Hsieh Ping-Hsuan, Lin Chin, Lin Chin-Sheng, Liu Wei-Ting, Lin Tsung-Kun, Tsai Dung-Jang, Hung Yi-Jen, Chen Yuan-Hao, Lin Chih-Yuan, Lin Shih-Hua, Tsai Chien-Sung
School of Pharmacy, National Defense Medical Center, Taipei, Taiwan, ROC.
School of Medicine, National Defense Medical Center, Taipei, Taiwan, ROC.
NPJ Digit Med. 2025 Jun 11;8(1):348. doi: 10.1038/s41746-025-01735-7.
Findings from a previous study (ClinicalTrials.gov: NCT05118035) demonstrated that an AI-enabled electrocardiogram (AI-ECG), combining AI reports and physician alerts, effectively identified hospitalized patients at high risk of mortality and reduced all-cause mortality. This study evaluates its cost-effectiveness from the health payer's perspective in Taiwan over a 90-day post-intervention period. Cost data were obtained from electronic health records of participating hospitals, and incremental cost-effectiveness ratios (ICERs) per death averted were calculated. Non-parametric bootstrap techniques were used to address uncertainty. Among 15,965 patients, 90-day all-cause mortality was 3.6% in the intervention group versus 4.3% in controls. Medication and ICU costs were higher in the AI-ECG group, but overall medical cost was similar ($6204 vs. $5803). The ICER was $59,500 (95% CI: $-4657 to $385,950) per death averted. The cost-effectiveness acceptability curve showed that 95% of the probability mass lies below a willingness-to-pay threshold of $409,321, supporting favorable cost-effectiveness despite uncertainty.
先前一项研究（ClinicalTrials.gov：NCT05118035）的结果表明，一种结合人工智能报告和医生警报的人工智能心电图（AI-ECG）能够有效识别住院的高死亡风险患者，并降低全因死亡率。本研究从台湾地区医疗支付方的角度评估了其在干预后90天内的成本效益。成本数据来自参与研究医院的电子健康记录，并计算了每避免一例死亡的增量成本效益比（ICER）。采用非参数自助法来处理不确定性。在15965名患者中，干预组90天全因死亡率为3.6%，对照组为4.3%。AI-ECG组的药物和重症监护病房成本较高，但总体医疗成本相似（6204美元对5803美元）。每避免一例死亡的ICER为59500美元（95%CI：-4657美元至385950美元）。成本效益可接受性曲线显示，95%的概率质量位于409321美元的支付意愿阈值以下，这表明尽管存在不确定性，但成本效益仍较为可观。
