The Impact of COVID-19 on Medical Device Reporting and Investigation

Abstract

Introduction The Manufacturer and User Facility Device Experience (MAUDE) database houses medical device reports submitted to the U.S. Food and Drug Administration (FDA). In May 2020, the FDA released guidance about medical device reporting during a pandemic, anticipating delays in reporting and investigating events involving medical devices. Methods We aimed to understand how the COVID-19 pandemic impacted medical device reporting by analyzing reports in the MAUDE database that mention COVID-19. Results From the 816,470 reports submitted between January 1 and July 31, 2020, 3,500 (0.43%) included phrases related to COVID-19. Of these reports, 4.8% (167/3,500) described adverse events during COVID-19 patients’ treatment, and 90.3% (3,161 /3,500) described barriers manufacturers faced investigating malfunctioning devices during the pandemic. 4.9% (172/3,500) of reports were not related to COVID-19. Malfunctions were clinically significant in 85.8% (3,004/3,500) of reports. Discussion Reports indicate challenges some manufacturers had when investigating medical devices during the pandemic. The pandemic made investigating implants uniquely difficult, as restrictions to person-to-person contact limited the type of care patients could receive. Because full-scale investigations into malfunctioning devices may be difficult to perform during the pandemic, safety issues may go unaddressed and result in future harm to patients. Conclusion The COVID-19 pandemic and the myriad of healthcare, travel, and shipping challenges it created impacted how manufacturers reported and investigated medical devices. At the current time, it is unclear how manufacturers will address delayed clinical management of implant devices and other uninvestigated malfunctions after the pandemic and how this will impact patient safety.

Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing

“…Furthermore, one of the primary barriers to error reporting is the varying perspective on errors themselves, since reporters may have conflicting perspectives regarding the incident being reported [ 5 ]. Given these problems associated with the analysis of error reports, natural language processing (NLP), the branch of machine learning that deals with human language and speech, has emerged as a potential technology in patient safety research to improve error prevention through better detection, reporting, and analysis [ 4 , 6 , 7 , 8 ]. A significant benefit of NLP models for medical personnel is the reduction in time and effort required to report medical errors.…”