Jesper Lindberg1,2,3, Paul Holmström4, Stefan Hallberg5, Thomas Björk-Eriksson5,6, Caroline E Olsson4,5. 1. Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45, Gothenburg, Sweden. jesper.lindberg@gu.se. 2. Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden. jesper.lindberg@gu.se. 3. Regional Cancer Centre West, Western Sweden Healthcare Region, 413 45, Gothenburg, Sweden. jesper.lindberg@gu.se. 4. Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45, Gothenburg, Sweden. 5. Regional Cancer Centre West, Western Sweden Healthcare Region, 413 45, Gothenburg, Sweden. 6. Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45, Gothenburg, Sweden.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Entities:
Keywords:
Data input set; Patient inflow; Radiotherapy; Simulation; System dynamics
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