The laparoscopic pancreaticojejunostomy procedure is among the riskiest and most challenging procedures. In clinics, the operation is rarely permitted to be performed by surgeons with low or intermediate seniority. Therefore, the creation of an efficient stepwise training program and a trustworthy simulation training model with an emphasis on laparoscopic pancreaticojejunostomy was urgently required.

At Sir Run Run Shaw Hospital, surgeons with various employment histories or exposure to various training programs were separated into 4 categories. Each participant had to carry out a laparoscopic pancreaticojejunostomy using a specially made dry lab model in three dimensions. The baseline parameters and surgical performance of each surgeon, including the duration and results of each procedure, were noted and analyzed. Four experienced surgeons analyzed the model’s veracity.

A higher average age, more years of experience, and more laparoscopic cholecystectomy and laparoscopic common bile duct exploration procedures were all characteristics of the surgeon group with the highest seniority. In contrast, the group of surgeons with more experience performed operations with better results, taking less time overall and scoring higher overall during their initial simulation training. Comparing resident surgeons who had stepwise training with the laparoscopic basic suture training program to those who underwent stepwise instruction with the laparoscopic biliary-enteric anastomosis training program, it was shown that the former performed better at the start. The surgeons’ surgical performance increased after further training.

As more experienced surgeons performed better with the model for their initial simulation training in laparoscopic pancreaticojejunostomy, the pancreaticojejunostomy model demonstrated a fair degree of discernibility. Surgeons achieved a superior beginning performance thanks to stepwise training of the laparoscopic biliary-enteric anastomosis training program, and their surgical performance was enhanced by recurrent simulation training on the model.