Causal factors for error in radiation oncology

Abstract

In this issue of Practical Radiation Oncology, the authors of “Evaluation of Near Miss and Adverse Events in Radiation Oncology Using a Comprehensive Causal Factor Taxonomy” present their analysis of the contributing factors taxonomy from the American Association of Physicists in Medicine consensus document, [1] Ford EC Fong de Los Santos L Pawlicki T Sutlief S Dunscombe P Consensus recommendations for incident learning database structures in radiation oncology. Med Phys. 2012; 39: 7272-7290 Google Scholar an adaption of which is in use in radiation oncology incident learning systems (RO-ILS). This work is important because it represents an initial exploration of what the causal factors are in radiation oncology from high-quality institutional data and international data. It is important to note the differences between the 2 sources of data used for this analysis: the author’s institutional data and the international Safety in Radiation Oncology (SAFRON) data. The institutional data are from a center with high levels of event reporting (including near misses, process improvement, unsafe conditions, and incidents) with a healthy feedback loop that supports such high levels of reporting. In contrast, the SAFRON data are from an international system, comprising 42 separate centers at last report, [2] Gilley D, Holmberg O. Risk assessment in radiotherapy and patient safety. Paper presented at: 25th European Safety and Reliability Conference 2015; Zurich, Switzerland. Google Scholar with varying levels of reporting thresholds and varying levels of safety culture at each department. Whether all 42 of these centers have individual incident learning systems in which a broader range of reporting occurs is not known. The causal factors for the reported events were determined through a consensus of 3 skilled raters. Although no interrater reliability was presented for these individuals, existing data indicate that the kappa for causal factors in this formalism is quite good (0.77). [3] Kapur A Evans S Brown D et al. TU-D-201-04: Veracity of data elements in radiation oncology incident learning systems. Med Phys. 2016; 43: 3743-3744 Google Scholar Evaluation of near-miss and adverse events in radiation oncology using a comprehensive causal factor taxonomyPractical Radiation OncologyVol. 7Issue 5PreviewIncident learning systems (ILSs) are a popular strategy for improving safety in radiation oncology (RO) clinics, but few reports focus on the causes of errors in RO. The goal of this study was to test a causal factor taxonomy developed in 2012 by the American Association of Physicists in Medicine and adopted for use in the RO: Incident Learning System (RO-ILS). Full-Text PDF