Vithlani Jai, Hawksworth Claire, Elvidge Jamie, Ayiku Lynda, Dawoud Dalia
National Institute for Health and Care Excellence, London, United Kingdom.
National Institute for Health and Care Excellence, Manchester, United Kingdom.
Front Pharmacol. 2023 Aug 8;14:1220950. doi: 10.3389/fphar.2023.1220950. eCollection 2023.
Health economic evaluations (HEEs) help healthcare decision makers understand the value of new technologies. Artificial intelligence (AI) is increasingly being used in healthcare interventions. We sought to review the conduct and reporting of published HEEs for AI-based health interventions. We conducted a systematic literature review with a 15-month search window (April 2021 to June 2022) on 17 June 2022 to identify HEEs of AI health interventions and update a previous review. Records were identified from 3 databases (Medline, Embase, and Cochrane Central). Two reviewers screened papers against predefined study selection criteria. Data were extracted from included studies using prespecified data extraction tables. Included studies were quality assessed using the National Institute for Health and Care Excellence (NICE) checklist. Results were synthesized narratively. A total of 21 studies were included. The most common type of AI intervention was automated image analysis (9/21, 43%) mainly used for screening or diagnosis in general medicine and oncology. Nearly all were cost-utility (10/21, 48%) or cost-effectiveness analyses (8/21, 38%) that took a healthcare system or payer perspective. Decision-analytic models were used in 16/21 (76%) studies, mostly Markov models and decision trees. Three (3/16, 19%) used a short-term decision tree followed by a longer-term Markov component. Thirteen studies (13/21, 62%) reported the AI intervention to be cost effective or dominant. Limitations tended to result from the input data, authorship conflicts of interest, and a lack of transparent reporting, especially regarding the AI nature of the intervention. Published HEEs of AI-based health interventions are rapidly increasing in number. Despite the potentially innovative nature of AI, most have used traditional methods like Markov models or decision trees. Most attempted to assess the impact on quality of life to present the cost per QALY gained. However, studies have not been comprehensively reported. Specific reporting standards for the economic evaluation of AI interventions would help improve transparency and promote their usefulness for decision making. This is fundamental for reimbursement decisions, which in turn will generate the necessary data to develop flexible models better suited to capturing the potentially dynamic nature of AI interventions.
卫生经济评估（HEEs）有助于医疗保健决策者理解新技术的价值。人工智能（AI）在医疗保健干预中的应用日益广泛。我们旨在回顾已发表的基于人工智能的卫生干预措施的卫生经济评估的开展情况和报告情况。2022年6月16日，我们进行了一项系统的文献综述，搜索窗口为15个月（2021年4月至2022年6月），以确定人工智能卫生干预措施的卫生经济评估，并更新之前的综述。从3个数据库（Medline、Embase和Cochrane Central）中检索记录。两名评审员根据预先确定的研究选择标准筛选论文。使用预先指定的数据提取表从纳入的研究中提取数据。使用英国国家卫生与临床优化研究所（NICE）清单对纳入的研究进行质量评估。结果进行了叙述性综合。共纳入21项研究。最常见的人工智能干预类型是自动图像分析（9/21，43%），主要用于普通医学和肿瘤学的筛查或诊断。几乎所有研究都是成本效用分析（10/21，48%）或成本效益分析（8/21，38%），采用的是医疗保健系统或支付方的视角。16/21（76%）的研究使用了决策分析模型，则主要是马尔可夫模型和决策树。三项研究（3/16，19%）使用了短期决策树，随后是长期马尔可夫组件。13项研究（13/21，62%）报告称人工智能干预具有成本效益或占主导地位。局限性往往源于输入数据、作者利益冲突以及缺乏透明报告，尤其是关于干预措施的人工智能性质方面。已发表的基于人工智能的卫生干预措施的卫生经济评估数量正在迅速增加。尽管人工智能具有潜在的创新性，但大多数研究都使用了马尔可夫模型或决策树等传统方法。大多数研究试图评估对生活质量的影响，以呈现每获得一个质量调整生命年的成本。然而，研究报告并不全面。人工智能干预措施经济评估的具体报告标准将有助于提高透明度，并促进其在决策中的实用性。这对于报销决策至关重要，而报销决策反过来又将产生必要的数据，以开发更适合捕捉人工智能干预措施潜在动态性质的灵活模型。
