Initializing and Growing a Database of Health Information Technology (HIT) Events by Using TF-IDF and Biterm Topic Modeling

Abstract

Health information technology (HIT) events were listed in the top 10 technology-related hazards since one in six patient safety events (PSE) is related to HIT. Although it becomes a common sense that event reporting is an effective way to accumulate typical cases for learning, the lack of HIT event databases remains a challenge. Aiming to retrieve HIT events from millions of event reports related to medical devices in FDA Manufacturer and User Facility Device Experience (MAUDE) database, we proposed a novel identification strategy composed of a structured data-based filter and an unstructured data-based classifier using both TF-IDF and biterm topic. A dataset with 97% HIT events was retrieved from the raw database of 2015 FDA MAUDE, which contains approximately 0.4~0.9% HIT events. This strategy holds promise of initializing and growing an HIT database to meet the challenges of collecting, analyzing, sharing, and learning from HIT events at an aggregated level.