A Machine Learning Approach to Reclassifying Miscellaneous Patient Safety Event Reports.

Journal: Journal of patient safety
Published Date:

Abstract

BACKGROUND AND OBJECTIVES: Medical errors are a leading cause of death in the United States. Despite widespread adoption of patient safety reporting systems to address medical errors, making sense of the reports collected in these systems is challenging in practice. Event classification taxonomies used in many reporting systems can be complex and difficult to understand by frontline reporters, leading reporters to classify reports as "miscellaneous" as opposed to assigning a specific event-type category, which may facilitate analysis.

Authors

  • Allan Fong
    National Center for Human Factors in Healthcare, MedStar Health, Washington, District of Columbia.
  • Shabnam Behzad
    Department of Computer Science, Georgetown University.
  • Zoe Pruitt
    From the National Center for Human Factors in Healthcare.
  • Raj M Ratwani
    MedStar Health, MedStar Georgetown University Hospital, 3800 Reservoir Rd, NW CG201, Washington DC, 20007 (R.W.F.); and MedStar Health, National Center for Human Factors in Healthcare, Washington, DC (R.M.R.).