Scenario planning for emergent technology (big data & cloud) in healthcare industry
Keywords:
big data, scenario planning, health care industry, emergent technology.
Abstract
The emergence of big data, as well as advancements in data science approaches and technology, is providing pharmaceutical companies with an opportunity to gain novel insights that can enhance and accelerate drug development. It will increasingly help government, health agencies, players, and providers to make decisions about such issues as drug discovery, patient access, and marketing. In this paper we use scenario planning tools and system dynamic to evaluate the impact of emergent technology such as big data &cloud &in healthcare industry. In this case we have four scenarios of big data and emergent tech transformation in Iran health care industry.
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References
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d'Arcy, M., O'Hanlon, M. E., Orszag, P. R., Shapiro, J., & Steinberg, J. B. (2007). Protecting the Homeland 2006/2007. Brookings Institution Press.
Hoffman, B. (2002). Lessons of 9/11: Testimony before the United States Joint September 11, 2001 Inquiry Staff of the House and Senate Select Committees on Intelligence on October 8, 2002. Arlington, VA: Rand Corporation. (Rand Corporation Document No. CT-201).
Lynch, M. D. (2005). Developing a scenario-based training program: Giving officers a tactical advantage. FBI L. Enforcement Bull., 74, 1.
ead-Gordon, M., & Head-Gordon, T. (1994). Analytic MP2 frequencies without fifth-order storage. Theory and application to bifurcated hydrogen bonds in the water hexamer. Chemical Physics Letters, 220(1-2), 122-128.
Weimer-Jehle, W. (2008). Scenariowizard Basic2. 3. Zim: university of Stuttgart Publ., Germany.
Arcade, J., Godet, M., Meunier, F., & Roubelat, F. (2003). Structral Analysis,“Structural analysis with the MICMAC method & Actors' strategy with MACTOR method”, CD ROM, the Millennium Project. Laboratory for Investigation in Prospective and Strategy (LIPS).
Asan, S. S., & Asan, U. (2007). Qualitative cross-impact analysis with time consideration. Technological Forecasting and Social Change, 74(5), 627-644.
Godet, M., & Roubelat, F. (1996). Creating the future: the use and misuse of scenarios. Long range planning, 29(2), 164-171.
Schwarz, P. (1991). The art of the long view: planning for the future in an uncertain world. Currency Doubleday, New York.
Copping, R., & Li, M. (2016). Analytics: The promise and challenge of big data for pharma. Harvard Business Review.
Costa, F. F. (2013). Social networks, web-based tools and diseases: implications for biomedical research. Drug Discovery Today, 18(5-6), 272-281.
Costa, F. F. (2014). Big data in biomedicine. Drug discovery today, 19(4), 433-440.
Knoppers, B. M., Zawati, M. N. H., & Kirby, E. S. (2012). Sampling populations of humans across the world: ELSI issues. Annual review of genomics and human genetics, 13, 395-413.
Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13-53.
Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare information management, 19(2), 65.
Villars, R. L., Olofson, C. W., & Eastwood, M. (2011). Big data: What it is and why you should care. White Paper, IDC, 14, 1-14.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13.
Published
2018-08-30
How to Cite
Akhlaghinia, N., rajabzadeh, A., Yazdian, A., & Moghbel, A. (2018). Scenario planning for emergent technology (big data & cloud) in healthcare industry. Amazonia Investiga, 7(15), 299-305. Retrieved from https://www.amazoniainvestiga.info/index.php/amazonia/article/view/461
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