title: Artificial Neural Networks and risk stratification models in Emergency Departments: The policy maker's perspective.
abstract: The primary goal of Emergency Department (ED) physicians is to discriminate between individuals at low risk, who can be safely discharged, and patients at high risk, who require prompt hospitalization. The problem of correctly classifying patients is an issue involving not only clinical but also managerial aspects, since reducing the rate of admission of patients to EDs could dramatically cut costs. Nevertheless, a trade-off might arise due to the need to find a balance between economic interests and the health conditions of patients. This work considers patients in EDs after a syncope event and presents a comparative analysis between two models: a multivariate logistic regression model, as proposed by the scientific community to stratify the expected risk of severe outcomes in the short and long run, and Artificial Neural Networks (ANNs), an innovative model. The analysis highlights differences in correct classification of severe outcomes at 10 days (98.30% vs. 94.07%) and 1 year (97.67% vs. 96.40%), pointing to the superiority of Neural Networks. According to the results, there is also a significant superiority of ANNs in terms of false negatives both at 10 days (3.70% vs. 5.93%) and at 1 year (2.33% vs. 10.07%). However, considering the false positives, the adoption of ANNs would cause an increase in hospital costs, highlighting the potential trade-off which policy makers might face.
authors.affiliation: Emergency Department, "SS Antonio e Biagio e Cesare Arrigo" Hospital, Alessandria, Italy.
authors.forename: Ivo
authors.initials: I
authors.lastname: Casagranda
authors.affiliation: Internal Medicine Department, "Fondazione IRCCS Ca' Granda" Hospital, Milan, Italy.
authors.forename: Giorgio
authors.initials: G
authors.lastname: Costantino
authors.affiliation: CNR-IRCrES (National Research Council of Italy - Research Institute on Sustainable Economic Growth), Moncalieri (Turin), Italy.
authors.forename: Greta
authors.initials: G
authors.lastname: Falavigna
authors.affiliation: Division of Internal Medicine, Humanitas Research Hospital, Rozzano, Italy; Università degli Studi di Milano, Milan, Italy.
authors.forename: Raffaello
authors.initials: R
authors.lastname: Furlan
authors.affiliation: Scientific Promotion, "SS Antonio e Biagio e Cesare Arrigo" Hospital, Alessandria, Italy; Department of Management, University of Torino, Italy. Electronic address: roberto.ippoliti@unito.it.
authors.forename: Roberto
authors.initials: R
authors.lastname: Ippoliti
mesh_terms: D000288:Administrative Personnel / Q000523:psychology*; D000368:Aged; D004636:Emergency Service, Hospital; D005260:Female; D007722:Health Knowledge, Attitudes, Practice; D006760:Hospitalization; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010351:Patient Discharge; D018570:Risk Assessment / Q000379:methods; D012720:Severity of Illness Index
keywords: Artificial Neural Networks (ANNs); Emergency Departments (ED); Hospital admission; Risk stratification; Syncope