Measuring the learning outcomes of datathons

IF 1.4 Q3 HEALTH CARE SCIENCES & SERVICES
M. Lyndon, Atipong Pathanasethpong, M. Henning, Yan Chen, L. Celi
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引用次数: 0

Abstract

Purpose Healthcare datathons are events in which cross-disciplinary teams leverage data science methodologies to address clinical questions using large datasets. The aim of this research was to evaluate participant satisfaction and learning outcomes of datathons. Methods A multicentre cross-sectional study was performed using survey data from datathons conducted in Sydney, Australia (April 2018) n=98, Singapore (July 2018) n=169 and Beijing, China (December 2018) n=200. Participants (n=467) completed an online confidential survey at the end of the datathons which contained the Affective Learning Scale, and measures of event satisfaction, perceived knowledge gain, as well as free text responses, and participants’ demographic background. Data analysis used descriptive statistics and multivariate analysis of variance (MANOVA). Thematic analysis was performed on the text responses. Results The overall response rate was 64% (301/467). Participants were mostly male (70%); 50.2% were health professionals and 49.8% were data scientists. Based on the Affective Learning Scale (7-point Likert type scale), participants reported a positive learning experience (M = 5.93, SD = 1.21), satisfaction for content and subject matter of the datathon (M = 5.81, SD = 1.17), applying behaviours (M = 4.71, SD =2.02), instruction from mentors (M = 6.01, SD = 1.18), and intention to participate in future datathons (M = 6.03, SD = 1.23). The MANOVA showed significant differences between health professionals and data scientists in perceived knowledge gain from the datathons. Themes from text responses emerged: (1) cross-disciplinary collaboration; (2) improving healthcare using data science and (3) preparations for big data analytics. Conclusions Datathons provide a satisfying learning experience for participants and promote affective learning, cross-disciplinary collaboration and knowledge gain in health data science.
测量数据马拉松的学习成果
医疗保健数据马拉松是跨学科团队利用数据科学方法使用大型数据集解决临床问题的活动。本研究的目的是评估数据马拉松的参与者满意度和学习成果。方法采用澳大利亚悉尼(2018年4月)(n=98)、新加坡(2018年7月)(n=169)和中国北京(2018年12月)(n=200)数据马拉松的调查数据进行多中心横断面研究。参与者(n=467)在数据马拉松结束时完成了一项在线保密调查,其中包括情感学习量表、事件满意度、感知知识获得、自由文本回复和参与者的人口统计学背景。数据分析采用描述性统计和多变量方差分析(MANOVA)。对文本回复进行主题分析。结果总有效率为64%(301/467)。参与者以男性居多(70%);50.2%是卫生专业人员,49.8%是数据科学家。基于情感学习量表(7点Likert型量表),参与者报告了积极的学习体验(M = 5.93, SD = 1.21),对数据马拉松的内容和主题的满意度(M = 5.81, SD = 1.17),应用行为(M = 4.71, SD =2.02),导师指导(M = 6.01, SD = 1.18)和参与未来数据马拉松的意愿(M = 6.03, SD = 1.23)。方差分析显示,卫生专业人员和数据科学家在从数据马拉松中获得的感知知识方面存在显著差异。从文本回复中可以得出以下主题:(1)跨学科合作;(2)利用数据科学改善医疗保健;(3)为大数据分析做准备。结论数据马拉松为参与者提供了满意的学习体验,促进了健康数据科学的情感学习、跨学科协作和知识获取。
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来源期刊
BMJ Innovations
BMJ Innovations Medicine-Medicine (all)
CiteScore
4.20
自引率
0.00%
发文量
63
期刊介绍: Healthcare is undergoing a revolution and novel medical technologies are being developed to treat patients in better and faster ways. Mobile revolution has put a handheld computer in pockets of billions and we are ushering in an era of mHealth. In developed and developing world alike healthcare costs are a concern and frugal innovations are being promoted for bringing down the costs of healthcare. BMJ Innovations aims to promote innovative research which creates new, cost-effective medical devices, technologies, processes and systems that improve patient care, with particular focus on the needs of patients, physicians, and the health care industry as a whole and act as a platform to catalyse and seed more innovations. Submissions to BMJ Innovations will be considered from all clinical areas of medicine along with business and process innovations that make healthcare accessible and affordable. Submissions from groups of investigators engaged in international collaborations are especially encouraged. The broad areas of innovations that this journal aims to chronicle include but are not limited to: Medical devices, mHealth and wearable health technologies, Assistive technologies, Diagnostics, Health IT, systems and process innovation.
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