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时间就是金钱:测量放射学阅片时间的考量因素

Time Is Money: Considerations for Measuring the Radiological Reading Time.

作者信息

Sexauer Raphael, Bestler Caroline

机构信息

Department of Radiology and Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland.

出版信息

J Imaging. 2022 Jul 24;8(8):208. doi: 10.3390/jimaging8080208.

DOI:10.3390/jimaging8080208
PMID:35893086
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9394242/
Abstract

Timestamps in the Radiology Information System (RIS) are a readily available and valuable source of information with increasing significance, among others, due to the current focus on the clinical impact of artificial intelligence applications. We aimed to evaluate timestamp-based radiological dictation time, introduce timestamp modeling techniques, and compare those with prospective measured reporting. Dictation time was calculated from RIS timestamps between 05/2010 and 01/2021 at our institution ( = 108,310). We minimized contextual outliers by simulating the raw data by iteration (1000, vector size (µ/sd/λ) = 100/loop), assuming normally distributed reporting times. In addition, 329 reporting times were prospectively measured by two radiologists (1 and 4 years of experience). Altogether, 106,127 of 108,310 exams were included after simulation, with a mean dictation time of 16.62 min. Mean dictation time was 16.05 min head CT (44,743/45,596), 15.84 min for chest CT (32,797/33,381), 17.92 min for abdominal CT ( = 22,805/23,483), 10.96 min for CT foot ( = 937/958), 9.14 min for lumbar spine (881/892), 8.83 min for shoulder (409/436), 8.83 min for CT wrist (1201/1322), and 39.20 min for a polytrauma patient (2127/2242), without a significant difference to the prospective reporting times. In conclusion, timestamp analysis is useful to measure current reporting practice, whereas body-region and radiological experience are confounders. This could aid in cost-benefit assessments of workflow changes (e.g., AI implementation).

摘要

放射信息系统(RIS)中的时间戳是一个随时可用且有价值的信息来源,其重要性日益增加,尤其是由于当前对人工智能应用临床影响的关注。我们旨在评估基于时间戳的放射学口述时间,引入时间戳建模技术,并将其与前瞻性测量的报告进行比较。口述时间是根据我们机构2010年5月至2021年1月期间的RIS时间戳计算得出的(=108,310)。我们通过迭代模拟原始数据(1000次,向量大小(µ/sd/λ)=100/循环)来最小化上下文异常值,假设报告时间呈正态分布。此外,两位放射科医生(分别有1年和4年经验)前瞻性地测量了329次报告时间。模拟后,108,310次检查中的106,127次被纳入,平均口述时间为16.62分钟。头部CT的平均口述时间为16.05分钟(44,743/45,596),胸部CT为15.84分钟(32,797/33,381),腹部CT为17.92分钟(=22,805/23,483),足部CT为10.96分钟(=937/958),腰椎为9.14分钟(881/892),肩部为8.83分钟(409/436),腕部CT为8.83分钟(1201/1322),多发伤患者为39.20分钟(2127/2242),与前瞻性报告时间无显著差异。总之,时间戳分析有助于衡量当前的报告实践,而身体部位和放射学经验是混杂因素。这有助于对工作流程变化(如人工智能实施)进行成本效益评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9394242/4355f29964c7/jimaging-08-00208-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9394242/359b59d813a6/jimaging-08-00208-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9394242/4355f29964c7/jimaging-08-00208-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9394242/359b59d813a6/jimaging-08-00208-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9394242/4355f29964c7/jimaging-08-00208-g002.jpg

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2
Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network.使用深度卷积神经网络对胸部CT上心包积液进行自动检测、分割和分类
Diagnostics (Basel). 2022 Apr 21;12(5):1045. doi: 10.3390/diagnostics12051045.
3
Machine learning for medical imaging: methodological failures and recommendations for the future.
医学成像中的机器学习:方法学上的失败与未来建议。
NPJ Digit Med. 2022 Apr 12;5(1):48. doi: 10.1038/s41746-022-00592-y.
4
Considerations on Baseline Generation for Imaging AI Studies Illustrated on the CT-Based Prediction of Empyema and Outcome Assessment.基于CT的脓胸预测和结果评估对影像人工智能研究基线生成的思考
J Imaging. 2022 Feb 22;8(3):50. doi: 10.3390/jimaging8030050.
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Subspecialized radiological reporting reduces radiology report turnaround time.专科放射学报告可缩短放射学报告周转时间。
Insights Imaging. 2020 Oct 30;11(1):114. doi: 10.1186/s13244-020-00917-z.
6
Artificial intelligence in cardiac radiology.人工智能在心脏放射学中的应用。
Radiol Med. 2020 Nov;125(11):1186-1199. doi: 10.1007/s11547-020-01277-w. Epub 2020 Sep 18.
7
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J Biomed Inform. 2020 Sep;109:103523. doi: 10.1016/j.jbi.2020.103523. Epub 2020 Aug 3.
8
Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine.HCI 在决策任务中的最新进展,以优化临床工作流程和精准医疗。
J Biomed Inform. 2020 Aug;108:103479. doi: 10.1016/j.jbi.2020.103479. Epub 2020 Jun 17.
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AJR Am J Roentgenol. 2018 Apr;210(4):799-806. doi: 10.2214/AJR.17.18613. Epub 2018 Feb 15.
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