AI Agentic Workflow for Real-World Evidence (RWE) Study Data Curation

Manually defining inclusion/exclusion criteria, mapping data elements, and building extraction logic for a new RWE study can consume 6-12 weeks of analyst and data engineering time. A custom agentic workflow ingests the study protocol, interprets criteria using LLMs, and auto-generates the data transformation logic and SQL queries needed to pull the initial cohort. This reduces the setup phase to days, allowing epidemiology teams to iterate on study design faster and begin analysis sooner.

Poor data quality—missing values, implausible ranges, inconsistent coding—is the primary cause of rework and delays in RWE analysis. This workflow embeds validation agents that check for completeness, conformance to expected value sets (like LOINC, SNOMED), and temporal logic (e.g., diagnosis before procedure) as data is extracted. Exceptions are routed to a human-in-the-loop queue with clear context, preventing corrupted data from entering the curated dataset and eliminating weeks of downstream cleaning.

Regulatory submissions (e.g., to FDA, EMA) require a complete, immutable audit trail of how the analysis dataset was created. Manual provenance logging is error-prone and incomplete. This workflow automatically generates a data dictionary, lineage map, and step-by-step execution log for every record, documenting source system, transformation logic, quality checks passed/failed, and any human overrides. This creates an inspection-ready package from day one, cutting weeks from audit preparation.

The manual labor for data curation—involving biostatisticians, data managers, and SAS/R programmers—is a major cost center with high variability. Automating the repetitive extraction, mapping, and validation tasks reduces direct labor costs by 50-70% for each study. More importantly, it standardizes the process, minimizing the risk of human error that could invalidate study results or trigger regulatory queries, protecting millions in R&D investment.

A one-off script for one study doesn't scale. This workflow is built as a reusable orchestration layer (using frameworks like LangGraph or Prefect) that connects to source systems (Epic, Cerner, claims adjudicators), applies study-specific logic, and outputs to analysis environments (R, Python, SAS). Once built for one therapeutic area, it can be adapted for new studies in weeks, not months, creating a compounding ROI and turning data curation from a bottleneck into a strategic capability.

Deployment is phased: a pilot on a historical study to validate output against a gold-standard manual curation, followed by production rollout with key controls. The architecture includes approval gates for final cohort lock, observability dashboards for pipeline health, and role-based access to the curation logic. This ensures the automated workflow operates under the same rigorous governance as manual processes, earning trust from medical, statistical, and regulatory stakeholders.

This agent connects to source systems (EHRs like Epic/Cerner, claims warehouses, registries) via secure APIs or SFTP, extracts raw data, and maps disparate codes (e.g., ICD-10, CPT, local lab codes) to a unified study ontology. It handles schema drift and missingness, logging all transformations for the provenance ledger. The bottleneck it automates is the manual, error-prone mapping of thousands of codes across sites, which typically consumes weeks of analyst time per study.

A rules-based agent that applies the study's inclusion/exclusion criteria (e.g., diagnosis within window, age, prior treatments, lab values) to the harmonized patient timeline. It executes complex temporal logic and outputs a candidate cohort with a patient-level audit trail showing exactly which criteria were met or failed. This replaces manual chart review or brittle SQL scripting, directly accelerating study setup and ensuring consistent, reproducible cohort definition—a core requirement for regulatory submission integrity.

This agent runs statistical and clinical plausibility checks on the curated dataset. It detects outliers (e.g., biologically impossible lab values), checks for temporal inconsistencies (medication after death date), and validates cross-field relationships. Flags are routed to a human-in-the-loop review queue with suggested actions. Automating this step prevents garbage-in/garbage-out outcomes and provides the quality control evidence needed for protocol and audit reviews, reducing the risk of study disqualification.

A critical explainability component. This agent automatically generates a comprehensive data dictionary with source-to-target mappings, derivation logic, and term definitions. It simultaneously builds an immutable provenance ledger that tracks every patient record from source through each transformation and criteria decision. This creates the defensible audit trail required by regulators (FDA, EMA) and internal compliance, turning a black-box extraction into a transparent, documentable process.

The central nervous system, typically built with a framework like LangGraph or Prefect. It sequences agent execution, manages state, handles retries, and enforces approval gates. It routes exceptions from the QA agent to a secure web queue where data managers can review flags, approve overrides, or request re-extraction. This controller ensures the workflow is robust, observable, and compliant, providing the operational control plane that allows automation to scale without losing governance.

The final agent packages the curated dataset, data dictionary, and provenance log into a structured, submission-ready format (e.g., CDISC SDTM/OMOP, or sponsor-specific schema). It can generate summary reports and quality metrics dashboards. This automates the last-mile manual effort of collating outputs and formatting for downstream analysis or regulatory submission, ensuring consistency and reducing the timeline from curation locked to analysis ready.