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The aim of this retrospective, observational study was to compare fasting durations arising from standard practice with different approaches for calculating the timepoint at which patients are instructed to stop eating and drinking. Scheduling data for procedures performed in the cardiac catheterization laboratory of an academic hospital in Canada (January 2020 to April 2022) were used. Four approaches utilizing machine learning (ML) and simulation were used to predict procedure start times and calculate when patients should be instructed to start fasting. Median fasting duration for standard practice was 10.08\u00a0h (IQR 3.5) for both food and clear fluids intake. The best performing alternative approach, using tree-based ML models to predict procedure start time, reduced median fasting from food\/non-clear fluids to 7.7\u00a0h (IQR 2) and clear liquids fasting to 3.7\u00a0h (IQR 2.4). 97.3% met the minimum fasting duration requirements (95% CI 96.9% to 97.6%). Further studies are required to determine the effectiveness of operationalizing this approach as an automated fasting alert system. <\/jats:p>","DOI":"10.1177\/14604582241252791","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T10:37:02Z","timestamp":1715251022000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Determining pre-procedure fasting alert time using procedural and scheduling data"],"prefix":"10.1177","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1956-4482","authenticated-orcid":false,"given":"Litong","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada"}]},{"given":"J Christopher","family":"Beck","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, 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