Department of Radiation Oncology, University of Washington, Seattle, Washington.
School of Medicine, Oregon Health and Science University, Portland, Oregon.
Pract Radiat Oncol. 2017 Sep-Oct;7(5):346-353. doi: 10.1016/j.prro.2017.05.008.
Incident 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).
Three hundred event reports were randomly selected from an institutional ILS database and Safety in Radiation Oncology (SAFRON), an international ILS. The reports were split into 3 groups of 100 events each: low-risk institutional, high-risk institutional, and SAFRON. Three raters retrospectively analyzed each event for contributing factors using the American Association of Physicists in Medicine taxonomy.
No events were described by a single causal factor (median, 7). The causal factor taxonomy was found to be applicable for all events, but 4 causal factors were not described in the taxonomy: linear accelerator failure (n = 3), hardware/equipment failure (n = 2), failure to follow through with a quality improvement intervention (n = 1), and workflow documentation was misleading (n = 1). The most common causal factor categories contributing to events were similar in all event types. The most common specific causal factor to contribute to events was a "slip causing physical error." Poor human factors engineering was the only causal factor found to contribute more frequently to high-risk institutional versus low-risk institutional events.
The taxonomy in the study was found to be applicable for all events and may be useful in root cause analyses and future studies. Communication and human behaviors were the most common errors affecting all types of events. Poor human factors engineering was found to specifically contribute to high-risk more than low-risk institutional events, and may represent a strategy for reducing errors in all types of events.
事件学习系统（ILSs）是提高放射肿瘤学（RO）诊所安全性的一种流行策略，但很少有报告关注 RO 中的错误原因。本研究的目的是测试 2012 年美国医学物理学家协会制定的因果因素分类法，并将其应用于 RO：事件学习系统（RO-ILS）。
从机构 ILS 数据库和国际 ILS Safety in Radiation Oncology（SAFRON）中随机选择了 300 份事件报告。将报告分为 3 组，每组 100 个事件：低风险机构、高风险机构和 SAFRON。3 名评估员使用美国医学物理学家协会的分类法回顾性地分析了每个事件的促成因素。
没有一个事件仅由一个因果因素描述（中位数为 7）。发现因果因素分类法适用于所有事件，但该分类法中未描述 4 个因果因素：直线加速器故障（n = 3）、硬件/设备故障（n = 2）、未能跟进质量改进干预措施（n = 1）和工作流程文档具有误导性（n = 1）。导致事件的最常见因果因素类别在所有事件类型中都相似。导致事件最常见的特定因果因素是“导致物理错误的失误”。仅发现人为因素工程差是导致高风险机构事件与低风险机构事件的唯一因果因素。
研究中的分类法被发现适用于所有事件，并且可能对根本原因分析和未来研究有用。沟通和人为行为是影响所有类型事件的最常见错误。发现人为因素工程差特别导致高风险机构事件多于低风险机构事件，这可能代表了降低所有类型事件错误的策略。
