IDEAS home Printed from
   My bibliography  Save this article

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


  • 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.

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

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Olmeda, Ignacio & Fernandez, Eugenio, 1997. "Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 10(4), pages 317-335, November.
    2. Vandoros, Sotiris & Stargardt, Tom, 2013. "Reforms in the Greek pharmaceutical market during the financial crisis," Health Policy, Elsevier, vol. 109(1), pages 1-6.
    3. Greta Falavigna, 2008. "New contents and perspectives in the risk analysis of enterprises," International Journal of Business Performance Management, Inderscience Enterprises Ltd, vol. 10(2/3), pages 136-173.
    4. Cheng, Amy H.Y. & Sutherland, Jason M., 2013. "British Columbia's pay-for-performance experiment: Part of the solution to reduce emergency department crowding?," Health Policy, Elsevier, vol. 113(1), pages 86-92.
    5. White, Joseph, 2013. "Budget-makers and health care systems," Health Policy, Elsevier, vol. 112(3), pages 163-171.
    6. Quaglio, GianLuca & Karapiperis, Theodoros & Van Woensel, Lieve & Arnold, Elleke & McDaid, David, 2013. "Austerity and health in Europe," Health Policy, Elsevier, vol. 113(1), pages 13-19.
    7. de Andres, Javier & Landajo, Manuel & Lorca, Pedro, 2005. "Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case," European Journal of Operational Research, Elsevier, vol. 167(2), pages 518-542, December.
    8. De Vos, Pol & Orduñez-García, Pedro & Santos-Peña, Moisés & Van der Stuyft, Patrick, 2010. "Public hospital management in times of crisis: Lessons learned from Cienfuegos, Cuba (1996-2008)," Health Policy, Elsevier, vol. 96(1), pages 64-71, June.
    9. McDonagh, Marian S. & Smith, David H. & Goddard, Maria, 2000. "Measuring appropriate use of acute beds: A systematic review of methods and results," Health Policy, Elsevier, vol. 53(3), pages 157-184, October.
    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.
    12. Harper, Paul R., 2005. "A review and comparison of classification algorithms for medical decision making," Health Policy, Elsevier, vol. 71(3), pages 315-331, March.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Greta Falavigna & Roberto Ippoliti & Marinella Bertolotti & Franca Riva & Antonio Maconi, 2017. "Supportare la ricerca e l’innovazione in sanità tramite i modelli organizzativi: il caso dell’Azienda Ospedaliera “SS. Antonio e Biagio e Cesare Arrigo” di Alessandria/Supporting research and innovati," IRCrES Working Paper 201709, Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.

    More about this item


    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


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:hepoli:v:120:y:2016:i:1:p:111-119. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu) or (). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.