Optimizing computed tomography simulation wait times in a busy radiation medicine program

Roussos, Jerry; Zahedi, Payam; Spence, Tara; Swanson, Lue-Ann; Li-Cheung, Fionna; Cops, F.; D’Arcy, P. F.; Chhin, Veng; Moyo, Elen; Warde, Padraig; Foxcroft, Sophie; Liu, Fei‐Fei

doi:10.1016/j.prro.2016.06.007

Cited by 2 publications

(2 citation statements)

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“…A total of 91 publications were identified and included in this report. Among the Canadian publications, some were from pan-Canadian groups or were relevant to Canada in general 3,8,[10][11][12][13][14][15][16][17][18] ; others were action plans, initiatives, or studies from specific provinces, including British Columbia, [19][20][21] Alberta, 6,22 Saskatchewan, 23,24 Manitoba, 25,26 Ontario, [27][28][29][30][31][32][33][34][35][36][37][38] Quebec, 39 Prince Edward Island, 16 Newfoundland, 40 Nova Scotia, 41,42 New Brunswick, 43 and Yukon. 44 Publications were also identified from Australia, 45,46 China, 47,48 the European Union, 49 France, 50,51 India, 52 Ireland, 53 Israel,…”

Section: Resultsmentioning

confidence: 99%

Wait List Strategies for CT and MRI Scans

CADTH¹

2023

cjht

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There are various potential causes of long wait times for CT and MRI scans, and conducting a situation-specific assessment of available resources and potential cause(s) of wait times may help to identify appropriate strategies for their management. Identified principles for developing a plan to address wait times include engaging stakeholders, taking a coordinated approach to develop short- and long-term plans, ensuring plans are flexible to account for changes in technologies, and developing a plan for quality monitoring and assessment of specified outcomes. Identified strategies for reducing wait times for CT and/or MRI scans include increasing capacity, improving efficiencies, reducing low-value scans, improving communication, and adopting new technology.

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Section: Resultsmentioning

confidence: 99%

Wait List Strategies for CT and MRI Scans

CADTH¹

2023

cjht

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“…Scheduling management, a newly recognized problem in precise radiotherapy, has been of considerable interest in recent years. However, in several studies, the time spent on pretreatment preparation is the only problem that has received the most attention, 8 , 19 while the efficiency of scheduling management, that is, the utility rate of the radiation device, is ignored. In this study, realistic clinical work was performed more appropriately.…”

Section: Discussionmentioning

confidence: 99%

Machine learning‐based radiotherapy time prediction and treatment scheduling management

Xie,

Xu,

et al. 2023

J Applied Clin Med Phys

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PurposeThe utility efficiency of medical devices is important, especially for countries such as China, which have a large population and shortage of medical care resources. Radiotherapy devices are among the most valuable and specialized equipment categories and carry enormous treatment loads. In this study, a novel method is proposed to improve the efficiency of a radiotherapy device (linac). Although scheduling management with accurate prediction of the entire treatment time included in each appointment, arrange a reasonable time duration for appointments and save time between patient shifts effectively. Tasks belonging to the treatment and non‐treatment groups can be assigned more flexibly based on the availability of time.Material and methodsData from 1665 patients, including patient positioning time (PT) and treatment time (TT), were collected in collaboration with the Radiotherapy Center of the Department of Oncology at the Second Affiliated Hospital of Kunming Medical University from November 2020 to August 2021. The features related to PT and TT were extracted and used to train the machine learning‐based model to predict PT and TT in independent patients. The prediction results were subsequently applied to a minute‐based scheduling tool.ConclusionArtificial intelligence is a promising approach to solve abstract problems with a specialized knowledge background. The results of this study show encouraging prediction outcomes in relation to effective scheduling management and could improve the efficiency of the linac. This successful trial broadens the meaning of medical data and potential future research directions in radiotherapy.

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Optimizing computed tomography simulation wait times in a busy radiation medicine program

Cited by 2 publications

References 10 publications

Wait List Strategies for CT and MRI Scans

Wait List Strategies for CT and MRI Scans

Machine learning‐based radiotherapy time prediction and treatment scheduling management

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