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Thinking, Creating, Innovating: Utilizing Algorithms in PBL Setup to Enhance Pathology Education


 
 
 Exponential development in the depth and breadth of information in pathology has been challenging for the learners, because the pressure it creates to move quickly beyond content knowledge, to critical analysis, and to the process of applying pathophysiological concepts to clinical scenarios creates cognitive dissonance that impairs near-term performance and long-term retention. We propose an instructional approach where well-organized information is amalgamated with radiological imaging, immunohistochemistry, and molecular pathology, in the form of executable computer programs, to help students categorize the material and retain it as a way to activate multiple sensory systems.
 
 
 
 A demonstration to convey a basic setup of Java to the students was delivered by IT support in the beginning of the semester. PowerPoint presentations on selective systemic pathology topics were used as an in-class vehicle by the large groups to learn, and by the small groups as a tool to create and design computer algorithms with essential checklists in select pathophysiology topics, like solid tumors. Data comprising gross and microscopic histopathological findings, pathophysiology, and differential diagnoses, woven into correlated clinical vignettes, were then provided to small groups, who worked in a PBL setup using patient cases and the already designed computer algorithms, to present their diagnoses. Continuous faculty assessment of student performance was done.
 
 
 
 Overall data analysis of block exams revealed that the mean scores were considerably higher for participants, compared to the average results of nonparticipant batches in the same exam. These participants reported better retention and development of significantly higher levels of interest in the subject.
 
 
 
 Our preliminary experience suggests that our new instructional methodology can revitalize pathology education by aligning it better with medical students’ inherent enthusiasm for learning and retention. We will continue developing the computational pathology model in various clinicopathological topics as an undergraduate pathology education strategy.
