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

Listed author(s):
  • Casagranda, Ivo
  • Costantino, Giorgio
  • Falavigna, Greta
  • Furlan, Raffaello
  • Ippoliti, Roberto

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.

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Article provided by Elsevier in its journal Health Policy.

Volume (Year): 120 (2016)
Issue (Month): 1 ()
Pages: 111-119

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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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  10. Lee, Albert & Hazlett, Clarke B. & Chow, S. & Lau, Fei-lung & Kam, Chak-wah & Wong, Patrick & Wong, Tai-wai, 2003. "How to minimize inappropriate utilization of Accident and Emergency Departments: improve the validity of classifying the general practice cases amongst the A&E attendees," Health Policy, Elsevier, vol. 66(2), pages 159-168, November.
  11. Al, Maiwenn J. & Feenstra, Talitha & Brouwer, Werner B. F., 2004. "Decision makers' views on health care objectives and budget constraints: results from a pilot study," Health Policy, Elsevier, vol. 70(1), pages 33-48, October.
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