AI-ASSISTED CPR SKILL ASSESSMENT: EVALUATING EFFECTIVENESS, EFFICIENCY, AND COST-EFFECTIVENESS IN CLINICAL TRAINING
Author(s)
Min Zhang1, Yu Qing, MD2, Zhang Wen, MD2.
1assistant researcher, Zhongshan Hospital , Fudan University, SHANGHAI, China, 2Zhongshan Hospital , Fudan University, shanghai, China.
1assistant researcher, Zhongshan Hospital , Fudan University, SHANGHAI, China, 2Zhongshan Hospital , Fudan University, shanghai, China.
OBJECTIVES: To evaluate the effectiveness, efficiency, and cost-effectiveness of an artificial intelligence-based CPR composite error action recognition system in real-world skill assessments, and to determine its potential to reduce time and labor burden compared with traditional evaluation methods.
METHODS: A vision-based system was constructed through literature research and expert consultation to define 13 single and 74 composite error actions of extracorporeal cardiac compression in CPR. From July to September 2023, we collected 500 CPR action videos in Zhongshan hospital and constructed a fine-grained composite error action set named CPR-Coach. In addition, we proposed a neural network that can predict composite errors while training single-error samples. The control group rated videos through traditional methods, others with the proposed model. T test was used for comparison among groups. This study uses incremental cost effectiveness ratio to analyze the effectiveness of the system.
RESULTS: In the CPR-Coach dataset, our model achieved 88.79% Top-1 Acc and 99.40% Top-3 Acc, which suggests that it can effectively handle composite error recognition tasks. There were no differences in age, gender, educational background and clinical competence between the evaluation experts in groups. Results showed that there is no difference in accuracy between the control group (99.56 %) and the experimental group (99.72 %). But the average time consuming in experimental group (57 mins) was saved by nearly four times compared with the control group (15 mins) (P<0.05). The proposed system has preliminary capabilities to assist decision-making in CPR assessment.
CONCLUSIONS: The CPR composite error action recognition system based on AI, which can support fine-grained action recognition and composite error action recognition tasks under restricted supervision, can effectively assist doctors in CPR skill assessment, which can alleviate the time-consuming and labor-intensive issues of traditional assessment methods. In addition, it can effectively reduce labor costs and time costs without increasing the cost.
METHODS: A vision-based system was constructed through literature research and expert consultation to define 13 single and 74 composite error actions of extracorporeal cardiac compression in CPR. From July to September 2023, we collected 500 CPR action videos in Zhongshan hospital and constructed a fine-grained composite error action set named CPR-Coach. In addition, we proposed a neural network that can predict composite errors while training single-error samples. The control group rated videos through traditional methods, others with the proposed model. T test was used for comparison among groups. This study uses incremental cost effectiveness ratio to analyze the effectiveness of the system.
RESULTS: In the CPR-Coach dataset, our model achieved 88.79% Top-1 Acc and 99.40% Top-3 Acc, which suggests that it can effectively handle composite error recognition tasks. There were no differences in age, gender, educational background and clinical competence between the evaluation experts in groups. Results showed that there is no difference in accuracy between the control group (99.56 %) and the experimental group (99.72 %). But the average time consuming in experimental group (57 mins) was saved by nearly four times compared with the control group (15 mins) (P<0.05). The proposed system has preliminary capabilities to assist decision-making in CPR assessment.
CONCLUSIONS: The CPR composite error action recognition system based on AI, which can support fine-grained action recognition and composite error action recognition tasks under restricted supervision, can effectively assist doctors in CPR skill assessment, which can alleviate the time-consuming and labor-intensive issues of traditional assessment methods. In addition, it can effectively reduce labor costs and time costs without increasing the cost.
Conference/Value in Health Info
2026-09, ISPOR Asia Pacific 2026, Bangkok, Thailand
Value in Health, Volume 55, Issue S1
Code
EE80
Topic
Economic Evaluation
Topic Subcategory
Cost/Cost of Illness/Resource Use Studies
Disease
No Additional Disease & Conditions/Specialized Treatment Areas, SDC: Cardiovascular Disorders (including MI, Stroke, Circulatory)