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 assessment of automated versus manual AE assessment. We found a substantial improvement in accuracy/completeness with the automated grading tool, which identified an additional 17 % of severe grade 3e4 AEs that had been missed/misgraded manually. The automated system also provided an average time saving of 5.5 min per treatment course. With 400 ongoing treatment trials at City of Hope and an average of 1800 laboratory results requiring assessment per study, the implications of these findings for patient safety are enormous