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Simulating healthcare quality innovation based on a novel medical treatment: The case of Hepatitis-C in Europe

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  • Zsifkovits, Martin
  • Zsifkovits, Johannes
  • Pickl, Stefan W.

Abstract

In 2014, a novel medication for treating Hepatitis C virus (HCV) infections caused severe difficulties for European decision makers in the public medical sector. Even though new drugs cure HCV in nearly all cases, related costs in the short run are extremely high. Thus, the estimation of overall costs for the national healthcare systems was of great importance for profound far-reaching decisions on policies regarding the medication and their reimbursement. As this budget estimation is extremely difficult due to the complexity of the virus spread and the existence of further discomforts that lead to additional costs, a new microsimulation model was developed that considers the problem from an individual's perspective and finally aggregates numbers on the macro level. While developing the model, general insights into the cost burden due to the new medication for the next 3years were generated. Using the introduced model, a decision maker is able to test for impact of one financial unit in several policies in order to maximize the overall benefit for the healthcare system. As initial results imply the need to change current reimbursement strategies in Europe, further research demand is discussed at the end of this article.

Suggested Citation

  • Zsifkovits, Martin & Zsifkovits, Johannes & Pickl, Stefan W., 2016. "Simulating healthcare quality innovation based on a novel medical treatment: The case of Hepatitis-C in Europe," Technological Forecasting and Social Change, Elsevier, vol. 113(PB), pages 454-459.
    • Handle: RePEc:eee:tefoso:v:113:y:2016:i:pb:p:454-459
    DOI: 10.1016/j.techfore.2016.07.013
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    References listed on IDEAS

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    1. Sun-Young Kim & Sue Goldie, 2008. "Cost-Effectiveness Analyses of Vaccination Programmes," PharmacoEconomics, Springer, vol. 26(3), pages 191-215, March.
    2. Kwakkel, Jan H. & Pruyt, Erik, 2013. "Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 419-431.
    3. Alexander Gutfraind & Basmattee Boodram & Nikhil Prachand & Atesmachew Hailegiorgis & Harel Dahari & Marian E Major, 2015. "Agent-Based Model Forecasts Aging of the Population of People Who Inject Drugs in Metropolitan Chicago and Changing Prevalence of Hepatitis C Infections," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-23, September.
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    Cited by:

    1. Škare, Marinko & Soriano, Domingo Riberio & Porada-Rochoń, Małgorzata, 2021. "Impact of COVID-19 on the travel and tourism industry," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    2. Kim, Rachel H. & Gaukler, Gary M. & Lee, Chang Won, 2016. "Improving healthcare quality: A technological and managerial innovation perspective," Technological Forecasting and Social Change, Elsevier, vol. 113(PB), pages 373-378.

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