M. Lyndon, Atipong Pathanasethpong, M. Henning, Yan Chen, L. Celi
{"title":"Measuring the learning outcomes of datathons","authors":"M. Lyndon, Atipong Pathanasethpong, M. Henning, Yan Chen, L. Celi","doi":"10.1136/bmjinnov-2021-000747","DOIUrl":null,"url":null,"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.","PeriodicalId":53454,"journal":{"name":"BMJ Innovations","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ Innovations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/bmjinnov-2021-000747","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
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.
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.