Automated Clinical Trial Feasibility Assessment Workflow

Manual site identification and qualification involve weeks of outreach, spreadsheet analysis, and subjective scoring. A custom workflow automates this by ingesting investigator databases, historical trial performance, and therapeutic area expertise to generate ranked, qualification-ready site reports. This compresses a 10-12 week process into 2-4 weeks, directly accelerating study start-up and first-patient-in timelines.

Up to 30% of costly protocol amendments stem from flawed feasibility assumptions. An automated assessment workflow models patient population availability against inclusion/exclusion criteria using real-world data (RWD) and historical trial benchmarks before protocol finalization. This surfaces recruitment bottlenecks and operational infeasibility early, allowing for pre-emptive design adjustments that protect the budget and timeline.

Sponsor and CRO forecasts are often based on optimistic site estimates. A custom AI agent synthesizes country-level epidemiology data, competing trial landscape, and site-level recruitment history to generate probabilistic enrollment curves. This data-driven model replaces guesswork, enabling more reliable resource planning, vendor contracting, and interim analysis scheduling.

The manual process of collating country regulations, import/export logistics, and standard-of-care data is labor-intensive and repetitive. An automated workflow uses specialized agents to query and summarize regulatory databases (e.g., FDA, EMA, country-specific portals) and logistics requirements, generating consolidated operational risk profiles. This eliminates hundreds of hours of manual research per trial.

When pipeline priorities shift, sponsors need to quickly reassess a protocol's viability under new constraints (e.g., different countries, faster timelines). A custom orchestration layer allows users to adjust parameters (target countries, screen failure rates) and instantly regenerate feasibility reports. This creates an agile, strategic planning tool that improves portfolio-level decision speed and capital allocation.

Manual assessments leave a fragmented, poorly documented decision trail. A custom workflow automatically documents every data source, assumption, and modeling choice, generating a definitive audit trail for regulatory or internal quality review. This strengthens the sponsor's position during regulatory interactions and provides a reusable knowledge base for future trials in the same therapeutic area.

This initial agent parses the draft protocol to extract and normalize all feasibility-critical elements: inclusion/exclusion logic, visit schedules, procedure complexity, and endpoint definitions. It maps these to structured queries for downstream population and site analysis, transforming narrative text into executable data filters. Without this step, manual interpretation creates inconsistent assumptions that derail the entire assessment.

An agent that connects to de-identified EHR/claims databases (e.g., TriNetX, Flatiron) and geographic data lakes. It executes the mapped criteria to model the available patient pool by region, calculating incidence rates and forecasting realistic screen-failure ratios. This replaces manual epidemiologist queries, providing a probabilistic enrollment curve that directly informs timeline and site-count decisions.

This component integrates with internal CRO databases and external sources (e.g., Citeline) to score and rank potential sites. It evaluates historical performance metrics (enrollment rates, data quality, audit findings), current workload, and specific capabilities (e.g., biomarker testing, imaging equipment) required by the protocol. The output is a prioritized, evidence-based site shortlist.

A rules-based and LLM-driven agent that models the operational burden and cost implications. It analyzes protocol procedures to estimate site effort, patient burden, monitoring intensity, and supply chain logistics. It generates scenario-based budget forecasts and flags high-risk elements (e.g., complex blinding, temperature-sensitive IP) that could jeopardize execution.

This agent cross-references protocol requirements against country-specific regulatory databases (e.g., FDA, EMA, local ethics guidelines) and import/export regulations. It identifies potential approval roadblocks, standard-of-care conflicts, and logistical hurdles (e.g., genetic sample export bans) by country, automating a layer of review typically requiring deep local expertise.

The final component synthesizes outputs from all upstream agents into a consolidated feasibility report. It weighs conflicting signals, applies sponsor-specific risk tolerance, and generates a clear go/no-go/mitigate recommendation with supporting rationale. This report, with embedded interactive dashboards, is routed via approval workflows in systems like Veeva Vault for stakeholder sign-off.

Drives the build to eliminate the 6-12 week manual feasibility bottleneck that delays site activation and first-patient-in. Benefits from automated, evidence-based site identification and qualification reports, reducing subjective selection and improving activation timelines by 30-50%. The workflow integrates with internal investigator databases and external profiling tools (e.g., Medidata Site Analytics) to generate actionable shortlists.

Drives the requirement for population availability modeling to validate protocol inclusion/exclusion criteria before final sign-off. Benefits from immediate simulation of recruitment impact across geographies, using real-world data (RWD) sources like TriNetX or Flatiron. This reduces costly Protocol Amendments by identifying infeasible criteria early, directly protecting the trial's scientific and operational integrity.

Drives the architecture for the data ingestion and modeling agents. Benefits from a scalable pipeline that automates the fusion of disparate data (EHR aggregates, claims, public health registries, country-level regulatory databases) into a unified feasibility model. This shifts their role from manual data wrangling to overseeing model validation and interpreting complex scenario outputs for stakeholders.

Drives the ROI case by linking feasibility accuracy to cost avoidance. Benefits from automated generation of country- and site-level budget implications during the assessment. The workflow models operational complexity scores that translate into projected monitoring burden and vendor costs, enabling more accurate go/no-go decisions and protecting the trial's financial model from the outset.

Drives the governance requirements for audit trails and data provenance. Benefits from an automated workflow that documents every data source, assumption, and modeling step used in the feasibility assessment. This creates a defensible evidence package for health authority questions on site selection rationale, strengthening the sponsor's regulatory posture and reducing submission risk.

Drives the build execution, selecting orchestration frameworks (LangGraph, Prefect) and integrating agents with clinical systems (Veeva SiteVault, Oracle Siebel CTMS). Benefits by creating a reusable, API-first feasibility microservice that can be plugged into multiple therapeutic area workflows, reducing future build costs and establishing a core component of the sponsor's clinical development platform.