FDA MAUDE Dataset
About the Dataset
The FDA MAUDE (Manufacturer and User Facility Device Experience) Dataset is a comprehensive data product that provides normalized, cleaned, and analytically-ready access to FDA medical device adverse event reports. This dataset encompasses over 22 million normalized device event records, 38 million device records, and 25 million patient records, with over 100 standardized attributes and 5 pre-aggregated analytical reporting models.
🔗 Find the FDA MAUDE Dataset on the Snowflake Marketplace.
Quick Access
Base Tables: Device events, devices, patients, classifications, and narrative text Aggregate Models: 5 pre-built analytical views for competitive intelligence, safety trends, and compliance monitoring Update Frequency: Weekly from FDA MAUDE database
Overview
The FDA MAUDE Dataset provides comprehensive access to medical device adverse event data including:
Core Event Data (device__events) - Main fact table with cleaned, normalized event information
Device Information (device__events_devices) - Detailed device specifications and manufacturer data
Patient Demographics (device__events_patients) - Normalized patient information with data quality enhancements
Classifications (device__events_classifications) - Product problems, remedial actions, and report types
Narrative Text (device__events_text) - Detailed event descriptions and investigative text
Analytical Reports - Pre-aggregated intelligence for competitive analysis, safety monitoring, and compliance tracking
Dataset Structure
The FDA MAUDE dataset is organized around medical device events, with connected tables containing detailed information about the devices, patients, problem classifications, and narrative descriptions.
Color Legend:
🔵 Blue (Aggregate Reports): Pre-computed analytical models for business intelligence
⚪ White (Base Tables): Core normalized MAUDE data tables
Base Tables
device__events (Main Events Table)
Normalized FDA device adverse event reports containing core event-level information. Each record represents a single device adverse event report identified by an 8-digit mdr_report_key.
Key Features:
Over 22 million medical device event records
Clean, standardized data ready for analysis
Complete event details including dates, manufacturers, and outcomes
Enhanced data quality with both original and cleaned values
device__events_devices (Device Details Table)
Device-specific information flattened from adverse event reports. Each record represents one device involved in an adverse event.
Key Features:
Over 38 million device records with detailed specifications
FDA risk classifications (Class I, II, III) and medical specialties
Standardized manufacturer and operator information
Brand names, model numbers, and device categories
device__events_patients (Patient Information Table)
Patient-specific information with comprehensive data cleaning and normalization.
Key Features:
Over 25 million patient records with demographics and outcomes
Standardized age data (converted to consistent years format)
Weight data converted to consistent units (kilograms)
Clean gender and race categories for demographic analysis
device__events_classifications (Classifications Table)
Classification information including product problems, remedial actions, source types, and report types.
Key Features:
Four types of classifications: device problems, corrective actions, report sources, and report types
Categorized problem descriptions and manufacturer responses
Links to official FDA classification definitions
device__events_text (Narrative Text Table)
Narrative text content providing detailed descriptions of adverse events.
Key Features:
Searchable narrative descriptions of device events and problems
Connected to specific patients and events for complete context
Categorized by FDA text types (event descriptions, investigations, etc.)
Aggregate/Reporting Models
device__agg_competitive_intelligence
Market intelligence and competitive analysis across device manufacturers and medical specialties.
Use Cases:
Market share analysis and competitive benchmarking
Manufacturer risk assessment and investment due diligence
Device class safety performance comparison
Growth trend analysis and market opportunity identification
Key Metrics:
Market share by reports within medical specialties
Safety performance rankings and risk classifications
Growth trends and competitive positioning
Threat assessments and strategic recommendations
device__agg_executive_dashboard
High-level metrics and KPIs for executive reporting and strategic decision making.
Use Cases:
Executive dashboard reporting and KPI tracking
Board presentations and regulatory updates
Market sizing and opportunity analysis
Investment thesis validation
Key Metrics:
Market overview statistics across all device categories
Top manufacturers and device classes by adverse events
Trend analysis and year-over-year growth metrics
High-level safety indicators and regulatory activity
device__agg_manufacturer_adverse_events
Complete risk profiles for medical device manufacturers based on their adverse event history.
Use Cases:
Investment due diligence and manufacturer risk assessment
Supplier evaluation and vendor risk management
Insurance underwriting and claims analysis
Competitive manufacturer benchmarking
Key Metrics:
Total adverse events and affected patients across all time
Product problem rates and trend analysis
Risk classification from MINIMAL_RISK to HIGH_RISK
12-month activity vs. historical comparison
device__agg_regulatory_compliance
Compliance monitoring and regulatory risk assessment for device manufacturers.
Use Cases:
Regulatory compliance monitoring and audit preparation
Manufacturer compliance benchmarking and assessment
Legal risk evaluation and litigation support
FDA reporting timeline analysis
Key Metrics:
FDA and manufacturer reporting compliance rates
Average reporting delays and timeline compliance
Regulatory risk levels and compliance red flags
Reporting activity trends and patterns
device__agg_safety_trends
Safety trend analysis over time to identify patterns and emerging risks in medical devices.
Use Cases:
Safety trend monitoring and early warning systems
Device class risk assessment over time
Medical specialty safety performance tracking
Quality assurance and safety improvement programs
Key Metrics:
Time-series analytics by device class and medical specialty
Trend classifications (INCREASING, STABLE, DECREASING)
Risk level assessments and safety performance monitoring
Quarterly and monthly trend indicators
Data Quality Improvements
Patient Information Standardization
Consistent Age Data
All patient ages converted to a standard years format for easy analysis
Handles various original formats like "65 YR", "6 MO", "30 DA", age ranges, and approximations
Missing or invalid ages clearly marked as unknown
Unified Weight Measurements
All patient weights converted to kilograms for consistent analysis
Automatically detects and converts from pounds when needed
Realistic weight ranges validated
Standardized Demographics
Gender categories standardized across all records
Race information cleaned and made consistent
Missing demographic data clearly identified
Device Information Enhancements
Geographic Standardization
Country codes expanded to full country names for clarity
Consistent geographic categorization for global analysis
Operator Categories
Device operators grouped into clear categories (Healthcare Professional, Patient/Family, etc.)
Eliminates confusion from inconsistent original coding
Proper Date Handling
All dates converted to standard date formats for time-based analysis
Invalid dates identified and handled appropriately
Getting Started
Basic Query Examples
-- Get recent adverse events with device and patient information
SELECT
e.mdr_report_key,
e.date_report,
e.event_type,
d.brand_name,
d.generic_name,
d.openfda_medical_specialty_description,
p.patient_age_years,
p.patient_sex,
p.patient_outcomes
FROM device__events e
JOIN device__events_devices d ON e.id = d.device_event_id
LEFT JOIN device__events_patients p ON e.id = p.device_event_id
WHERE e.date_received >= '2024-01-01'
LIMIT 100;-- Search for specific device problems
SELECT
e.mdr_report_key,
e.date_report,
d.brand_name,
c.classification_value as product_problem
FROM device__events e
JOIN device__events_devices d ON e.id = d.device_event_id
JOIN device__events_classifications c ON e.id = c.device_event_id
WHERE c.classification_type = 'product_problem'
AND c.classification_value ILIKE '%battery%'
LIMIT 50;-- Find narrative text about specific events
SELECT
e.mdr_report_key,
e.date_report,
t.text_content,
d.brand_name
FROM device__events e
JOIN device__events_text t ON e.id = t.device_event_id
LEFT JOIN device__events_devices d ON e.id = d.device_event_id
WHERE t.text_content ILIKE '%device malfunction%'
LIMIT 25;Advanced Analytics Examples
-- Market share analysis by medical specialty
SELECT
medical_specialty_description,
manufacturer_name,
market_share_by_reports,
market_position,
safety_position,
growth_trend
FROM device__agg_competitive_intelligence
WHERE medical_specialty_description = 'Cardiovascular'
AND market_position IN ('MARKET_LEADER', 'MAJOR_PLAYER')
ORDER BY market_share_by_reports DESC;-- Manufacturer risk assessment
SELECT
manufacturer_name,
risk_classification,
adverse_events_last_12_months,
total_adverse_events_all_time,
product_problem_rate_pct,
adverse_event_trend
FROM device__agg_manufacturer_adverse_events
WHERE risk_classification IN ('HIGH_RISK', 'MEDIUM_RISK')
AND adverse_event_trend = 'INCREASING'
ORDER BY adverse_events_last_12_months DESC;-- Safety trend monitoring
SELECT
device_class,
medical_specialty_description,
report_period,
metric_name,
metric_value,
trend_classification,
risk_level
FROM device__agg_safety_trends
WHERE report_type = 'QUARTERLY_TREND'
AND risk_level IN ('HIGH_RISK', 'MEDIUM_RISK')
AND trend_classification = 'INCREASING'
ORDER BY report_period DESC;Patient Demographics Analysis
-- Age group analysis with normalized patient data
SELECT
CASE
WHEN patient_age_years < 18 THEN 'Pediatric'
WHEN patient_age_years BETWEEN 18 AND 64 THEN 'Adult'
WHEN patient_age_years >= 65 THEN 'Elderly'
ELSE 'Unknown'
END as age_group,
COUNT(*) as event_count,
AVG(patient_age_years) as avg_age,
AVG(patient_weight_kg) as avg_weight_kg
FROM device__events_patients
WHERE patient_age_years IS NOT NULL
GROUP BY 1
ORDER BY event_count DESC;-- Device return analysis by manufacturer
SELECT
manufacturer_d_name,
manufacturer_d_country,
COUNT(*) as total_devices,
COUNT(date_returned_to_manufacturer) as devices_returned,
ROUND(COUNT(date_returned_to_manufacturer) * 100.0 / COUNT(*), 2) as return_rate_pct,
AVG(DATEDIFF(day, device_date_received, date_returned_to_manufacturer)) as avg_days_to_return
FROM device__events_devices
WHERE device_date_received IS NOT NULL
GROUP BY 1, 2
HAVING COUNT(*) >= 100
ORDER BY return_rate_pct DESC;Simplified Access to FDA Data
Unlike the complex FDA MAUDE API, this dataset allows simple SQL queries to find the information you need:
-- Equivalent to FDA API: device.brand_name:"ARTHROSCOPY EQUIPMENT CART"
SELECT
e.mdr_report_key,
e.date_report,
e.event_type,
d.brand_name,
d.generic_name,
d.model_number
FROM device__events e
JOIN device__events_devices d ON e.id = d.device_event_id
WHERE d.brand_name = 'ARTHROSCOPY EQUIPMENT CART';-- Equivalent to FDA API: date_received:[20240101 TO 20241231]
SELECT
e.mdr_report_key,
e.date_received,
e.date_report,
e.event_type,
e.manufacturer_name
FROM device__events e
WHERE e.date_received BETWEEN '2024-01-01' AND '2024-12-31';-- Equivalent to FDA API text search: "device malfunction"
SELECT
e.mdr_report_key,
e.date_report,
t.text_content,
d.brand_name
FROM device__events e
JOIN device__events_text t ON e.id = t.device_event_id
LEFT JOIN device__events_devices d ON e.id = d.device_event_id
WHERE t.text_content ILIKE '%device malfunction%';Why Choose This Dataset
Ready for Analysis: Clean, standardized data you can query immediately
Complete Picture: All related information (devices, patients, outcomes) properly connected
Instant Insights: Pre-built analytical views for competitive intelligence and safety trends
Reliable Data Quality: Messy source data cleaned and normalized for accurate analysis
Fast Results: Optimized for quick queries and reporting
Important Data Interpretation Guidelines
Adverse event reports do not undergo extensive FDA validation and may be incomplete or inaccurate
A causal relationship cannot be established between device and reported reactions based solely on this data
Reports represent a small percentage of total device usage and should not be the sole source for clinical decisions
FDA Documentation Resources
Last updated