Keywords
Artificial intelligence
Teaching model
Anesthesia education
Residency training
Submitted : 2026-04-13
Accepted : 2026-04-28
Published : 2026-05-13
Abstract
Objective: To investigate the educational effectiveness of the CDID preoperative visit model, defined as Consultation, Direction, Instruction, and Digital integration, in anesthesia training, and to assess its influence on patients’ preoperative anxiety, the success rate of preoperative visits, and the comprehensive competencies of trainees. Methods: Sixty anesthesia residents enrolled in the standardized training program at Shaanxi Provincial People’s Hospital between 2024 and 2025 were randomly assigned to a CDID group or a conventional teaching group, each with thirty trainees. All instructors possessed teaching certification and at least five years of clinical experience. The conventional group received routine multimedia-based theoretical instruction. In contrast, the CDID group was trained through a four-stage framework including emotional reassurance, structured guidance, procedural instruction, and digital learning support. Teaching activities were supported by a self-developed educational platform, an interactive human-machine system, and a three-dimensional digital spinal model. At the end of the training period, patients’ preoperative anxiety scores, the success rate of preoperative visits, and trainee assessment results were compared between groups. Results: No significant difference in baseline anxiety scores was observed between the two groups prior to the preoperative visit (P = 0.90). After the visit, patients managed by trainees in the CDID group showed significantly lower anxiety scores than those in the conventional group (51.97 ± 3.44 vs 55.77 ± 3.27, P < 0.01). The success rate of preoperative visits in the CDID group reached 100.0%, which was higher than that of the conventional group at 83.3% (χ2 = 5.36, P = 0.02). Trainees trained under the CDID framework achieved higher overall assessment scores compared with the conventional group (84.04 ± 3.48 vs 70.14 ± 3.33, P < 0.01). Significant improvements were observed in clinical practice performance and professional humanistic competence (P < 0.01), whereas theoretical knowledge scores did not differ significantly between groups (P = 0.07). Conclusion: The CDID educational model effectively reduces patient anxiety before surgery and improves the success rate of preoperative anesthesia visits while strengthening the overall competencies of anesthesia residents. It demonstrates particular advantages in clinical practice training and the development of professional humanistic qualities. Supported by theoretical knowledge and integrated with artificial intelligence technologies, this approach offers a novel perspective for advancing reforms in anesthesia education.
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