ArticleJournal of medical Internet research2026
Value of AI in Critical Care Using Real-World Evidence on Intensive Care Unit Mortality Prediction: Cost-Utility Analysis.
Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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9 authors.
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Abstract
Background: AI-based models for predicting mortality have shown potential for intensive care unit (ICU) patients, but evidence regarding their cost-effectiveness remains limited. Objective: This study aimed to evaluate the population-level cost-utility of an AI-based mortality prediction strategy activated during ICU admission in Korea. Methods: A lifetime Markov model followed a hypothetical cohort of Korean adults from age 19 years in the general-population state to capture ICU admissions, including recurrent ICU admissions, occurring over the lifetime horizon. AI-based mortality risk monitoring and its implementation cost were applied only when an individual entered the ICU state. Model inputs were derived from national claims data, published utility estimates, and cost sources. Outcomes were expressed as incremental cost-effectiveness ratios (ICERs) in Korean won (KRW) per quality-adjusted life year (QALY). Deterministic and probabilistic sensitivity analyses were conducted. Results: The AI-assisted strategy yielded an ICER of 8.37 million KRW (approximately US $6200) per QALY, below the societal willingness-to-pay (WTP) threshold of 40 million KRW (approximately US $29,600) per QALY in Korea. The incremental costs and QALYs were lifetime expected values per member of the general-population starting cohort rather than outcomes per directly monitored ICU admission. Probabilistic sensitivity analysis showed an 83.94% probability of cost-effectiveness at the threshold. One-way sensitivity analysis identified the true-positive rate, AI implementation cost, and postdischarge well-state utility value as the most influential parameters. Additional claims-based analyses indicated that ICU transition pathways and patient age were strongly associated with survival outcomes. Conclusions: Under modeled assumptions, AI-assisted ICU mortality prediction showed potential cost-effectiveness compared with usual care in the Korean critical care setting. These findings should be interpreted as decision-analytic estimates rather than direct evidence that the AI system reduces ICU mortality in real patients. Prospective real-world evaluations are needed to confirm clinical effectiveness and implementation value.
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