Automated Feasibility Report Generation for Stakeholders

Manual feasibility studies require analysts to manually query disparate systems, compile spreadsheets, and draft narratives—a process that typically consumes 4-6 weeks per protocol. A custom automation workflow orchestrates data ingestion, analysis, and narrative generation, delivering a comprehensive, formatted report to stakeholders in under two days. This acceleration directly translates to faster trial design decisions, earlier site identification, and a significant reduction in pre-study timeline risk.

The operational cost of a manual feasibility report is driven by high-value analyst time, consultant fees, and opportunity cost from delayed studies. Automating data aggregation, prevalence analysis, and initial narrative drafting reduces the manual effort required from ~200 analyst-hours to ~20 review-hours. This 90% reduction in direct labor per assessment creates a predictable, scalable operating model for global development teams, protecting R&D budgets and improving portfolio planning economics.

Manual reports are often limited by sample size, outdated data, or analyst bias. An automated workflow systematically queries real-world evidence (RWE) datasets, trial registries, and internal site performance databases to generate insights grounded in larger, more current populations. By applying consistent analytic logic and simulating protocol amendments, it provides sponsors with higher-confidence forecasts for enrollment rates, site feasibility, and competitive landscape—reducing costly protocol amendments post-launch.

The production workflow is built on a LangGraph or CrewAI orchestration layer that coordinates specialized agents: a Data Ingestion Agent pulls from Flatiron, TriNetX, and internal CTMS; an Analytic Agent runs prevalence queries and site performance models; a Narrative Assembly Agent drafts findings using RAG over prior reports and protocol documents. Outputs route through a human-in-the-loop review interface in platforms like Veeva Vault or SharePoint, where medical and operational leads can approve, edit, or request revisions before final distribution.

Deployment follows a pilot-on, scale-out pattern. Phase 1 automates report generation for a single therapeutic area, integrating with one RWE source and the CTMS to prove the data pipeline and review workflow. Phase 2 expands agent capabilities to include competitive intelligence scraping and protocol complexity scoring. Critical to success is embedding governance controls from day one: audit trails for all data queries, version control for generated narratives, and clear escalation paths for discrepant data, ensuring the system is both efficient and inspection-ready.

Automation introduces new risks around data drift and model decay. The operational architecture includes continuous monitoring agents that track the freshness of source data, the performance of analytic queries against known benchmarks, and the quality of generated text. A quarterly validation sprint re-runs a sample of historical feasibility assessments through the automated system and compares outputs to the original human-generated reports, ensuring the workflow remains accurate, compliant, and aligned with stakeholder needs.

Specialized agents orchestrate API calls and data pulls from fragmented sources: RWE platforms (e.g., TriNetX, Flatiron), internal site performance databases, competitive intelligence feeds, and protocol repositories. They map, deduplicate, and validate incoming data, transforming it into a structured, query-ready format. This eliminates the manual, multi-day effort of data collection and normalization that typically bottlenecks report creation.

A core orchestration layer (e.g., LangGraph) sequences analytic agents that calculate key metrics: prevalence estimates, site feasibility scores, competitive landscape analysis, and enrollment risk projections. A separate LLM-powered narrative agent synthesizes these outputs into coherent, draft report sections (Executive Summary, Methodology, Findings), adhering to pre-defined templates and brand voice. This replaces the labor-intensive manual analysis and writing performed by clinical operations specialists.

The workflow is not fully autonomous. It incorporates mandatory review checkpoints. After draft generation, the report is routed via Slack or MS Teams to designated Medical, Feasibility, and Regulatory stakeholders. A review agent provides a diff view of changes and tracks feedback. Final version control and archival are automated into document management systems (e.g., Veeva Vault). This ensures scientific accuracy, regulatory prudence, and maintains necessary human oversight.

Upon final approval, distribution agents execute the dissemination plan. They personalize and send PDF reports via email to internal stakeholders (Clinical, Biometrics, Finance) and external partners (CROs). Simultaneously, key structured data points—like projected enrollment rates and top recommended sites—are pushed via API into downstream systems: the Clinical Trial Management System (CTMS) for site activation planning and the portfolio management tool for pipeline forecasting.

The entire workflow is instrumented for observability. Monitoring agents track pipeline health, data freshness, agent performance, and user engagement with report outputs. A feedback loop captures stakeholder ratings and comments on report usefulness, which is used to retrain narrative generation models and refine analytic queries. This operational layer turns a one-time automation project into a continuously improving asset that adapts to changing data sources and stakeholder needs.

A practical build starts with a pilot on a single therapy area and two key data sources (e.g., one RWE platform, internal site data). The architecture is deployed as a containerized microservice on cloud infrastructure (AWS/Azure/GCP) for scalability. Phase 1 focuses on core data ingestion and a simple PDF report. Phases 2+ add complex analytics, CTMS integration, and the stakeholder review portal. This phased approach de-risks implementation and demonstrates ROI within the first quarter.

Primary Benefit: Receives a comprehensive, data-driven feasibility report in hours, not weeks, enabling faster go/no-go decisions on trial protocols and more confident site selection. Management Role: Defines the report structure, approves the data sources (e.g., RWE platforms, internal site databases), and reviews the final narrative for strategic alignment. Manages the approval workflow for report distribution.

Primary Benefit: Eliminates the manual task of querying and aggregating disparate datasets (claims, EHR, competitive intelligence). The automated pipeline delivers clean, structured data ready for analysis. Management Role: Configures and validates the statistical models and algorithms used for prevalence estimation, incidence rates, and standard-of-care analysis within the report. Governs the logic for data quality checks.

Primary Benefit: Gains access to a consistently formatted report that synthesizes therapeutic area landscape, competitor trial activity, and patient population estimates, supporting stronger protocol design. Management Role: Provides the medical context, reviews the narrative for scientific accuracy, and approves the inclusion of specific competitive intelligence sources. Acts as a final reviewer before stakeholder release.

Primary Benefit: Transforms a recurring, high-effort manual report into a governed, scalable data product with clear SLAs, reducing fire-drill requests. Management Role: Architects and maintains the automation workflow. Manages API integrations with RWE platforms (e.g., TriNetX, Flatiron), internal CTMS, and competitive databases. Implements security, logging, and the observability layer for pipeline health.

Primary Benefit: Receives a pre-analyzed shortlist of high-potential sites with estimated enrollment rates and investigator profiles, replacing weeks of manual research. Management Role: Provides feedback on site profiling criteria, validates the automated rankings, and uses the report to prioritize outreach. Manages the handoff from the automated report to the site activation workflow.

Primary Benefit: Ensures all data used in the automated report is sourced and processed under compliant frameworks (e.g., data use agreements, GDPR), with a full audit trail. Management Role: Defines and monitors the compliance rules embedded in the workflow. Reviews automated audit logs and approves the report generation process for use in regulated decision-making.