Understanding the Building Blocks of Interpreting Complex Medical Graphs for Enabling Evidence-Based Medical Decision Making

Abstract

Abstract Understanding health statistics in graphical or numerical format is essential for evidence-based medical decision making. It has been reported repeatedly that physicians, medical students, and patients perform poorly when interpreting those kinds of information. With the current research we started to decompose skills and competencies needed for interpreting complex medical graphs. We invited students at Weill Cornell Medical College in Qatar to fill in a questionnaire on www.SurveyMonkey.com that contained three visual medical tasks and one well-investigated complex diagrammatic reasoning task from economics. We expected that students’ performance would improve with years at the medical college for medical tasks, but not necessarily for an unrelated diagrammatic reasoning task. 85 students (between 8 and 19 students per class) participated in this research. For the diagrammatic reasoning task, students performed as poorly as reported in the literature and there was no statistically significant difference between pre-medical and medical students. In contrast, medical students outperformed pre-medical students for a gastrointestinal anatomy test and for two general surgery tasks on steps and structures associated with cholecystectomy. These results replicate findings of low performance for interpreting complex graphs. At the same time, medical education seems to foster students’ understanding of simple graphs in their domain and might prepare them for understanding more complex graphs in that domain. Currently, we are working on extending our research to decompose students’, physicians’, and patients’ understanding of survival curves as complex graphs in the medical domain needed for evidence-based medical decision making.