Cost-effectiveness and cost-utility of community-based blinding fundus diseases screening with artificial intelligence: A modelling study from Shanghai, China

被引：0
作者：
Lin, Senlin [1 ,2 ,3 ]
Ma, Yingyan [1 ,2 ,3 ,4 ]
Li, Liping [5 ]
Jiang, Yanwei [5 ]
Peng, Yajun [1 ,2 ,3 ]
Yu, Tao [1 ,2 ,3 ]
Qian, Dan [6 ]
Xu, Yi [1 ,2 ,3 ]
Lu, Lina [1 ,2 ,3 ]
Chen, Yingyao [7 ]
Zou, Haidong [1 ,2 ,3 ,4 ]
机构：
[1] Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, No. 1440, Hongqiao Road, Shanghai
[2] National Clinical Research Center for Eye Diseases, No. 1440, Hongqiao Road, Shanghai
[3] Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, No. 1440, Hongqiao Road, Shanghai
[4] Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, No. 85/86, Wujin Road, Shanghai
[5] Shanghai Hongkou Center for Disease Control and Prevention, No. 197, Changyang Road, Shanghai
[6] Eye and Dental Diseases Prevention and Treatment Center of Pudong New Area, No. 222, Wenhua Road, Shanghai
[7] School of Public Health, Fudan University, No. 130, Dong'an Road, Shanghai
来源：
Computers in Biology and Medicine | 2024年 / 183卷
关键词：
Artificial intelligence; Blinding eye disease; Health economic evaluation; Process reengineering; Screening;
D O I：
10.1016/j.compbiomed.2024.109329
中图分类号：
学科分类号：
摘要：
Background: With application of artificial intelligence (AI) in the disease screening, process reengineering occurred simultaneously. Whether process reengineering deserves special emphasis in AI implementation in the community-based blinding fundus diseases screening is not clear. Method: Cost-effectiveness and cost-utility analyses were performed employing decision-analytic Markov models. A hypothetical cohort of community residents was followed in the model over a period of 30 1-year Markov cycles, starting from the age of 60. The simulated cohort was based on work data of the Shanghai Digital Eye Disease Screening program (SDEDS). Three scenarios were compared: centralized screening with manual grading-based telemedicine systems (Scenario 1), centralized screening with an AI-assisted screening system (Scenario 2), and process reengineered screening with an AI-assisted screening system (Scenario 3). The main outcomes were incremental cost-effectiveness ratio (ICER) and incremental cost-utility ratio (ICUR). Results: Compared with Scenario 1, Scenario 2 results in incremental 187.03 years of blindness avoided and incremental 106.78 QALYs at an additional cost of $ 490010.62 per 10,000 people screened, with an ICER of $2619.98 per year of blindness avoided and an ICUR of $4589.13 per QALY. Compared with Scenario 1, Scenario 3 results in incremental 187.03 years of blindness avoided and incremental 106.78 QALYs at an additional cost of $242313.23 per 10,000 people screened, with an ICER of $1295.60 per year of blindness avoided and an ICUR of $2269.35 per QALY. Although Scenario 2 and 3 could be considered cost-effective, the screening cost of Scenario 3 was 27.6 % and the total cost was 1.1 % lower, with the same expected effectiveness and utility. The probabilistic sensitivity analyses show that Scenario 3 dominated 69.1 % and 70.3 % of simulations under one and three times the local GDP per capita thresholds. Conclusions: AI can improve the cost-effectiveness and cost-utility of screenings, especially when process reengineering is performed. Therefore, process reengineering is strongly recommended when AI is implemented. © 2024
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