Placidi L, Boldrini L, Lenkowicz J, Manfrida S, Gatta R, Damiani A, Chiesa S, Ciellini F, Valentini V
Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy.
Istituto di Radiologia, Università Cattolica del Sacro Cuore, Roma, Italy.
Tech Innov Patient Support Radiat Oncol. 2021 Mar 1;17:32-39. doi: 10.1016/j.tipsro.2021.02.005. eCollection 2021 Mar.
In radiotherapy, palliative patients are often suboptimal managed and patients experience long waiting times. Event-logs (recorded local files) of palliative patients, could provide a continuative decision-making system by means of shared guidelines to improve patient flow. Based on an event-log analysis, we aimed to accurately understand how to successively optimize patient flow in palliative care.
A process mining methodology was applied on palliative patient flow in a high-volume radiotherapy department. Five hundred palliative radiation treatment plans of patients with bone and brain metastases were included in the study, corresponding to 290 patients treated in our department in 2018. Event-logs and the relative attributes were extracted and organized. A process discovery algorithm was applied to describe the real process model, which produced the event-log. Finally, conformance checking was performed to analyze how the acquired event-log database works in a predefined theoretical process model.
Based on the process discovery algorithm, 53 (10%) plans had a dose prescription of 8 Gy, 249 (49.8%) plans had a dose prescription of 20 Gy and 159 (31.8%) plans had a dose prescription of 30 Gy. The remaining 39 (7.8%) plans had different dose prescriptions. Considering a median value, conformance checking demonstrated that event-logs work in the theoretical model.
The obtained results partially validate and support the palliative patient care guideline implemented in our department. Process mining can be used to provide new insights, which facilitate the improvement of existing palliative patient care flows.
在放射治疗中，姑息治疗患者的管理往往不够理想，患者等待时间较长。姑息治疗患者的事件日志（记录的本地文件）可通过共享指南提供一个持续的决策系统，以改善患者流程。基于事件日志分析，我们旨在准确了解如何在姑息治疗中连续优化患者流程。
将过程挖掘方法应用于一个高流量放射治疗科室的姑息治疗患者流程。本研究纳入了500例骨转移和脑转移患者的姑息性放射治疗计划，对应于2018年在我们科室接受治疗的290例患者。提取并整理了事件日志及其相关属性。应用过程发现算法来描述生成事件日志的实际过程模型。最后，进行一致性检查，以分析所获取的事件日志数据库在预定义的理论过程模型中的运行情况。
基于过程发现算法，53例（10%）计划的剂量处方为8 Gy，249例（49.8%）计划的剂量处方为20 Gy，159例（31.8%）计划的剂量处方为30 Gy。其余39例（7.8%）计划有不同的剂量处方。考虑到中位数，一致性检查表明事件日志在理论模型中有效。
所获结果部分验证并支持了我们科室实施的姑息治疗患者护理指南。过程挖掘可用于提供新的见解，有助于改进现有的姑息治疗患者护理流程。
