SIMULATION TRAINING AND DECISION SUPPORT FOR MEDICAL EDUCATION

Purpose:    This research investigated optimal training conditions for medical education using a laparoscopic surgery simulation engine. We manipulated the amount of practice and the availability of decision support between participants. 

Method:    In five sessions, three groups of 3^rd and 4^th year medical students learned to perform laparoscopic cholecystectomy for each of the four subtasks. They were either guided by one-on-one decision support by their mentor or they explored the system on their own. In addition, free-exploration groups were either matched to the guided group by time on the simulator (short-exploration), or were allotted double the time to compensate for missing decision support (long-exploration).    Students performed the procedure on a high fidelity simulation system for eight cases until they mastered it before progressing to the next case. Each case started with dissection and ended with removing the gallbladder from the cavity. Students had to choose one of seven instruments per hand (grasper, straight dissector, Maryland dissector, clip applicator, Metzenbaum scissors, L-hook, suction irrigation) for each subtask. Anatomic differences between cases added to the complexity of decision making for this task.

Result:      All students improved their knowledge on anatomy and on the procedure as well as skills for performing the task from pre-test to post-test. For example they reduced their time on task from over 20 min before training to less than 5 min after training. Improvements in choosing appropriate instruments for each subtask and anatomy knowledge were comparable for all groups. However, only free-exploration students with extensive practice performed as well as mentored students in terms of path length and proficiency. For example, the average number of major complications before training was 1.5 for mentored students and 2.3 for long-exploration and 2.7 for short-exploration groups. After training those numbers were 1.0 for mentored students, 1.7 for long-exploration, and 3.0 for short-exploration groups.

Conclusion:    As demonstrated for other well-structured domains, like learning algebra, learning by free exploration can be as efficient as learning by instruction if students can accumulate sufficient practice on performing the task to support their decisions.