{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T23:40:56Z","timestamp":1792712456310,"version":"4.5.0"},"reference-count":39,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Tumori"],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Process mining (PM) is a powerful approach for analysing and optimising complex workflows. Radiation therapy (RT) involves multiple steps and resources, making it well suited for PM analysis.<\/jats:p>\n                    <jats:p>This study aims to apply a PM approach to characterise the real-world workflow of a high-volume RT department, with the goal of identifying critical transitions and bottlenecks, quantifying their impact on treatment timelines, and exploring the main factors associated with treatment suspensions and cancellations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods:<\/jats:title>\n                    <jats:p>Patients treated with RT at our centre between January 2017 and December 2021 were included. All patient-related events were extracted from the institutional database. A first-order Markov model was used to analyse event sequences and identify anomalies, while the Kruskal\u2013Wallis test compared median completion times across stratified sub-cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      The study analysed 43,183 events associated with 8608 treatments. The pathway diagram depicted key events, such as\n                      <jats:italic toggle=\"yes\">Prescription<\/jats:italic>\n                      ,\n                      <jats:italic toggle=\"yes\">Scheduling<\/jats:italic>\n                      ,\n                      <jats:italic toggle=\"yes\">CT simulation<\/jats:italic>\n                      ,\n                      <jats:italic toggle=\"yes\">RT start<\/jats:italic>\n                      ,\n                      <jats:italic toggle=\"yes\">RT cancelled<\/jats:italic>\n                      ,\n                      <jats:italic toggle=\"yes\">RT suspension<\/jats:italic>\n                      , and\n                      <jats:italic toggle=\"yes\">RT end<\/jats:italic>\n                      . Statistical analysis revealed a significant difference in terms of median time in case of suspension event during the path, leading to a delay in the start of RT treatment (p&lt;0.05). The analysis of the employed time from\n                      <jats:italic toggle=\"yes\">Prescription<\/jats:italic>\n                      to\n                      <jats:italic toggle=\"yes\">RT start<\/jats:italic>\n                      demonstrated a significant impact of suspension on the time interval for breast, genitourinary, gastrointestinal, metastatic, head and neck, gynaecological, thoracic, and skin cancer (p&lt;0.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>This study illustrates the applicability of PM as a methodological framework for analysing care pathways. By demonstrating how PM can identify delays, interruptions, and workflow inefficiencies, it highlights its potential to support process evaluation and optimisation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/03008916261452956","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T13:04:56Z","timestamp":1786539896000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Analysing workflows in radiation oncology: A process mining approach to identify treatment process challenges"],"prefix":"10.1177","author":[{"given":"Federico","family":"Mastroleo","sequence":"first","affiliation":[{"name":"Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy"},{"name":"Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto","family":"Gatta","sequence":"additional","affiliation":[{"name":"Department of Clinical and Experimental Sciences, University of Study of Brescia, Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariagrazia","family":"Lorusso","sequence":"additional","affiliation":[{"name":"Department of Clinical and Experimental Sciences, University of Study of Brescia, Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0498-2964","authenticated-orcid":false,"given":"Stefania","family":"Volpe","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy"},{"name":"Department of Oncology and Hemato-Oncology, University of Milan, Milan, 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