Automated Inclusion/Exclusion Criteria Development Workflow

Manual criteria development often creates unrealistic patient pools, leading to slow recruitment and costly protocol amendments. A custom AI workflow analyzes historical trial data, real-world evidence (RWE), and target product profiles to generate feasible, precise criteria. This directly shrinks the enrollment timeline, accelerating time-to-first-patient and overall study completion.

Up to 30% of Phase 3 protocols require amendments due to flawed eligibility criteria. An automated workflow embeds rule-based validation and simulation to stress-test criteria against synthetic and real-world patient cohorts before finalization. This pre-empts enrollment bottlenecks and safety exclusions, protecting the trial from costly, disruptive mid-study changes.

Vague or complex criteria force sites into lengthy manual feasibility assessments. A custom agentic system generates site-ready feasibility reports by modeling local population demographics against the proposed criteria. This gives operational teams data-driven confidence in site selection, reducing activation delays and improving resource allocation across the trial network.

Leveraging RWE for criteria design is manually intensive and slow. A custom workflow automates the ingestion and analysis of claims data, EHRs, and registries to model prevalence and comorbidity patterns. This creates a continuous feedback loop where criteria are grounded in actual patient populations, turning data assets into a direct competitive advantage in trial design.

Regulators and ethics committees demand clear justification for each inclusion/exclusion criterion. A manual process leaves this rationale fragmented. An automated workflow documents the provenance of every criterion—linking it to source guidelines, historical data, and simulation outcomes. This creates a defensible audit trail that streamlines regulatory submissions and inspections.

Medical writers and biostatisticians spend weeks drafting and revising criteria tables. Automating this foundational drafting frees 50-70% of their time for high-value strategic review, complex endpoint design, and adaptive planning. This transforms the protocol team from document producers into strategic designers, improving both job satisfaction and protocol quality.

This agent ingests the target product profile, historical trial protocols, and real-world evidence (RWE) databases to draft initial inclusion/exclusion criteria. It uses retrieval-augmented generation (RAG) to ground suggestions in past successful studies and current regulatory guidance (e.g., FDA, EMA). The output is a structured set of candidate criteria with source citations for medical review.

A core system that models the impact of draft criteria on patient availability. It connects to de-identified EHR and claims data lakes, runs cohort queries, and simulates enrollment rates across geographies. It flags criteria likely to cause recruitment bottlenecks (e.g., overly restrictive biomarkers) and suggests alternatives, providing a data-driven basis for optimization before protocol finalization.

A rule-based agent that scans criteria for logical contradictions, redundancy, and alignment with protocol objectives. It checks for conflicts (e.g., excluding a medication required by the study drug) and ensures criteria syntax matches the structured format required downstream for EDC and patient screening systems. Exceptions are routed to a review queue for medical monitors.

This agent continuously maps draft criteria against therapeutic-area-specific guidelines from ICH, FDA, and EMA. It flags criteria that deviate from recent guidance (e.g., pediatric age ranges, renal/hepatic function cut-offs) and suggests compliant alternatives. It maintains an audit trail of all guidance references integrated into the final criteria set for submission readiness.

The workflow's governance layer. It manages the review queue, routes criteria sets to assigned medical monitors and biostatisticians via integrated systems (e.g., Veeva Vault), tracks changes, and consolidates feedback. It only advances criteria to the final protocol document once all required electronic sign-offs are captured, ensuring controlled iteration.

This component translates finalized, structured criteria into specifications for connected clinical systems. It auto-generates code for EDC (e.g., Medidata Rave) edit checks, builds patient pre-screening logic for recruitment platforms, and updates trial registry entries (ClinicalTrials.gov). This eliminates manual, error-prone re-entry and ensures operational consistency from day one.