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Towards an implementation framework for business intelligence in healthcare

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  • Foshay, Neil
  • Kuziemsky, Craig

Abstract

As healthcare organizations continue to be asked to do more with less, access to information is essential for sound evidence-based decision making. Business intelligence (BI) systems are designed to deliver decision-support information and have been repeatedly shown to provide value to organizations. Many healthcare organizations have yet to implement BI systems and no existing research provides a healthcare-specific framework to guide implementation. To address this research gap, we employ a case study in a Canadian Health Authority in order to address three questions: (1) what are the most significant adverse impacts to the organization's decision processes and outcomes attributable to a lack of decision-support capabilities? (2) what are the root causes of these impacts, and what workarounds do they necessitate? and (3) in light of the issues identified, what are the key considerations for healthcare organizations in the early stages of BI implementation? Using the concept of co-agency as a guide we identified significant decision-related adverse impacts and their root causes. We found strong management support, the right skill sets and an information-oriented culture to be key implementation considerations. Our major contribution is a framework for defining and prioritizing decision-support information needs in the context of healthcare-specific processes.

Suggested Citation

  • Foshay, Neil & Kuziemsky, Craig, 2014. "Towards an implementation framework for business intelligence in healthcare," International Journal of Information Management, Elsevier, vol. 34(1), pages 20-27.
    • Handle: RePEc:eee:ininma:v:34:y:2014:i:1:p:20-27
    DOI: 10.1016/j.ijinfomgt.2013.09.003
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    References listed on IDEAS

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    1. Brooks, Patti & El-Gayar, Omar & Sarnikar, Surendra, 2015. "A framework for developing a domain specific business intelligence maturity model: Application to healthcare," International Journal of Information Management, Elsevier, vol. 35(3), pages 337-345.
    2. Jaklič, Jurij & Grublješič, Tanja & Popovič, Aleš, 2018. "The role of compatibility in predicting business intelligence and analytics use intentions," International Journal of Information Management, Elsevier, vol. 43(C), pages 305-318.
    3. Ippolito, Adelaide & Sorrentino, Marco & Guardato, Luisa & Marcello, Raffaele & Paolone, Giuseppe, 2024. "The paradoxes of the reengineering of information flows for management control: A case study in a public university hospital," International Journal of Accounting Information Systems, Elsevier, vol. 53(C).
    4. Basile, Luigi Jesus & Carbonara, Nunzia & Pellegrino, Roberta & Panniello, Umberto, 2023. "Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making," Technovation, Elsevier, vol. 120(C).
    5. Farzad Tarhani & Omid Zare Ameli, 2016. "Business Intelligence Application Model in Hedge Funds Supporting Knowledge-Based Companies," Modern Applied Science, Canadian Center of Science and Education, vol. 10(12), pages 137-137, December.
    6. Li, Manning & Mao, Jiye, 2015. "Hedonic or utilitarian? Exploring the impact of communication style alignment on user's perception of virtual health advisory services," International Journal of Information Management, Elsevier, vol. 35(2), pages 229-243.

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