disease progression and predicts future clinical milestones. This enables提前 identification of patients who may soon meet trial criteria, allowing sites to plan enrollment ahead of narrowing treatment windows.
Azra Clinical Research Network: Health systems can opt into this network to connect directly with pharmaceutical studies tailored to their patient populations. The network aims to turn hospitals into active research hubs while giving drug developers access to pre-qualified trial sites.
Automated Portfolio Reporting and ROI Tracking: The platform generates real-time analytics across an entire health network, tracking the enrollment funnel from pre-screening to final enrollment. This allows research leaders and pharma partners to measure ROI and identify bottlenecks.
Full Traceability and Transparency: Every AI-generated patient match includes traceability to the original, de-identified EMR data. This ensures audit readiness, verification capability, and data integrity.
Tradeoffs
The platform’s reliance on real-time EHR integration requires deep technical interoperability with existing health IT systems. While Azra AI is already installed in hundreds of health systems, broader adoption may depend on EHR compatibility and data governance policies. Additionally, the use of autonomous AI agents in clinical decision support raises expectations for explainability and regulatory alignment, though the platform supports transparency through source data traceability.
When to use it
The platform is particularly relevant for health systems running complex, biomarker-driven trials—especially in oncology—where eligibility criteria are often buried in unstructured notes. It is also suited for pharmaceutical companies seeking faster site activation and higher-fidelity patient matching. Early customer adoption and a strategic partnership indicate market validation.
Azra AI’s platform represents a shift toward integrating research directly into clinical workflows, reducing administrative burden and improving coordination. By operationalizing real-time, multimodal data, it aims to make clinical trial participation more accessible and efficient.