Simulations, Models, And RadioTherapy working strategies (SMART II)

Background

Worldwide, increasing number of cancer patients is resulting in a high demand for radiotherapy (RT). Swedish RT is already facing major challenges, especially regarding workload, education, and recruitment of staff.

Considering the current level of resources, the demand can be difficult to meet, and patients may be facing long waiting times. RT is also a time-critical cancer treatment with a radiobiological basis motivating the importance of keeping to a planned schedule not to jeopardize the chances of cure.

To solve operational issues in RT, simulations and computational methods can be a time-efficient way to assist in identifying solutions to areas of improvement. Created models can also predict scenarios about likely effects if results are to be implemented in reality.

To this end, decision support aids in healthcare are designed based on such models and are intended to assist the clinical decision-making process for both healthcare professionals and patients. The concept of decision support can also be applied at a more organizational level to create technical solutions which assist staff in decision-making processes to improve an existing clinical workflow.

Purpose

The overall purpose of this project is to develop and test various decision support strategies to assist RT departments in streamlining their work given existing number of staff and available equipment for treatment delivery.

The hope is that the project will provide increased opportunities for RT managers and staff to optimize the use of limited resources with maintained or improved safety, treatment quality, workload, and patient satisfaction.

Objective

Our goal is to enable the use of stored RT data in Oncology Information Systems (OIS) for various applications in RT using a data-driven approach to find strategies supporting daily work-related decisions at RT departments.

Results

Previous results from the project have been summarized in one PhD thesis from 2021 (1).

A second PhD project has recently created a tool for automatic OIS data extraction, cleaning, and formatting since stored information in the OIS is commonly in an unstructured format and needs to be processed before use (2).

The tool was tested using the data from a large RT department in Sweden and successfully created ready-to-use datasets for external use. The time taken by the tool to prepare the datasets was significantly less compared to manual preparation with results close to a manually-prepared reference dataset. We also tested the tool-created dataset as input to an example external application and found that the result using the tool dataset was similar to the result using the reference.

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Future work

In future work, we will identify factors that potentially affect waiting times to create a dynamic waiting list model to assist in the scheduling of patients for RT. The model will account for clinical factors and patient characteristics and we will also investigate ways to automatically assign individually-tailored treatment slot times.

We are also conducting staff interviews to better understand the organizational changes or any other significant events that may have affected patient waiting times historically.