Lindberg Jesper, Holmström Paul, Hallberg Stefan, Björk-Eriksson Thomas, Olsson Caroline E
Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45, Gothenburg, Sweden.
Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden.
BMC Health Serv Res. 2021 Mar 8;21(1):207. doi: 10.1186/s12913-021-06162-4.
In meeting input data requirements for a system dynamics (SD) model simulating the radiotherapy (RT) process, the number of patient care pathways (RT workflows) needs to be kept low to simplify the model without affecting the overall performance. A large RT department can have more than 100 workflows, which results in a complex model structure if each is to be handled separately. Here we investigated effects on model performance by reducing the number of workflows for a model of the preparatory steps of the RT process.
We created a SD model sub-structure capturing the preparatory RT process. Real data for patients treated in 2015-2016 at a modern RT department in Sweden were used. RT workflow similarity was quantified by averaged pairwise utilization rate differences (%) and the size of corresponding correlation coefficients (r). Grouping of RT workflows was determined using two accepted strategies (80/20 Pareto rule; merging all data into one group) and a customized algorithm with r≥0.75:0.05:0.95 as criteria for group inclusion by two strategies (A1 and A2). Number of waiting patients for each grouping strategy were compared to the reference of all workflows handled separately.
There were 128 RT workflows for 3209 patients during the studied period. The 80/20 Pareto rule resulted in 14/8/21 groups for curative/palliative/disregarding treatment intent. Correspondingly, A1 and A2 resulted in 7-40/≤4-36/7-82 groups depending on r cutoff. Results for the Pareto rule and A2 at r≥85 were comparable to the reference.
The performance of a simulation model over the RT process will depend on the grouping strategy of patient input data. Either the Pareto rule or the grouping of patients by resource use can be expected to better reflect overall departmental effects to various changes than when merging all data into one group. Our proposed approach to identify groups based on similarity in resource use can potentially be used in any setting with variable incoming flows of objects which go through a multi-step process comparable to RT where the aim is to reduce the complexity of associated model structures without compromising with overall performance.
在满足用于模拟放射治疗（RT）过程的系统动力学（SD）模型的输入数据要求时，需要保持患者护理路径（RT工作流程）的数量较低，以简化模型而不影响整体性能。一个大型RT科室可能有100多个工作流程，如果每个流程都单独处理，会导致模型结构复杂。在此，我们研究了通过减少RT过程准备步骤模型的工作流程数量对模型性能的影响。
我们创建了一个捕获RT准备过程的SD模型子结构。使用了瑞典一家现代RT科室在2015 - 2016年治疗患者的真实数据。RT工作流程相似性通过平均成对利用率差异（%）和相应相关系数（r）的大小进行量化。RT工作流程的分组使用两种公认策略（80/20帕累托法则；将所有数据合并为一组）以及一种定制算法（以r≥0.75:0.05:0.95作为两种策略（A1和A2）纳入组的标准）来确定。将每种分组策略下等待患者的数量与单独处理所有工作流程的参考情况进行比较。
在研究期间，3209名患者有128个RT工作流程。80/20帕累托法则导致针对根治性/姑息性/不考虑治疗意图分别有14/8/21组。相应地，根据r截止值，A1和A2分别导致7 - 40/≤4 - 36/7 - 82组。r≥85时，帕累托法则和A2的结果与参考情况相当。
RT过程模拟模型的性能将取决于患者输入数据的分组策略。与将所有数据合并为一组相比，帕累托法则或按资源使用对患者进行分组预计能更好地反映部门对各种变化的总体影响。我们提出的基于资源使用相似性识别组的方法可能适用于任何具有可变输入流对象的环境，这些对象经过类似于RT的多步骤过程，目的是在不影响整体性能的情况下降低相关模型结构的复杂性。
