Prospective economic evaluation of a predictive artificial intelligence model for sepsis: Effects on hospital costs and return on investment
| dc.contributor.author | Giglio, Andres | |
| dc.coverage.spatial | Estados Unidos | |
| dc.date.accessioned | 2026-09-07T15:49:05Z | |
| dc.date.available | 2026-09-07T15:49:05Z | |
| dc.date.issued | 2026-08-31 | |
| dc.description.abstract | Resumen: 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.citation | PLOS Global Public Health, Vol. 6, N° 8 (2026) 1-17 | |
| dc.identifier.doi | https://doi.org/10.1371/journal.pgph.0006059 | |
| dc.identifier.issn | 2767-3375 | |
| dc.identifier.orcid | https://orcid.org/0000-0002-0533-4531 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12254/7731 | |
| dc.language.iso | eng | |
| dc.publisher | PLOS | |
| dc.rights | Acceso abierto | |
| dc.rights.license | Atribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL) | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/3.0/cl/ | |
| dc.subject | Sepsis | |
| dc.subject.nabs | 07 - Protección y mejora de la salud humana | |
| dc.subject.ods | ODS 3 - Salud y bienestar | |
| dc.subject.oecd | 3.2.28 - Otros Temas de Medicina Clínica||1.2.1 - Ciencias de la Computación | |
| dc.subject.tech | IA - Inteligencia Artificial||SD - Salud Digital | |
| dc.title | Prospective economic evaluation of a predictive artificial intelligence model for sepsis: Effects on hospital costs and return on investment | |
| dc.type | Article | |
| dc.type.coar | http://purl.org/coar/resource_type/c_6501 |