Artificial Neural Networks and risk stratification models in Emergency Departments: The policy maker's perspective. Issue 1 (January 2016)

• Record Type: Journal Article
• Authors: Casagranda, Ivo; Costantino, Giorgio; Falavigna, Greta; Furlan, Raffaello; Ippoliti, Roberto
• Is Part Of: Health policy. Volume 120:Issue 1(2016)
• Page Start: 111
• Page End: 119
• Publication Date: 2016-01
• Subjects: I12 -- D81; Emergency Departments (ED) -- Risk stratification -- Artificial Neural Networks (ANNs) -- Syncope -- Hospital admission; Medical education -- Periodicals; Medical policy -- Periodicals; Delivery of Health Care -- Periodicals; Education, Medical -- Periodicals; Health Education -- Periodicals; Health Planning -- Periodicals; Public Policy -- Periodicals; Enseignement médical -- Périodiques; Politique sanitaire -- Périodiques; Medical education; Medical policy; Periodicals; Electronic journals; Electronic journals; 362.1
• DOI: 10.1016/j.healthpol.2015.12.003
• Languages: English
• ISSNs: 0168-8510

Abstract

Highlights: Emergency Departments have to discriminate patients according to the risk stratification. Correctly classifying patients is a clinical and a managerial issue. The work proposes an innovative risk stratification approach based on neural network. The analysis highlights differences in correct classification of severe outcomes. Policy implication in the adoption of risk models. 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.