Improving patient safety via automated laboratory-based adverse event grading.
The identification and grading of adverse events (AEs) during the conduct of clinical trials is a labor-intensive and error-prone process. This paper describes and evaluates a software tool developed by City of Hope to automate complex algorithms to assess laboratory results and identify and grade AEs. We compared AEs identified by the automated system with those previously assessed manually, to evaluate missed/misgraded AEs. We also conducted a prospective paired time [...]
Author(s): Niland, Joyce C, Stiller, Tracey, Neat, Jennifer, Londrc, Adina, Johnson, Dina, Pannoni, Susan
DOI: 10.1136/amiajnl-2011-000513