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Automating Safety Surveillance for Software-Based Medical Devices: Insights from FDA MAUDE Data.

Rohit Kesharwani¹, Lana Cvijic², Kerstin Denecke²

¹Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (BHU), Varanasi, India.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

This study analyzed FDA MAUDE database reports on digital health interventions (DHIs). Natural language processing identified trends in software medical device incidents, highlighting cybersecurity and communication issues, but noted limitations in root-cause analysis.

Keywords:

Digital health intervention automatized post-market surveillance errors incidents natural language processing software as a medical device

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Published on: July 14, 2023

Area of Science:

Medical Informatics
Health Technology Assessment
Regulatory Science

Background:

Digital health interventions (DHIs) are increasingly used in healthcare delivery and support.
DHIs registered as medical devices require incident reporting.
The U.S. Food and Drug Administration's MAUDE database collects these incident reports.

Purpose of the Study:

To examine errors and incidents involving software-based medical devices using MAUDE database narratives.
To identify event types, contributing factors, and temporal patterns in these incidents.
To assess the utility of natural language processing for analyzing regulatory reports.

Main Methods:

Natural language processing (NLP) techniques, including BERTopic clustering and large language model-based classification.
Analysis of narrative reports from the FDA's MAUDE database.
Identification of thematic trends and topic evolution over time.

Main Results:

BERTopic revealed thematic trends and topic evolution, though some clusters reflected documentation rather than software issues.
Emerging themes included cybersecurity, patient-device communication, and pandemic monitoring.
MAUDE narratives provided insights but lacked contextual detail for comprehensive root-cause analysis.

Conclusions:

Post-market surveillance for DHIs is expanding to include broader human-device and public health contexts.
Current reporting structures in the MAUDE database have limitations for in-depth root-cause analysis of software medical device incidents.
Enhanced reporting mechanisms are necessary for the safer design and oversight of digital health interventions.