Determining pre-procedure fasting alert time using procedural and scheduling data.

IF 2.2 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES Health Informatics Journal Pub Date : 2024-04-01 DOI:10.1177/14604582241252791
Litong Zheng, J Christopher Beck, Sebastian Mafeld, Matteo Parotto, Amanda Matthews, Sheryl Alexandre, Aaron Conway
{"title":"Determining pre-procedure fasting alert time using procedural and scheduling data.","authors":"Litong Zheng, J Christopher Beck, Sebastian Mafeld, Matteo Parotto, Amanda Matthews, Sheryl Alexandre, Aaron Conway","doi":"10.1177/14604582241252791","DOIUrl":null,"url":null,"abstract":"<p><p>Before a medical procedure requiring anesthesia, patients are required to not eat or drink non-clear fluids for 6 h and not drink clear fluids for 2 h. Fasting durations in standard practice far exceed these minimum thresholds due to uncertainties in procedure start time. 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 h (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 h (IQR 2) and clear liquids fasting to 3.7 h (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.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 2","pages":"14604582241252791"},"PeriodicalIF":2.2000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Informatics Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/14604582241252791","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

Abstract

Before a medical procedure requiring anesthesia, patients are required to not eat or drink non-clear fluids for 6 h and not drink clear fluids for 2 h. Fasting durations in standard practice far exceed these minimum thresholds due to uncertainties in procedure start time. 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 h (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 h (IQR 2) and clear liquids fasting to 3.7 h (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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用手术和日程安排数据确定手术前禁食警戒时间。
由于手术开始时间的不确定性,标准实践中的禁食时间远远超过了这些最低阈值。这项回顾性观察研究的目的是比较标准实践中的禁食持续时间与不同的计算方法,即患者被指示停止进食和饮水的时间点。研究使用了加拿大一家学术医院心导管室的手术排程数据(2020 年 1 月至 2022 年 4 月)。利用机器学习(ML)和模拟的四种方法预测了手术开始时间,并计算出患者应在何时开始禁食。食物和清水摄入的标准禁食时间中位数为 10.08 小时(IQR 3.5)。性能最佳的替代方法是使用基于树的 ML 模型来预测手术开始时间,从而将食物/非清流液禁食时间的中位数缩短至 7.7 小时(IQR 2),清流液禁食时间的中位数缩短至 3.7 小时(IQR 2.4)。97.3%的患者达到了最短禁食时间要求(95% CI 96.9% 至 97.6%)。还需要进一步的研究来确定这种方法作为自动空腹警报系统的操作有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Health Informatics Journal
Health Informatics Journal HEALTH CARE SCIENCES & SERVICES-MEDICAL INFORMATICS
CiteScore
7.80
自引率
6.70%
发文量
80
审稿时长
6 months
期刊介绍: Health Informatics Journal is an international peer-reviewed journal. All papers submitted to Health Informatics Journal are subject to peer review by members of a carefully appointed editorial board. The journal operates a conventional single-blind reviewing policy in which the reviewer’s name is always concealed from the submitting author.
期刊最新文献
Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals. Analysis of health recommendations using longitudinal quality of life data: QoL@TbA - A transformer-based approach. Analysis of total RNA as a potential biomarker of developmental neurotoxicity in silico. Characterizing pituitary adenomas in clinical notes: Corpus construction and its application in LLMs. HealthCheck: A method for evaluating persuasive mobile health applications.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1