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

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  • Casagranda, Ivo
  • Costantino, Giorgio
  • Falavigna, Greta
  • Furlan, Raffaello
  • Ippoliti, Roberto

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.

Suggested Citation

  • Casagranda, Ivo & Costantino, Giorgio & Falavigna, Greta & Furlan, Raffaello & Ippoliti, Roberto, 2016. "Artificial Neural Networks and risk stratification models in Emergency Departments: The policy maker's perspective," Health Policy, Elsevier, vol. 120(1), pages 111-119.
  • Handle: RePEc:eee:hepoli:v:120:y:2016:i:1:p:111-119
    DOI: 10.1016/j.healthpol.2015.12.003
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    References listed on IDEAS

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    More about this item

    Keywords

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

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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