Abstract

Error detection in radiation oncology relies heavily on voluntary reporting and many adverse events and near-misses likely go undetected. Trigger tools use existing data in patient charts to identify otherwise unaccounted-for events and have been successfully employed in other areas of medicine. We developed an automated radiation oncology-specific trigger tool and validated it against near-miss data from a high-volume incident learning system (ILS).Twenty triggers were derived from an electronic radiation oncology information system (OIS). Data from the ILS and OIS over an approximately 3.5-year period were split randomly into training and test sets. The probability of a high-grade (grade 3-4) near-miss for each treatment course in the training set was estimated using a regularized logistic regression model. The predictive model was applied to the test set. Records for 25 flagged treatment courses with an ILS entry were reviewed to explore the association between triggers and near-misses, and 25 flagged courses without an ILS entry were reviewed to detect unreported near-misses.3,159 treatment courses were analyzed; 357 had a grade 3-4 ILS entry. 2,210 courses comprised the training set, and the test set had 949 courses. Areas under the curve on the training and test sets were 0.650 and 0.652, respectively. Of 20 triggers, nine reached statistical significance on univariate analysis. Fifty percent of the 25 treatment courses in the test set with the highest predicted likelihood of a high-grade near-miss with an ILS entry had a direct relationship between the triggers and the near-miss. Review of the 25 treatment courses with the highest predicted likelihood of high-grade near-miss without an ILS entry found two unreported near-miss events.The radiation oncology-specific automated trigger tool performed modestly and identified additional treatment courses with near-miss events. Radiation oncology trigger tools deserve further exploration.

View details for DOI 10.1016/j.prro.2019.10.017

View details for PubMedID 31783170