The Fusion of Incident Learning and Failure Mode and Effects Analysis for Data-Driven Patient Safety Improvements.

Purpose: Incident learning is a critical part of the quality improvement process for all radiation therapy clinics. Failure mode and effects analysis has also been adopted as a hazard analysis method within the field of radiation oncology based on the recommendations of American Association of Physicists in Medicine Task Group 100. In this work, we demonstrate a fusion of these techniques that is efficient and transferrable to all types of clinics and that allows data-driven targeting of the highest risk error types.

Methods And Materials: Four clinical physicists recorded safety events detected during physics treatment plan quality assurance over a 27-month period. Events were sorted into the broad categories of either a documentation or plan construction error. Events were further stratified into subcategories until sufficiently discriminated against for analysis. Event risks were quantified using reduced-resolution TG-100 severity scores combined with observed occurrence rates. The highest risk categories were examined for intervention strategies.

Results: A total of 871 events were identified over the study period. Of these, 652 (74.9%) were classified as low severity, 178 (20.4%) as medium severity, and 41 (4.7%) as high severity. Four of the top 5 ranked categories could be targeted by a preplanning chart rounds. Several of the categories could be targeted by additional automation in the planning and QA processes.

Conclusions: The retrospective classification and risk analysis of safety events allows clinics to design targeted workflow and quality assurance changes aimed at reducing the occurrence of high-risk events. The method presented here leverages incident learning efforts that many clinics are already performing, allows the severity of events to be efficiently assigned, and generates actionable results without requiring a complete failure mode and effects analysis.