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

IF 7 2区医学 Q1 BIOLOGY

Computers in biology and medicine Pub Date : 2024-11-02 DOI:10.1016/j.compbiomed.2024.109329

Senlin Lin , Yingyan Ma , Liping Li , Yanwei Jiang , Yajun Peng , Tao Yu , Dan Qian , Yi Xu , Lina Lu , Yingyao Chen , Haidong Zou

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引用次数: 0

Abstract

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.

查看原文本刊更多论文

利用人工智能进行社区盲法眼底疾病筛查的成本效益和成本效用：中国上海的模型研究。

背景：随着人工智能（AI）在疾病筛查中的应用，流程再造也同时发生。在社区盲法眼底疾病筛查中实施人工智能时，是否需要特别强调流程再造，目前尚不清楚：方法：采用决策分析马尔可夫模型进行成本效益和成本效用分析。模型中的社区居民假定队列从 60 岁开始，在 30 个 1 年马尔可夫周期内进行跟踪。模拟队列基于上海数字化眼病筛查项目（SDEDS）的工作数据。比较了三种方案：基于人工分级的远程医疗系统集中筛查（方案 1）、人工智能辅助筛查系统集中筛查（方案 2）和人工智能辅助筛查系统流程再造筛查（方案 3）。主要结果是增量成本效益比（ICER）和增量成本效用比（ICUR）：与方案 1 相比，方案 2 可增加 187.03 年的防盲时间，增加 106.78 QALY，每筛查 10000 人的额外成本为 490010.62 美元，每防盲一年的 ICER 为 2619.98 美元，每 QALY 的 ICUR 为 4589.13 美元。与方案 1 相比，方案 3 可增加 187.03 年的失明避免率和 106.78 QALY，每 1 万人筛查的额外成本为 242313.23 美元，每一年失明避免率的 ICER 为 1295.60 美元，每 QALY 的 ICUR 为 2269.35 美元。尽管方案 2 和方案 3 可被视为具有成本效益，但在预期效果和效用相同的情况下，方案 3 的筛查成本降低了 27.6%，总成本降低了 1.1%。概率敏感性分析表明，在当地人均 GDP 临界值为 1 倍和 3 倍的情况下，方案 3 分别占 69.1% 和 70.3% 的模拟比例：人工智能可以提高筛查的成本效益和成本效用，尤其是在进行流程再造时。因此，强烈建议在实施人工智能时进行流程再造。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers in biology and medicine 工程技术-工程：生物医学

CiteScore

11.70

自引率

10.40%

发文量

1086

审稿时长

74 days

期刊介绍： Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.