Krishnatry Rahul, Johnny Carlton, Tahmeed Tahseena, Scaria Libin, Sutar Vivek, Tambe Chandrashekhar, Upreti Ritu Raj, Kinhikar Rajesh Ashok, Agarwal Jai Prakash
Department of Radiation Oncology and Medical Physics, Tata Memorial Center, Mumbai, Maharashtra, India.
HBNI, Mumbai, Maharashtra, India.
J Med Phys. 2022 Oct-Dec;47(4):367-373. doi: 10.4103/jmp.jmp_74_22. Epub 2023 Jan 10.
Higher frequency of transcriptional errors in the radiotherapy electronic charts for patients on telecobalt was noted. We describe the impact of the quality improvement (QI) initiative under the department's incident learning program (ILP).
The multidisciplinary quality team under ILP was formed to identify the root cause and introduce methods to reduce (smart goal) the current transcription error rate of 40% to <5% over 12 months. A root cause analysis including a fishbone diagram, Pareto chart, and action prioritization matrix was done to identify key drivers and interventions. Plan-Do-Study-Act (PDSA) Cycle strategy was undertaken. The primary outcome was percentage charts with transcriptional errors per month. The balancing measure was "new errors" due to interventions. All errors were identified and corrected before patient treatment.
The average baseline error rate was 44.14%. The two key drivers identified were education of the workforce involved and mechanical synchronization of various machine parameters. PDSA cycle 1 consisted of an education program and sensitization of the staff, post which the error rates dropped to 5.4% (-test = 0.03). Post-PDSA cycle 2 (synchronization of machine parameters), 1, 3, and 6 months and 1 year, the error rates were sustained to 5%, 4%, 3%, and 4% (-test > 0.05) with no new additional errors.
With various generations of machines and technologies that are not synchronized, the proneness of transcription errors can be very high which can be identified and corrected with a typical QI process under ILP.
发现远程钴治疗患者的放射治疗电子图表中存在更高频率的转录错误。我们描述了在部门事件学习计划（ILP）下质量改进（QI）举措的影响。
成立了ILP下的多学科质量团队，以确定根本原因并引入方法，在12个月内将当前40%的转录错误率降低至<5%（明智目标）。进行了包括鱼骨图、帕累托图和行动优先级矩阵在内的根本原因分析，以确定关键驱动因素和干预措施。采用了计划-执行-研究-行动（PDSA）循环策略。主要结果是每月有转录错误的图表百分比。平衡指标是干预导致的“新错误”。在患者治疗前识别并纠正所有错误。
平均基线错误率为44.14%。确定的两个关键驱动因素是相关工作人员的培训以及各种机器参数的机械同步。PDSA循环1包括一个培训计划和工作人员的宣传，之后错误率降至5.4%（-检验=0.03）。在PDSA循环2（机器参数同步）之后，1个月、3个月、6个月和1年时，错误率分别维持在5%、4%、3%和4%（-检验>0.05），且没有新的额外错误。
由于不同代的机器和技术未同步，转录错误的倾向可能非常高，而通过ILP下的典型QI流程可以识别并纠正这些错误。
