Prospective economic evaluation of a predictive artificial intelligence model for sepsis: Effects on hospital costs and return on investment

Early sepsis detection is essential for improving outcomes and reducing costs, but traditional rule-based systems have limited accuracy and real-world evidence for machine-learning alternatives remains scarce. In this context, BIAlert-Sepsis predicts sepsis risk within 24 hours using historical hospital data, and evaluating its implementation in a tertiary hospital setting provides an opportunity to quantify its clinical benefits and economic value. We conducted a retrospective quasi-experimental before–after study including all septic patients admitted from January 2011 to June 2024. The baseline period (Jan 2011 – Mar 2019) was compared with the AI implementation period (Apr 2019 – June 2024), excluding the COVID-19 interval. Outcomes were assessed using adjusted generalized linear models and interrupted time series regression. A hospital-perspective economic evaluation incorporated implementation and maintenance costs, and a 5-year model estimated net benefit and return on investment (ROI), supported by deterministic and probabilistic sensitivity analyses. A total of 8,039 patients were included (6,168 baseline period; 1,871 AI period). Demographic and clinical characteristics were comparable across periods. During the AI period, ICU admissions decreased from 34.4% to 30.4% (adjusted p = 0.001), accompanied by significant reductions of 0.35 ICU days and 0.59 ward days per patient. Mean admission costs declined from 26,517€ to 24,630€ (adjusted p = 0.005). After covariate adjustment, AI implementation was associated with a 26.1–31.1% reduction in mean admission costs across GLM models. Interrupted time series analysis identified a modest immediate cost level change after AI implementation and a larger sustained decline during the post-COVID period. The 5-year economic model projected a cumulative discounted net benefit of 3.55M€ and a 528% ROI. BIAlert-Sepsis was associated with favourable clinical outcomes and lower costs, with economic modelling suggesting early breakeven and positive financial returns.Author summary: Sepsis is a life-threatening condition in which early detection is critical to improving survival and reducing healthcare costs. However, traditional rule-based screening systems often fail to identify high-risk patients early enough. AI offers a promising way to support earlier and more accurate detection.