Journal Article
Study

Using statistical text classification to identify health information technology incidents.

Chai KE; Anthony S; Coiera E; Magrabi F.

A 2011 Institute of Medicine report found that existing health information technology (IT) systems have several problems that seriously compromise their ability to improve the safety and quality of care, and the report recommended standardizing measures of adverse events associated with health IT. This study discusses a novel method of identifying health IT–related adverse consequences within an existing database. Using the Food and Drug Administration's Manufacturer and User Facility Device Experience (MAUDE) database, which includes voluntarily reported safety incidents relating to medical devices, the authors developed and iteratively tested queries for identifying health IT–related adverse events. These queries can be used for earlier detection of patient care problems associated with health IT, in a manner analogous to post-approval drug safety surveillance. This study also demonstrates how "big data" can be analyzed to improve patient care (the dataset included more than 44 billion individual data elements generated from more than 500,000 separate incidents). A wrong-patient error resulting from poor health IT interoperability is discussed in an AHRQ WebM&M commentary.