MAUDEMetrics: A Web-Based Tool for Automated Extraction and Analysis of FDA MAUDE Device Reports

Description MAUDEMetrics is a Python-based, Docker-compatible web application that democratizes access to MAUDE data by automating extraction, aggregation, and descriptive analysis of adverse event reports via the openFDA Device Event API. The tool provides an intuitive web interface that eliminates the need for programming expertise while supporting sophisticated multi-term queries across brand names, product codes, generic names, and manufacturer names. It automatically handles API pagination limitations and aggregates comprehensive datasets, yielding complete structured results with professional data exports including optimized Excel reports and fast raw data exports in original API structure (ZIP with 5 CSV files). The tool now supports both formatted analysis-ready exports and high-performance raw data exports optimized for large datasets. Abstract Background Monitoring medical device safety is vital for patient safety and regulatory oversight. The FDA’s Manufacturer and User Facility Device Experience (MAUDE) database, containing over 22 million adverse event reports, is a cornerstone resource for postmarket surveillance. However, current access routes, via the MAUDE web portal and the openFDA API, are limited by strict record caps (500 - 1,000 per query), missing demographic data, and complex manual aggregation requirements. These constraints impede large-scale, reproducible analysis. Objective To develop a fully automated, open-source platform that enables comprehensive extraction, cleaning, and analysis of MAUDE data, minimizing technical barriers and improving reproducibility in medical device safety research. Methods MAUDEMetrics was developed as a Python-based web application using Flask for the interface, Pandas and NumPy for data handling, SQLite for caching, and OpenPyXL for multi-sheet Excel exports. The system automates openFDA API pagination, validates more than 100 data fields, and provides both raw and cleaned datasets. Deployment is containerized via Docker to ensure reproducibility. Results MAUDEMetrics extracts and processes approximately 5,000 records in under 30 seconds, automatically generating summary statistics and visual dashboards. The application supports complex queries across device brand names, manufacturers, product codes, and date ranges, without requiring programming skills. Impact By bridging the gap between regulatory data access and practical analytics, MAUDEMetrics enables clinicians, researchers, and regulatory professionals to perform scalable, transparent, and reproducible analyses of FDA MAUDE device safety data. The tool substantially reduces manual effort, minimizes transcription errors, and accelerates device safety investigations.