Hackathons are intense, short, collaborative events focusing on solving real world problems through interdisciplinary teams. This is a report of the mHealth hackathon hosted by Khon Kaen University in collaboration with MIT Sana and faculty members from Harvard Medical School with the aim to improve health care delivery in the Northeast region of Thailand. Key health challenges, such as improving population health literacy, tracking disease trajectory and outcomes among rural communities, and supporting the workflow of overburdened frontline providers, were addressed using mHealth. Many modifications from the usual format of hackathon were made to tailor the event to the local context and culture, such as the process of recruiting participants and how teams were matched and formed. These modifications serve as good learning points for hosting future hackathons. There are also many lessons learned about how to achieve a fruitful collaboration despite cultural barriers, how to best provide mentorship to the participants, how to instill in the participants a sense of mission, and how to match the participants in a fair and efficient manner. This event showcases how interdisciplinary collaboration can produce results that are unattainable by any discipline alone and demonstrates that innovations are the fruits of collective wisdom of people from different fields of expertise who work together toward the same goals.

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Assessment is closely connected to learning since assessment allows a learner’s current competencies to be measured. The results of such a measurement can then be used to produce learning. There are three goals for assessment in medical education: assessment of learning, assessment for learning, and assessment as learning. These three goals serve different purposes and therefore differ in how they are carried out. They also require different approaches for the assessors. It is crucial that all three goals be balanced and attained lest the testing culture wax and the learning culture wane, resulting in a situation in which learners place too much emphasis on passing the tests and not enough emphasis on learning and growing. No single method of assessment can sufficiently achieve all three goals. One comprehensive approach to achieve as well as balance all three goals is to utilize programmatic assessment, in which various methods of assessment are employed based on their strengths and how they can cover each other’s limitations.

PurposeHealthcare 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.MethodsA 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.ResultsThe 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.ConclusionsDatathons provide a satisfying learning experience for participants and promote affective learning, cross-disciplinary collaboration and knowledge gain in health data science.