REMOTE ASYNCHRONOUS FEEDBACK FOR UNSUPERVISED LAPAROSCOPIC TRAINING: THE “LAPP” PLATFORM - Gabriel ULLOA; Andres NEYEM; Gabriel ESCALONA; Catalina ORTIZ; Julian VARAS (ABCD: Arquivos Brasileiros de Cirurgia Digestiva, 2023)
ABSTRACT BACKGROUND: The advantages of laparoscopic surgery over traditional open surgery have changed the surgical education paradigm in the past 20 years. Among its benefits are an improvement in clinical outcomes and patient safety, becoming the standard in many surgical procedures. However, it encompasses an additional challenge due to the complexity to achieve the desired competency level. Simulation-based training has emerged as a solution to this problem. However, there is a relative scarcity of experts to provide personalized feedback. Technology-Enhanced Learning could be a valuable aid in personalizing the learning process and overcoming geographic and time-related barriers that otherwise would preclude the training to happen. Currently, various educational digital platforms are available, but none of them is able to successfully provide personalized feedback. AIMS: The aim of this study was to develop and test a proof of concept of a novel Technology-Enhanced Learning laparoscopic skills platform with personalized remote feedback. METHODS: The platform “Lapp,” a web and mobile cloud-based solution, is proposed. It consists of a web and mobile application where teachers can evaluate remotely and asynchronously exercises performed by students, adding personalized feedback for trainees to achieve a learning curve wherever and whenever they train. To assess the effectiveness of this platform, two groups of students were compared: 130 participants received in-person feedback and 39 participants received remote asynchronous feedback throughout the application. RESULTS: The results showed no significant differences regarding competency levels among both groups. CONCLUSION: A novel Technology-Enhanced Learning strategy consisting of remote asynchronous feedback throughout Lapp facilitates and optimizes learning, solving traditional spatiotemporal limitations.