Economics at your fingertips  

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

Ivo Casagranda, Giorgio Costantino, Greta Falavigna, Raffaello Furlan and Roberto Ippoliti ()

Health Policy, 2016, vol. 120, issue 1, 111-119

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.

Keywords: Emergency Departments (ED); Risk stratification; Artificial Neural Networks (ANNs); Syncope; Hospital admission (search for similar items in EconPapers)
JEL-codes: I12 D81 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.1016/j.healthpol.2015.12.003

Access Statistics for this article

Health Policy is currently edited by Katrien Kesteloot, Mia Defever and Irina Cleemput

More articles in Health Policy from Elsevier
Bibliographic data for series maintained by Haili He () and ().

Page updated 2020-11-11
Handle: RePEc:eee:hepoli:v:120:y:2016:i:1:p:111-119