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

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dc.contributor.authorGiglio, Andres
dc.contributor.authorMacias-Fassio, Eric
dc.contributor.authorSalas-Sosa, Santiago
dc.contributor.authorLopez, David
dc.contributor.authorPruenza, Cristina
dc.contributor.authorMorales, Aythami
dc.contributor.authorBorges-Sa, Marcio
dc.coverage.spatialEstados Unidos
dc.date.accessioned2026-09-07T15:49:05Z
dc.date.available2026-09-07T15:49:05Z
dc.date.issued2026-08-31
dc.description.abstractResumen: 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
dc.identifier.citationPLOS Global Public Health, Vol. 6, N° 8 (2026) 1-17
dc.identifier.doihttps://doi.org/10.1371/journal.pgph.0006059
dc.identifier.issn2767-3375
dc.identifier.orcidhttps://orcid.org/0000-0002-0533-4531
dc.identifier.urihttps://hdl.handle.net/20.500.12254/7731
dc.language.isoen
dc.publisherPLOS
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/cl/
dc.subjectSepsis
dc.subjectArtificial intelligence
dc.subjectEconomic evaluation
dc.titleProspective economic evaluation of a predictive artificial intelligence model for sepsis: Effects on hospital costs and return on investment
dc.typeArticle
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