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Original research
Measuring the learning outcomes of datathons
  1. Mataroria P Lyndon1,
  2. Atipong Pathanasethpong2,
  3. Marcus A Henning1,
  4. Yan Chen1,
  5. Leo Anthony Celi3
  1. 1 Centre for Medical and Health Sciences Education, The University of Auckland, Auckland, New Zealand
  2. 2 Department of Anesthesiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
  3. 3 Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
  1. Correspondence to Dr Mataroria P Lyndon, Centre for Medical and Health Sciences Education, The University of Auckland, Auckland, New Zealand; mataroria.lyndon{at}auckland.ac.nz

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.

  • information science
  • health
  • quality of health care

Data availability statement

Data are available on reasonable request. Data analysis (deidentified) are available on reasonable request from the corresponding author mataroria.lyndon@auckland.ac.nz.

Statistics from Altmetric.com

Data availability statement

Data are available on reasonable request. Data analysis (deidentified) are available on reasonable request from the corresponding author mataroria.lyndon@auckland.ac.nz.

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Footnotes

  • Contributors All authors meet authorship requirements. MPL: Substantial contributions to the conception and design of the work; acquisition, analysis, and interpretation of data for the work; Drafting the work and revising it critically for important intellectual content; Final approval of the version to be published and responsible for the overall content as gurantor; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. LAC: Substantial contributions to the conception and design of the work; acquisition, analysis, and interpretation of data for the work; revising the work critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. YC: Substantial contributions to the acquisition, analysis, and interpretation of data for the work; revising the work critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. MAH: Substantial contributions to the analysis, and interpretation of data for the work; revising the work critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. AP: Substantial contributions to the analysis, and interpretation of data for the work; drafting the work and revising the work critically for important intellectual content; final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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