Health information technology (HIT) events, a subtype of patient safety events, pose a major threat and barrier toward a safer healthcare system. It is crucial to gain a better understanding of the nature of the errors and adverse events caused by current HIT systems. The scarcity of HIT event-exclusive databases and event reporting systems indicates the challenge of identifying the HIT events from existing resources. FDA Manufacturer and User Facility Device Experience (MAUDE) database is a potential resource for HIT events. However, the low proportion and the rapid evolvement of HIT-related events present challenges for distinguishing them from other equipment failures and hazards. We proposed a strategy to identify and synchronize HIT events from MAUDE by using a filter based on structured features and classifiers based on unstructured features. The strategy will help us develop and grow an HIT event-exclusive database, keeping pace with updates to MAUDE toward shared learning.
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