Application and evaluation of a virtual patient system based on a multi-branch dynamic decision model in orthopedic graduate medical education.

BACKGROUND

Orthopedic Graduate Medical Education (GME) necessitates the cultivation of comprehensive clinical competencies across various subspecialties—including trauma, joint disorders, spinal conditions, and sports medicine—within a constrained training period. Conventional rotation-based training models often result in uneven exposure to infrequent yet high-risk clinical scenarios, thereby limiting the systematic development of integrative clinical reasoning and awareness of long-term patient prognoses. This study aims to design and evaluate an orthopedic generalist virtual patient artificial intelligence system (OrthoSim AI Agent) that employs a multi-branching, multi-outcome, temporally dynamic decision-making framework spanning the entire continuum of patient care, from initial diagnosis to long-term follow-up. The objective is to enhance orthopedic graduate students’ clinical proficiency within standardized educational programs.

METHODS

This single-center, prospective study employing a historical control design enrolled 38 orthopedic graduate students. The control group (n = 18) completed conventional rotation training, whereas the intervention group (n = 20) received supplementary interactive core disease reinforcement training through the OrthoSim AI Agent in addition to the standard rotation curriculum. The primary outcomes evaluated were the overall and domain-specific scores derived from the Mini-Clinical Evaluation Exercise (Mini-CEX). Secondary outcomes included performance scores obtained from the Objective Structured Clinical Examination (OSCE) station focused on complication management, as well as responses to a custom-designed 5-point Likert scale satisfaction questionnaire.

RESULTS

A longitudinal Mini-CEX analysis (T1–T5) showed both groups started similarly, but the intervention group’s performance significantly surpassed the control group from T4 onward (T4: P = 0.012; T5: P < 0.001). At T5, the intervention group scored higher in clinical judgment, history-taking, and organizational efficiency (all P < 0.05), with greater overall score gains. In the OSCE, the intervention group achieved significantly higher total scores (86.95 ± 9.97 vs. 80.15 ± 4.04, P = 0.019). Student satisfaction surveys (5-point Likert scale) revealed high ratings for cross-subspecialty integration (4.25 ± 0.72), safe simulation of high-risk complications (4.45 ± 0.83), and enhanced decision-making confidence (4.15 ± 0.81) (all P < 0.001). 80% were “Extremely Likely” to recommend the system.

CONCLUSION

The OrthSim AI Agent-Assisted Training provides an integrated learning model based on multi-branch dynamic decision-making for orthopedic Graduate Medical Education. By simulating the complete clinical causal chain in a safe environment, it effectively promotes cross-subspecialty thinking, high-risk scenario management, and awareness of long-term prognosis.