Real-World Evidence (RWE) Powered Trial Feasibility Automation

This workflow automates the high-stakes bottleneck of trial feasibility assessment. By orchestrating queries across claims, EHR, and registry data, it replaces weeks of manual analyst work with hours of automated analysis. The business value is direct: faster, more accurate go/no-go decisions that prevent costly protocol amendments and enrollment failures. Implementation requires agents for data querying, statistical analysis, and narrative synthesis, integrated with platforms like TriNetX, Flatiron, or custom RWE lakes, all governed by strict data use agreement (DUA) compliance checks.

The architecture is built for production. The orchestrator, potentially using LangGraph, manages parallel queries, handles API failures, and enforces DUA boundaries. The statistical engine calculates incidence, prevalence, and comorbidity overlaps. Outputs are not just dashboards but structured reports with confidence intervals and gap analyses, ready for stakeholder review. Critical controls include human-in-the-loop approval gates for the final assessment, continuous data quality monitoring, and a full audit trail to defend the analysis to regulators and internal governance boards.

This workflow automates the critical bottleneck of manual RWE analysis for trial feasibility, which typically consumes weeks of biostatistician and epidemiologist time. By orchestrating agents to query federated claims and EHR data, estimate disease prevalence, and model standard-of-care patterns, it delivers a quantified feasibility report in days. The operational upside comes from faster, more accurate go/no-go decisions, de-risking multi-million-dollar trial investments and accelerating pipeline planning. Implementation requires integrating with RWE platforms like TriNetX or Flatiron and embedding governance for data use agreement compliance.

The architecture is built on an agentic orchestrator, typically using LangGraph or a custom state machine, to manage the sequential and parallel tasks. It triggers specialized agents for query translation, data validation, statistical modeling, and narrative generation. Controls are mandatory: every data pull is checked against DUAs, all model outputs include confidence intervals, and a medical review gate validates the final report before distribution to stakeholders. Implementation integrates with Veeva Vault or similar systems for audit trails and requires a rollout plan that starts with a single therapeutic area to validate data quality and exception handling before scaling.

Phase 1 establishes a foundational data pipeline, ingesting and harmonizing RWE from claims and EHR systems like Epic or Cerner into a query-ready data lake. This initial build focuses on core data quality agents for deduplication and mapping, delivering a validated, structured dataset. The goal is to prove data accessibility and quality, creating the trusted source for subsequent analytic automation without over-investing in complex logic upfront.

Phase 2 layers on the analytic orchestration, deploying agents that decompose protocol criteria into executable database queries and calculate key feasibility metrics. This phase introduces the core business logic and requires tight integration with governance controls, including automated bias checks and explanation layers for each estimate. The final phase focuses on production integration, automating report generation, feeding insights into CTMS platforms like Veeva, and establishing continuous monitoring to ensure the model's outputs remain clinically and operationally valid as underlying data evolves.

Deploying an RWE feasibility workflow requires a governance layer that enforces data provenance, auditability, and decision accountability. The architecture must integrate with existing data warehouses (e.g., TriNetX, Flatiron) and enforce strict query logging against Data Use Agreements. Controls include pre-execution protocol validation, bias checks on analytic models, and mandatory human sign-off on final feasibility reports before stakeholder distribution. This governance reduces regulatory risk while ensuring the automated insights are defensible and actionable for go/no-go decisions.

A phased rollout mitigates implementation risk. Start with a single therapeutic area and a limited set of trusted RWE sources, integrating the workflow's output into existing feasibility review meetings. Measure success via reduced manual analyst hours and improved forecast accuracy against actual enrollment. Subsequent phases expand data sources, automate report integration into Veeva Vault or SharePoint, and introduce more complex simulation agents. This controlled approach builds operational confidence and demonstrates clear ROI before enterprise-scale deployment.

This workflow automates the historically manual, spreadsheet-driven bottleneck of trial feasibility analysis. By orchestrating queries across claims, EHR, and registry data, it replaces weeks of analyst effort with hours, directly de-risking protocol design and site selection. The operational upside comes from faster, more accurate go/no-go decisions, preventing costly amendments and enrollment failures. Implementation requires agents for data querying, a pipeline for statistical analysis, and integration with RWE platforms like TriNetX or Flatiron, governed by strict data use agreements.

Architecturally, the orchestrator manages API calls to source systems, handles data normalization, and routes outputs through validation and statistical agents. Controls are essential: all queries must be pre-approved against DUAs, results undergo plausibility checks, and human review gates are required before final reporting. Implementation is phased, starting with a single data source and protocol type, then scaling to multi-source analysis. The final output is an auditable, data-backed feasibility report integrated into clinical planning systems like Veeva Vault, enabling proactive portfolio strategy.