May 19, 9:45 AM – 11:15 AM
Christos Vaitsis, MSc, Gunnar Nilsson, PhD, Nabil Zary, PhD
Healthcare education improvement is widely driven by the need to create competent healthcare professionals. The possible exploitation of curriculum data has been partly explored to a very small extent and its contribution in such efforts remains small. Visual Analytics (VA) combines data analysis, information visualization, and human cognitive strength to ultimately impact on decision making with limited implementation in healthcare education. We used VA to analyze and represent data from an undergraduate medical curriculum to explore its impact on decision making as a factor significantly affecting healthcare education quality improvement. Collected data concerned teaching and assessment methods (TM, AM) and learning outcomes (LOs) and were analyzed in two levels; (i) intended and taught LOs for the entire medical program and (ii) all TM, AM and LOs in a course. We identified relations between them and built abstract models of the examined data for both levels. The visualization of the models revealed underlying networks in curriculum data which are represented with nodes and edges. The evaluation of the ability of the produced visualizations to positively impact on analytical reasoning and decision making brought positive results which signifies the need to expand this method to represent the entire medical program while sustaining low levels of visualization complexity and opens promising research direction and would inform the further development of technical standards.