{ "abstract": "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.", "abstract_shown": true, "affiliations": [ "Multidisciplary Sepsis Unit, Son Llatzer Hospital, Palma, Spain", "Multidisciplinary Sepsis Group, Health Research Intitute of the Balearic Islands (IdISBa), Palma, Spain", "University of the Balearic Islands, Palma, Spain", "Fundación Código Sepsis, Valencia, Spain", "Faculty of Medicine, Finis Terrae University, Santiago, Chile", "Critical Care Center, Clínica Las Condes Hospital, Santiago, Chile", "BiometricsAI, Universidad Autónoma de Madrid, Madrid, Spain" ], "article_type": "Research Article", "arxiv": null, "author_total": 7, "authors": [ { "affs": [ 0 ], "name": "Andres Giglio" }, { "affs": [ 1 ], "name": "Eric Macias-Fassio" }, { "affs": [ 2 ], "name": "Santiago Salas-Sosa" }, { "affs": [ 3 ], "name": "David Lopez" }, { "affs": [ 4 ], "name": "Cristina Pruenza" }, { "affs": [ 5 ], "name": "Aythami Morales" }, { "affs": [ 6 ], "name": "Marcio Borges-Sa" } ], "bibtex": "https://research.draconitic.com/paper/585dnz2mk3tzj.bib", "cited_by": 0, "doi": "10.1371/journal.pgph.0006059", "fulltext_url": "https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0006059", "id": "585dnz2mk3tzj", "issns": [ "2767-3375" ], "landing_url": "https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0006059", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "2713|Sepsis Diagnosis and Treatment" ], [ "plos", "/Computer and information sciences/Artificial intelligence" ], [ "plos", "/Medicine and health sciences/Clinical medicine/Signs and symptoms/Sepsis" ], [ "plos", "/Medicine and health sciences/Health care/Health care facilities/Hospitals" ], [ "plos", "/Medicine and health sciences/Health care/Health care facilities/Hospitals/Intensive care units" ], [ "plos", "/Medicine and health sciences/Health care/Health economics" ], [ "plos", "/Physical sciences/Mathematics/Statistics/Statistical methods/Monte Carlo method" ], [ "plos", "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Monte Carlo method" ], [ "plos", "/Social sciences/Economics" ], [ "plos", "/Social sciences/Economics/Economic analysis/Economic impact analysis" ], [ "plos", "/Social sciences/Economics/Health economics" ] ], "openalex": "W4417286564", "pdf_url": "https://journals.plos.org/globalpublichealth/article/file?id=10.1371/journal.pgph.0006059&type=printable", "pmcid": null, "pmid": null, "published": "2026-08-31", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/585dnz2mk3tzj.ris", "sources": [ { "first_seen": "2026-10-30T12:18:02Z", "native_id": "10.1371/journal.pgph.0006059", "source": "plos", "url": "https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0006059" } ], "subjects": [ { "field": "mathematics", "origin": "plos:statistical methods", "subfield": "statistics" } ], "title": "Prospective economic evaluation of a predictive artificial intelligence model for sepsis: Effects on hospital costs and return on investment", "title_text": "Prospective economic evaluation of a predictive artificial intelligence model for sepsis: Effects on hospital costs and return on investment", "type": "peer-reviewed", "updated_at": "2026-10-30T12:18:02Z", "url": "https://research.draconitic.com/paper/585dnz2mk3tzj", "venue": "PLOS Global Public Health", "versions": [] }