Multi-Agent Automation of Running Periodic Safety Update Report (PSUR) Data Pulls

Manual PSUR data pulls require safety associates to build and run dozens of complex queries across Oracle Argus, Veeva Safety, and clinical data warehouses, followed by days of spreadsheet reconciliation. A multi-agent workflow automates this end-to-end: a Query Orchestrator Agent executes the predefined query set per product and review period, while a Data Aggregation Agent normalizes results, flags discrepancies, and prepares a unified dataset. This directly converts a 2-3 week manual task into a 48-hour automated process, reclaiming hundreds of FTE hours per reporting cycle for medical review and signal assessment instead of data wrangling.

Manual processes introduce risk of missed queries, incorrect date ranges, or data transposition errors, jeopardizing report integrity and submission timelines. An automated workflow embeds business rule validation at each step—checking review period alignment, confirming query completeness, and validating against previous report baselines. A Deadline Monitoring Agent tracks the end-to-end timeline, triggering escalations if any component lags. This creates a defensible, audit-trailed process that ensures data accuracy and on-time delivery, directly protecting the organization from compliance findings and potential regulatory penalties.

By automating the repetitive, transactional work of data compilation, your pharmacovigilance team shifts from being a reporting cost center to a strategic insights function. Freed medical reviewers can focus on interpreting the aggregated data, investigating flagged discrepancies, and assessing emerging safety profiles. This operational leverage allows the same team to support more products or conduct deeper longitudinal analyses without increasing headcount, directly improving the quality and strategic impact of the final PSUR/PBRER submitted to health authorities.

Manual PSUR processes don't scale; adding a new product or changing a reporting frequency creates linear increases in labor. A custom automated workflow, built with orchestration frameworks like LangGraph or Prefect, codifies the entire data pull logic into a reusable, parameterized pipeline. The Product & Period Configuration Agent manages the schedule and inputs, allowing the system to effortlessly scale to additional products, subsidiaries, or licensing partners. This transforms PSUR preparation from a recurring project into a managed service, reducing marginal cost and operational volatility.

Spreadsheet-based reconciliation obscures data lineage. An automated architecture ensures full traceability: each data point in the final PSUR tables can be traced back to the source system query, execution timestamp, and any transformation rules applied. A Discrepancy Flagging Agent logs all data mismatches between sources for explicit review, turning a hidden problem into a managed exception. This creates a robust quality foundation, drastically reducing preparation time for regulatory inspections and internal audits by providing instant, queryable access to the complete data provenance.

The greatest efficiency gain is realized when automation spans the entire PSUR lifecycle. This workflow doesn't just produce raw data; it formats output into the precise tables and line listings required by the report template and triggers the next phase. An Output Preparation Agent structures the data for direct ingestion into document automation tools (e.g., Veeva Vault RIM) or triggers a downstream Report Drafting Agent. This end-to-end orchestration closes the loop, turning fragmented manual handoffs into a continuous, automated pipeline from data pull to regulatory submission draft.

The central coordinator that triggers the workflow based on the PSUR calendar. It ingests the product list and review period, decomposes the master data request into discrete queries for each source system (e.g., Oracle Argus, Veeva Safety, clinical data warehouses), and dispatches tasks to the query execution agents. It manages state, handles retries, and initiates the data aggregation phase.

Specialized agents that connect to and query specific safety databases using secure, compliant protocols. Each agent understands the schema and business rules of its assigned system (e.g., crafting ICSR line listings from Argus, retrieving cumulative patient exposure from a clinical repository). They execute parameterized queries, handle pagination, and return structured data extracts to the orchestrator.

An agent that receives data extracts from multiple sources and performs intelligent reconciliation. It aligns cases by unique identifiers, flags mismatches in counts or critical fields (e.g., serious vs. non-serious totals), and generates a discrepancy report. This automates the most tedious and error-prone part of the manual PSUR data pull process, surfacing only genuine issues for human review.

A rule-based agent that performs automated checks on the aggregated dataset before handoff. It validates data completeness against the PSUR requirements, checks for outliers in temporal or geographic distributions, and ensures numerical consistency across summary tables. Failed checks create tickets in a review queue, providing a defensible pre-submission QC layer.

The final agent in the pipeline that formats the validated, aggregated data into the structured inputs required by the downstream PSUR authoring workflow. It generates formatted line listings, summary tabulations, and data files, then packages them with metadata (data-cut date, product list, agent execution log) and deposits them into a designated secure repository (e.g., Veeva Vault RIM) for the medical writing team.

The production infrastructure, typically built on a framework like LangGraph, that defines the agent interaction logic, manages execution state, and provides full observability. This layer includes audit logging for compliance, dashboards showing query status and data volumes, and alerting for failed agents or data pipeline delays. It is the central nervous system that makes the multi-agent workflow reliable and monitorable.

Agents are configured with direct, secure API or ODBC connections to primary safety systems like Oracle Argus Safety or Veeva Safety. Each agent authenticates using service accounts, executes parameterized SQL or stored procedures for the defined review period and product list, and handles pagination for large datasets. This replaces manual login and ad-hoc query building across multiple database instances.

The workflow is governed by a central orchestrator, typically built on LangGraph or a similar framework, which manages the state machine. It triggers agents based on the PSUR calendar, passes context (product, date range), handles retries on query failure, and sequences the handoff to aggregation. This layer provides the audit trail for every execution cycle and defines the approval gates before data is passed downstream.

Specialized validation agents run concurrently with data pulls. They perform cross-database reconciliation, flagging mismatches in case counts or key fields (e.g., seriousness, outcome) between the core safety DB and secondary sources like literature databases or partner data feeds. Discrepancies are routed to a human-in-the-loop queue in tools like Jira or ServiceNow for investigation before aggregation proceeds.

Once validated, raw query results are streamed to a processing engine that transforms and aggregates data into the precise tabular formats (line listings, summary tabulations) required for the PSUR. This engine applies business rules, manages patient deduplication across data sources, and outputs structured JSON/CSV files ready for the report drafting workflow or direct ingestion into a document assembly platform like Veeva Vault RIM.

A custom dashboard (built with Grafana or similar) provides real-time visibility into the workflow's operational health. It displays metrics: query execution times, data volumes pulled, discrepancy counts, and agent status. Alerts are configured for failed jobs or data anomalies, ensuring the PV operations team can intervene before a reporting deadline is impacted. Logs feed into centralized systems like Datadog or Splunk for compliance auditing.

The final, validated dataset is not an endpoint. The workflow includes a secure handoff to the downstream regulatory submission system. This is often an automated push to a Veeva Vault Safety or CSC REGISTRY module, or the generation of a pre-formatted data packet that triggers the next phase of the PSUR authoring workflow. This integration closes the loop from data extraction to report assembly, preventing manual re-upload.