Analysis of virtual standardized patients for assessing clinical fundamental skills of medical students: a prospective study

Background History-taking is an essential clinical competency for qualified doctors. The limitations of the standardized patient (SP) in taking history can be addressed by the virtual standardized patient (VSP). This paper investigates the accuracy of virtual standardized patient simulators and evaluates the applicability of the improved system's accuracy for diagnostic teaching support and performance assessment. Methods Data from the application of VSP to medical residents and students were gathered for this prospective study. In a human-machine collaboration mode, students completed exams involving taking SP histories while VSP provided real-time scoring. Every participant had VSP and SP scores. Lastly, using the voice and text records as a guide, the technicians will adjust the system's intention recognition accuracy and speech recognition accuracy. Results The research revealed significant differences in scoring across several iterations of VSP and SP (p < 0.001). Across various clinical cases, there were differences in application accuracy for different versions of VSP (p < 0.001). Among training groups, the diarrhea case showed significant differences in speech recognition accuracy (Z = -2.719, p = 0.007) and intent recognition accuracy (Z = -2.406, p = 0.016). Scoring and intent recognition accuracy improved significantly after system upgrades. Conclusion VSP has a comprehensive and detailed scoring system and demonstrates good scoring accuracy, which can be a valuable tool for history-taking training.