Artificial Neural Networks and risk stratification models in Emergency Departments: The policy maker's perspective

I. Casagranda, Giorgio Costantino, Greta Falavigna, Raffaello Furlan, Roberto Ippoliti

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)111-119
Number of pages9
JournalHealth Policy
Issue number1
Publication statusPublished - Jan 1 2016


  • Artificial Neural Networks (ANNs)
  • Emergency Departments (ED)
  • Hospital admission
  • Risk stratification
  • Syncope

ASJC Scopus subject areas

  • Health Policy


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