O026 The surgical datathon - a high-yield learning model for students: a mixed methods analysis - C The; China, M; Jain, R; Joshi, S; Pandit, A (British journal of surgery, 2023)
Introduction Datathons are high-yield research events where cross-disciplinary researchers collaborate to investigate pertinent healthcare problems by analysing multimodal electronic patient data. This paper aims to demonstrate the value of a surgical datathon as a learning model in students using both objective and qualitative assessments. Methods Fifteen individuals were recruited for the 24-hour datathon with representation from medicine, psychology, and clinical neurosciences including two postgraduate neurosurgery faculty. All reported interest in learning more academic skills. The aim was to solve a pertinent neurosurgical challenge, namely the prediction of intracranial pressure based on brain MR-imaging, with the goal of completing a full manuscript within 24 hours. Participants were trained and split into smaller open working groups, supervised allowing real-time specialist feedback. Groups intermittently merged to cross-collaborate. Surveys with mixed-methods analysis explored participant opinions and the value of developing research skills at multiple time points. Results Participants’ knowledge were tested showing an increased mean test score post-datathon (69.92%) compared to pre-datathon scores (57.31%). Wilcoxon signed-rank test results demonstrated a significant change for these results (p=0.04, 95% CI -27.21 - -0.04). Likert-scales showed that majority of participants strongly agreed that the datathon accelerated their productivity (58.3%). Data collection was rated particularly useful (100%) despite majority being less than confident in analysing neuro-radiological images (84.6%) pre-datathon. Time pressures were the largest barrier participants faced (47.1%). Conclusion A full manuscript was produced and the datathon goal was met with conference acceptance. Surgical datathons represent a pragmatic learning model for students of all backgrounds.