Evaluating the Role of Real-Time Business Intelligence Dashboards in Enhancing Healthcare Performance Monitoring
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P117Keywords:
Business Intelligence, Real-Time Business, Dashboards, Healthcare Monitoring, Electronic Health Records, Artificial IntelligenceAbstract
This paper gives a description of the role of Business Intelligence (BI) tools in Healthcare Systems, fostering new innovation in the field of Healthcare Performance Monitoring process. Clinical and administrative problems of the healthcare sectors are impacted by BI tools, which is one of the primary objectives. In addition to this, the study suggests automation of administrative tasks, reduction of cost overheads and objectifying the areas that need to be improved. This also aims at the implementation of instrumental strategies that lead to efficient decision-making to determine the possible future workflow processes to take place. The study highlights a set of information and analytical aspects which determine the development and usability of the real-time dashboards and their application across the healthcare setting. The consideration of healthcare datasets and their implementation across an analytical dashboard is expected to help in enhancing the credibility of administrative bodies to undertake data-driven decisions. However, the issues that lie within its implementation might have a significant impact on the areas of data management, such that maximum compatibility can be attained while sustaining the scalability of operations. Insights into the changes that are revealed through the help of artificial intelligence help in programming the dashboards in such a way that visual insights provide an indication of the effectiveness of decisions that take place across a healthcare setting
References
[1] D. Frempong, Oluwatobi Akinboboye, I. Okoli, E. Afrihyia, and Olasehinde Omolayo, “Real-Time Analytics Dashboards for Decision-Making Using Tableau in Public Sector and Business Intelligence Applications,” Journal of Frontiers in Multidisciplinary Research, vol. 3, no. 2, pp. 65–80, Jan. 2022, doi: https://doi.org/10.54660/.IJFMR.2022.3.2.65-80.
[2] O. Hamed, “Data driven customer segmentation and personalization strategies in modern business intelligence frameworks,” World Journal of Advanced Research and Reviews, vol. 12, no. 3, pp. 711–726, Dec. 2021, doi: https://doi.org/10.30574/wjarr.2021.12.3.0658.
[3] 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. https://doi.org/10.1016/j.techfore.2015.12.019
[4] Yeoh, W., & Koronios, A. (2010). Critical success factors for business intelligence systems. Journal of Computer Information Systems, 50(3), 23–32.
[5] L. J. Basile, N. Carbonara, R. Pellegrino, and U. Panniello, “Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making,” Technovation, vol. 120, p. 102482, Feb. 2022, doi: https://doi.org/10.1016/j.technovation.2022.102482.
[6] A. U. Umana et al., “Data-Driven Project Monitoring: Leveraging Dashboards and KPIs to Track Performance in Technology Implementation Projects,” Journal of Frontiers in Multidisciplinary Research, vol. 3, no. 2, pp. 35–48, 2022, doi: https://doi.org/10.54660/.ijfmr.2022.3.2.35-48.
[7] J. Gartner and C. Lemaire, “Dimensions of performance and related key performance indicators addressed in healthcare organisations: A literature review,” The International Journal of Health Planning and Management, vol. 37, no. 4, Mar. 2022, doi: https://doi.org/10.1002/hpm.3452.
[8] F. Schiavone, D. Leone, A. Caporuscio, and A. Kumar, “Revealing the Role of Intellectual Capital in Digitalized Health networks. a Meso‑level Analysis for Building and Monitoring a KPI Dashboard,” Technological Forecasting and Social Change, vol. 175, p. 121325, Nov. 2021, doi: https://doi.org/10.1016/j.techfore.2021.121325.
[9] C. Olszak, J. Zurada, and P. Weichbroth, “Exploring the Benefits, Challenges, and Opportunities of Collaborative Business Intelligence,” Hawaii International Conference on System Sciences 2024, pp. 278–287, Dec. 2023, Accessed: Oct. 06, 2025. [Online]. Available: https://www.researchgate.net/publication/376956971_Exploring_the_Benefits_Challenges_and_Opportunities_of_Collaborative_Business_Intelligence
[10] M. Maghsoudi and N. Nezafati, “Navigating the acceptance of implementing business intelligence in organizations: A system dynamics approach,” Telematics and Informatics Reports, vol. 11, p. 100070, Sep. 2023, doi: https://doi.org/10.1016/j.teler.2023.100070.
[11] Dowding, D., Merrill, J., Onorato, N., Barreto, E. A., & Russell, D. (2015). Nurses’ experiences and preferences around clinical dashboard design: A mixed-methods study. Journal of Clinical Nursing, 24(5–6), 712–723. https://doi.org/10.1111/jocn.12650
[12] Creswell, J. W., & Creswell, J. D. (2017). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications.
[13] M. N. Laryeafio and O. C. Ogbewe, “Ethical Consideration dilemma: Systematic Review of Ethics in Qualitative Data Collection through Interviews,” Journal of Ethics in Entrepreneurship and Technology, vol. 3, no. 2, pp. 94–110, Aug. 2023, doi: https://doi.org/10.1108/JEET-09-2022-0014.
[14] D. Helminski et al., “Dashboards in Health Care Settings: Protocol for a Scoping Review,” JMIR Research Protocols, vol. 11, no. 3, p. e34894, Mar. 2022, doi: https://doi.org/10.2196/34894.
[15] Al-Araidah, O., Momani, A., & Osman, I. (2011). A simulation modeling approach for improving healthcare system efficiency. Journal of Industrial Engineering and Management, 4(3), 531–547. https://doi.org/10.3926/jiem.2011.v4n3.p531-547
[16] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., … Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
