FDA Adverse Event & Drug Safety Monitor

Monitor openFDA FAERS adverse-event signals by drug name. Compare current and prior reporting windows, serious/death shares, reaction terms and run-to-run changes for non-causal pharmacovigilance watchlists. Returns structured, machine-readable results for repeatable research.

Quick start

Start with this working example and replace the target values with your own:

{
  "drugNames": [
    "ASPIRIN"
  ],
  "windowDays": 365,
  "persistState": true
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• drugNames — Drug names: Medicinal product names to monitor in openFDA FAERS.
• windowDays — Analysis window days: Current and previous windows are compared using the same duration.
• openFdaApiKey — openFDA API key: Optional openFDA API key for higher request limits.
• persistState — Persist monitor state: Compare signal metrics with the previous Actor run.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include dataset.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.030000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

Turn openFDA FAERS into a recurring drug-safety watchlist workflow instead of another raw adverse-event exporter. For each drug name, the Actor compares equal-length current and previous reporting windows, measures total report volume, serious-report share, death-indicator share, top reaction composition, and persisted changes from the previous Actor run. It produces one aggregated signal-intelligence report per watched drug.

Why use this Actor

FAERS contains spontaneous safety reports that are valuable for surveillance but easy to misuse. Raw-row scraping creates volume without necessarily creating operational intelligence. This Actor emphasizes repeatable monitoring: compare report counts over time, surface serious/death indicators, summarize the most frequent reaction terms, and record whether the monitored profile changed since the previous run. The output is explicitly non-causal and is designed for pharmacovigilance operations, regulatory intelligence, research monitoring, and watchlist triage rather than diagnosis or clinical decision-making.

Key features

• Official openFDA drug adverse-event API as the source.
• Equal-length current versus previous window comparison.
• Current report count and percentage change versus the previous window.
• Serious-report and death-indicator counts and shares.
• Top reported MedDRA reaction terms from the current query.
• Persistent run-to-run state for report-count and reaction-composition changes.
• One aggregated report per drug instead of billing by every FAERS record.
• Optional openFDA API key for higher source request limits.
• Explicit causality disclaimer in every output item.

Input

Provide drugNames as medicinal-product names to monitor. windowDays sets both the current and previous comparison windows and is bounded between 90 and 1,825 days to reduce misleading ultra-short comparisons against reporting lag. openFdaApiKey is optional and secret. persistState enables comparison with the previous Actor run for the same drug name.

Output

Each dataset item contains totalReports, previousWindowReports, reportGrowthPct, seriousReports, seriousSharePct, deathReports, deathSharePct, topReactions, signalScore, and run-to-run change fields. signalScore is a deterministic operational triage heuristic. It is not a clinical risk score, incidence estimate, disproportionality statistic, or causal conclusion. The causalityDisclaimer field is always included so downstream agents and workflows preserve this limitation.

Example

Input: {"drugNames":["ASPIRIN"],"windowDays":365,"persistState":true}. The Actor queries the current 365-day FAERS window, the immediately preceding 365-day window, serious and death indicators, and current reaction counts. The first stateful run is INITIALIZED; later runs can become SIGNAL_CHANGED when the monitored count or top-reaction composition differs.

Use cases

Use it for pharmacovigilance watchlists, regulatory research, portfolio monitoring, safety-operations triage, recurring product surveillance, or research pipelines that need a compact machine-readable FAERS signal surface. Public Tasks can represent distinct watched drugs, while downstream automations can route unusually large changes to human review.

Pricing

Pay per event. One primary event is charged for each aggregated drug-safety signal report written to the default dataset. There is no separate QuanMatrix start fee. The pricing design intentionally values the analytical report rather than multiplying charges by thousands of raw FAERS rows.

Limitations

FAERS is a spontaneous reporting system. Reports do not establish that a drug caused an event and cannot be used directly to calculate incidence, prevalence, comparative safety, or clinical risk. Reporting is affected by duplication, stimulated reporting, publicity, product age, coding, incomplete information, and reporting lag. Drug-name matching follows the medicinal-product text searchable through openFDA and may not capture every synonym, formulation, or product alias. This Actor is operational signal intelligence, not medical advice and not a substitute for formal pharmacovigilance methods or expert review.

Gen2 decision intelligence

This Actor preserves its original analysis and adds a decision layer with baseline awareness, regression detection, confidence, GO/WARN/BLOCK executive output, and an optional economic-impact estimate. Economic estimates are produced only when the user supplies valuePerImpactUnitUsd; the result states the calculation basis instead of inventing monetary value.