EVALUATION OF THE EFFECTIVENESS, COSTS, AND COST-EFFECTIVENESS OF A DEEP LEARNING-BASED TRACHEAL INTUBATION TRAINING SYSTEM
Author(s)
Zhang Wen, MD1, Yu Qing, MD1, Min Zhang2.
1Zhongshan Hospital , Fudan University, shanghai, China, 2assistant researcher, Zhongshan Hospital , Fudan University, SHANGHAI, China.
1Zhongshan Hospital , Fudan University, shanghai, China, 2assistant researcher, Zhongshan Hospital , Fudan University, SHANGHAI, China.
OBJECTIVES: To evaluate the effectiveness, efficiency, and cost-effectiveness of a deep learning-based automated assessment system for tracheal intubation training among resident physicians in a real-world medical education setting.
METHODS: The study includes resident physicians involved in performing tracheal intubation procedures, and the data is collected from the medical training center. Data from January to June 2024 is designated as the control group, while data from July to December 2024 is assigned to the experimental group. The experimental group employs a deep learning-based system for the assessment of tracheal intubation procedures, whereas the control group undergoes evaluation by professional human assessors. Comparative analysis of the two groups is conducted using T-test for inter-group comparisons and C2-test for inter-group comparisons of count data. Incremental cost-effectiveness ratio analysis is employed to evaluate the effectiveness of the management approach.
RESULTS: The control group comprises 103 resident physicians participating in the training, while the experimental group consists of 121 participants. There are no significant differences in age and gender between the two groups. Following the introduction of the deep learning automated assessment system, the success rate of tracheal intubation significantly increased (81.9% vs 73.2%, p=0.017). The average training time for physicians in the experimental group (13.7 minutes vs 18.9 minutes, p>0.05) decreased by 5.2 minutes compared to the control group, but the difference was not statistically significant (P>0.05). The system enables real-time monitoring of physicians' tracheal intubation procedures, facilitating comprehensive and effective evaluation and supervision. Thus, the implementation of this intelligent system not only reduces manpower costs but also enhances training effectiveness, making it cost-effective.
CONCLUSIONS: A deep learning-based automated assessment system enhanced tracheal intubation training outcomes while reducing assessment-related resource use. The system offers a scalable, cost-effective approach to procedural skills training in medical education. Future multicenter studies are warranted to validate its long-term educational and economic impact.
METHODS: The study includes resident physicians involved in performing tracheal intubation procedures, and the data is collected from the medical training center. Data from January to June 2024 is designated as the control group, while data from July to December 2024 is assigned to the experimental group. The experimental group employs a deep learning-based system for the assessment of tracheal intubation procedures, whereas the control group undergoes evaluation by professional human assessors. Comparative analysis of the two groups is conducted using T-test for inter-group comparisons and C2-test for inter-group comparisons of count data. Incremental cost-effectiveness ratio analysis is employed to evaluate the effectiveness of the management approach.
RESULTS: The control group comprises 103 resident physicians participating in the training, while the experimental group consists of 121 participants. There are no significant differences in age and gender between the two groups. Following the introduction of the deep learning automated assessment system, the success rate of tracheal intubation significantly increased (81.9% vs 73.2%, p=0.017). The average training time for physicians in the experimental group (13.7 minutes vs 18.9 minutes, p>0.05) decreased by 5.2 minutes compared to the control group, but the difference was not statistically significant (P>0.05). The system enables real-time monitoring of physicians' tracheal intubation procedures, facilitating comprehensive and effective evaluation and supervision. Thus, the implementation of this intelligent system not only reduces manpower costs but also enhances training effectiveness, making it cost-effective.
CONCLUSIONS: A deep learning-based automated assessment system enhanced tracheal intubation training outcomes while reducing assessment-related resource use. The system offers a scalable, cost-effective approach to procedural skills training in medical education. Future multicenter studies are warranted to validate its long-term educational and economic impact.
Conference/Value in Health Info
2026-09, ISPOR Asia Pacific 2026, Bangkok, Thailand
Value in Health, Volume 55, Issue S1
Code
CO18
Topic
Clinical Outcomes
Topic Subcategory
Comparative Effectiveness or Efficacy
Disease
No Additional Disease & Conditions/Specialized Treatment Areas