Enhancing Egyptian Healthcare Industry Based on Customized Business Intelligence Solution

  • Dalia Ahmed MagdiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)


A new field of customizing business intelligence systems is the healthcare industry. Healthcare providers’ government and companies in Egypt, in a charge to enhance care quality and cost-effective, are progressively approaching IT-based business strategies. The threats of healthcare management and how to achieve clinical integration have been paid attention from national priorities. This step is a priori stage before developing an integrated IT-solution that achieves the strategic goals. Applications of business intelligence (BI) in different areas of the economy have shown to be very quickly growing. In recent years, it has been employed increasingly in Egypt in different areas. BI provides an effective methods and robust development environment for business intelligence in the healthcare domain. This paper discusses the newest and hot topics in two basic dimensions: business intelligence and healthcare management and designs the basis for customized BI deployment in the healthcare industry and proposes a customized version for immature Egyptian healthcare industry environment. Finally, this work reaches a conclusion that classical BI boundaries do not suit the industry of health care without data warehousing. It additionally gives particular directions toward the design of a data warehouse (DW) structure for the Egyptian healthcare field by proposing an iterative incremental approach.


Business intelligence (BI) Healthcare industry Data warehouse Decision support systems 


  1. 1.
    Mettler T, Vimarlund V (2008) Understanding business intelligence in the context of health care. In: Proceedings of the 13th international symposium for health information management research (ISHIMR 2008), At Auckland, New Zealand, pp 61–69, Oct 2022Google Scholar
  2. 2.
    Byrnes J (2012) Driving value: solving the issue of data overload with an executive dashboard. Healthc Financ Manage: J Healthc Financ Manage Assoc 66(10):116–118Google Scholar
  3. 3.
    Cucoranu IC, Parwani AV, West AJ, Romero-Lauro G, Nauman K, Carter AB, … Pantanowitz L (2013) Privacy and security of patient data in the pathology laboratory. J Pathol Inform. 4(1):23–39CrossRefGoogle Scholar
  4. 4.
    HIT Consultant, “Big Ways Big Data Could Add Value to Healthcare,”, last visited, 2/5/2016
  5. 5.
    Ivan ML, Velicanu M, Taranu I (2015) Using business intelligence in healthcare system. In: The 14th international conference on informatics in economy, IE 2015, 30 April-03 May, 2015, Bucharest, Romania, ISSN 22847472Google Scholar
  6. 6.
    Business Intelligence and Analytics for Healthcare, Available at:, last visited in 3/2/2016
  7. 7.
    Burke J, Ingraham R (2008) Path to insight. Health Manage Technol 29(9):12–14Google Scholar
  8. 8.
    Coddington DC, Moore KD (2012) Integrating physician perspectives into business intelligence. Healthc Financ Manage: J Healthc Financ Manage Assoc 66(6):158–160Google Scholar
  9. 9.
    Top Actions for Healthcare Delivery Organization CIOs, 2014: “Avoid 25 Years of Mistakes in Enterprise Data Warehousing”Google Scholar
  10. 10.
    Ferrand D (2010) Towards a business intelligence framework for healthcare safety. J Internet Banking Commer 15(3):SS 1–9Google Scholar
  11. 11.
    Frye GW (2010) Using business intelligence to build optimal decision support. Benefits Compensation Dig 47(2):1–21Google Scholar
  12. 12.
    Sukanesh R, Gautham P, Arunmozhivarman PT, Rajan SP, Vijayprasath S (2010) Cellular phone based biomedical system for health care. In: India communication control and computing technologies (ICCCCT), IEEE international conference 2010, Dept. of Electron. & Commun. Eng., Thiagarajar coll. of Eng., MaduraiGoogle Scholar
  13. 13.
    Ramya V, Palaniappan B, Kumari A (2011) Embedded Patient Monitoring System. Int J Embed Syst Appl (IJESA) 1(2), Dec 2011MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ado A, Aliyu A, Bello SA, Sharifai AG, Gezawa AS (2014) Building a Diabetes Data Warehouse to Support Decision making in healthcare industry. IOSR J Comput Eng (IOSR-JCE) 16(2):138143CrossRefGoogle Scholar
  15. 15.
    Di Bitonto P, Di Tria F, Roselli T, Rossano V, Tangorra F (2014) A data warehouse in an e-health system. In: Proceedings of the 3rd WSEAS international conference on biomedicine and health engineering (BIHE ‘14), pp 87–92, Tenerife, Spain, 10–14 Jan 2014Google Scholar
  16. 16.
    Knabke T, Olbrich S, Fahim S (2014) Impacts of in-memory technology on data warehouse architectures—a prototype implementation in the field of aircraft maintenance and service. In: Advancing the impact of design science: moving from theory to practice, lecture notes in computer science, vol 8463, pp 383–387. Springer, 2014Google Scholar
  17. 17.
    Pedersena TB, Jensena CS, Dyresonb CE (2001) A foundation for capturing and querying complex multidimensional data. Pergamon Inf Syst 26:383–423CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Information System DepartmentFrench University in EgyptCairoEgypt
  2. 2.Computer and Information System DepartmentSadat Academy for Management SciencesCairoEgypt

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