TRAINING ON THE INCIDENCE OF NEEDLE INJURY AND PREVENTIVE MEASURES FOR FITNESS NURSES AT DIFFERENT STAGES OF CLINICAL PRACTICE

Abstract

Medical training for fitness nurses accommodates theoretical and practical sessions for understanding real-time patient care and handling. Some common errors such as needle injury, improper tool handling, etc. occur due to novice fitness nursing students. For preventing such errors and improving the training quality, this article introduces an Artificial Intelligence assimilated Preventive Training Measure (AI-PTM). The proposed method observes the different training sessions of fitness nursing students for error detection and training qualification. In this method, classification with recurrent learning is induced for identifying the error-causing feature in the mid of the training session. This error-causing feature is classified based on student characteristics (such as mishandling, lack of concentration, etc.) and objects (new equipment, precision handling, etc.). Based on the classification, the instance is modified in the recurrent training session, improving the student’s concentration. The identified error-causing features are stored, congruently matched, and used for training further nursing sessions. This method improves training accuracy, and precision handling, and reduces error.