{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["32"],"submitter":["Gurjar M"],"funding":["The Kamprad Family Foundation","Swedish Research Council","Jubilee Clinic Forskningsfond against Cancer"],"pubmed_abstract":["<h4>Introduction</h4>There is an increase in demand for Radiotherapy (RT) and it is a time critical treatment with a complex scheduling process. RT workflow is inter-dependent and involves various steps including pre-treatment and treatment-related tasks which adds to these challenges. Globally, scheduling delays are reported as one of the most common issues in RT. We aim to create and evaluate an automated strategy which generates a patient allocation list to assist the scheduling staff to create an efficient scheduling process.<h4>Methods and materials</h4>We used historical data from a large RT department in Sweden from January to December 2022 with 11-13 operational linear accelerators. The algorithm was developed in C# language. It utilizes patient and treatment-related characteristic"],"journal":["Technical innovations & patient support in radiation oncology"],"pagination":["100282"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11533699"],"repository":["biostudies-literature"],"pubmed_title":["A data-driven approach to solve the RT scheduling problem."],"pmcid":["PMC11533699"],"pubmed_authors":["Bjork-Eriksson T","Lindberg J","Gurjar M","Olsson C"],"additional_accession":[]},"is_claimable":false,"name":"A data-driven approach to solve the RT scheduling problem.","description":"<h4>Introduction</h4>There is an increase in demand for Radiotherapy (RT) and it is a time critical treatment with a complex scheduling process. RT workflow is inter-dependent and involves various steps including pre-treatment and treatment-related tasks which adds to these challenges. Globally, scheduling delays are reported as one of the most common issues in RT. We aim to create and evaluate an automated strategy which generates a patient allocation list to assist the scheduling staff to create an efficient scheduling process.<h4>Methods and materials</h4>We used historical data from a large RT department in Sweden from January to December 2022 with 11-13 operational linear accelerators. The algorithm was developed in C# language. It utilizes patient and treatment-related characteristic","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Dec","modification":"2026-07-16T12:51:30.266Z","creation":"2025-04-04T07:28:09.42Z"},"accession":"S-EPMC11533699","cross_references":{"pubmed":["39497855"],"doi":["10.1016/j.tipsro.2024.100282"]}}