Volume 5 Number 1 2024
Strategic Role of Business Analytics in Healthcare Systems Performance Optimization
Al Akhir1*, Fahim Rahman2, Ariful Islam1, Niladry Chowdhury3, Md Sakib Mia3, Md Iqbal Hossain4
Journal of Primeasia 5 (1) 1-8 https://doi.org/10.25163/primeasia.5110347
Submitted: 18 December 2023 Revised: 18 February 2024 Published: 20 February 2024
Abstract
Background: Business analytics (BA) has become an increasingly disruptive force to healthcare organizations with respect to evidence-based decision-making, reducing costs, and improving patient outcomes. While Canadian healthcare systems are becoming ever-more complicated, BA has potential strategic value to healthcare organizations when optimizing performance. Methods: A descriptive cross-sectional survey was administered to 115 respondents within hospitals, public health, clinics, and private health organizations. The survey instrument accessed BA adoption within key performance areas, BA tools and techniques, and respondents’ perceived overall effect. Data were analyzed using descriptive statistics for frequency and percentage distributions. Results: After BA was implemented, 83.5% of participants reported improved patient care quality, and 76.5% reported reduced operational costs. Analytics-based disease trend forecasting received 64.3% adoption from study respondents. Respondents implemented predictive analytics models at the highest frequency (73%), with data visualizations dashboards (67%), and machine learning algorithms (60%). Most respondents (78.2%) rated overall assessments of BA High or Very High. Conclusion: Business analytics (BA) is an indispensable process toward improving healthcare service delivery models while also determining better resource management and operational efficiency practices. The immediate feedback received by respondents demonstrate the need for healthcare organizations to expand BA uptake while also engaging in new analytical techniques to meet the healthcare demands of today.
Keywords: Business Analytics, Healthcare Performance, Predictive Analytics, Operational Efficiency, Patient Care Optimization.
References
Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2023). Re-Thinking Data Strategy and Integration for Artificial intelligence: Concepts, opportunities, and challenges. Applied Sciences, 13(12), 7082. https://doi.org/10.3390/app13127082
Alghamdi, N. A., & Al-Baity, H. H. (2022). Augmented Analytics Driven by AI: A Digital Transformation beyond Business Intelligence. Sensors, 22(20), 8071. https://doi.org/10.3390/s22208071
Al-Okaily, A., Teoh, A. P., & Al-Okaily, M. (2023). Evaluation of data analytics-oriented business intelligence technology effectiveness: an enterprise-level analysis. Business Process Management Journal, 29(3), 777–800. https://doi.org/10.1108/bpmj-10-2022-0546
Althabatah, A., Yaqot, M., Menezes, B., & Kerbache, L. (2023). Transformative Procurement Trends: Integrating Industry 4.0 technologies for enhanced procurement processes. Logistics, 7(3), 63. https://doi.org/10.3390/logistics7030063
Antunes, A. L., Cardoso, E., & Barateiro, J. (2022). Incorporation of Ontologies in Data Warehouse/Business Intelligence Systems - A Systematic Literature review. International Journal of Information Management Data Insights, 2(2), 100131. https://doi.org/10.1016/j.jjimei.2022.100131
Azmi, M., Mansour, A., & Azmi, C. (2023). A Context-Aware Empowering Business with AI: Case of Chatbots in Business Intelligence Systems. Procedia Computer Science, 224, 479–484. https://doi.org/10.1016/j.procs.2023.09.068
Bag, S., Gupta, S., Choi, T., & Kumar, A. (2021). Roles of Innovation Leadership on Using big data analytics to establish resilient healthcare supply chains to combat the COVID-19 pandemic: a multimethodological study. IEEE Transactions on Engineering Management, 71, 13213–13226. https://doi.org/10.1109/tem.2021.3101590
Bahrami, M., & Shokouhyar, S. (2021). The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view. Information Technology and People, 35(5), 1621–1651. https://doi.org/10.1108/itp-01-2021-0048
Boda, V. V. R. (2021). Running Healthcare Systems Smoothly: DevOps Tips and Tricks You Can Use., 2(1), 49–59. https://doi.org/10.63282/3050-9246.ijetcsit-v2i3p106
Cannavale, C., Tammaro, A. E., Leone, D., & Schiavone, F. (2022). Innovation adoption in inter-organizational healthcare networks – the role of artificial intelligence. European Journal of Innovation Management, 25(6), 758–774. https://doi.org/10.1108/ejim-08-2021-0378
Cardoso, E., & Su, X. (2022). Designing a Business Intelligence and Analytics Maturity Model for Higher Education: A Design Science Approach. Applied Sciences, 12(9), 4625. https://doi.org/10.3390/app12094625
Dahiya, R., Le, S., Ring, J. K., & Watson, K. (2021). Big data analytics and competitive advantage: the strategic role of firm-specific knowledge. Journal of Strategy and Management, 15(2), 175–193. https://doi.org/10.1108/jsma-08-2020-0203
Demirdögen, G., Isik, Z., & Arayici, Y. (2022). Determination of business intelligence and Analytics-Based healthcare facility Management key performance indicators. Applied Sciences, 12(2), 651. https://doi.org/10.3390/app12020651
Fisher, S., & Rosella, L. C. (2022). Priorities for successful use of artificial intelligence by public health organizations: a literature review. BMC Public Health, 22(1). https://doi.org/10.1186/s12889-022-14422-z
Ghazal, T. M., & Taleb, N. (2022). Feature optimization and identification of ovarian cancer using internet of medical things. Expert Systems, 39(9). https://doi.org/10.1111/exsy.12987
Himeur, Y., Elnour, M., Fadli, F., Meskin, N., Petri, I., Rezgui, Y., Bensaali, F., & Amira, A. (2022). AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives. Artificial Intelligence Review, 56(6), 4929–5021. https://doi.org/10.1007/s10462-022-10286-2
Horani, O. M., Khatibi, A., Al-Soud, A. R., Tham, J., & Al-Adwan, A. S. (2023). Determining the factors influencing business analytics adoption at organizational level: A systematic literature review. Big Data and Cognitive Computing, 7(3), 125. https://doi.org/10.3390/bdcc7030125
Jankovic, S. D., & Curovic, D. M. (2023). Strategic Integration of Artificial Intelligence for Sustainable Businesses: Implications for data management and human user engagement in the Digital Era. Sustainability, 15(21), 15208. https://doi.org/10.3390/su152115208
Kumar, M., Kumar, A., Verma, S., Bhattacharya, P., Ghimire, D., Kim, S., & Hosen, A. S. M. S. (2023). Healthcare Internet of Things (H-IoT): current trends, future prospects, applications, challenges, and security issues. Electronics, 12(9), 2050. https://doi.org/10.3390/electronics12092050
Maaitah, T. (2023). The role of business intelligence tools in the decision making process and performance. Journal of Intelligence Studies in Business, 13(1), 43–52. https://doi.org/10.37380/jisib.v13i1.990
Maaitah, T. (2023). The role of business intelligence tools in the decision making process and performance. Journal of Intelligence Studies in Business, 13(1), 43–52. https://doi.org/10.37380/jisib.v13i1.990
Morr, C. E., & Ali-Hassan, H. (2019). Healthcare, data analytics, and business intelligence. In SpringerBriefs in health care management and economics (pp. 1–13). https://doi.org/10.1007/978-3-030-04506-7_1
Onasanya, A., & Elshakankiri, M. (2019). Smart integrated IoT healthcare system for cancer care. Wireless Networks, 27(6), 4297–4312. https://doi.org/10.1007/s11276-018-01932-1
Perifanis, N., & Kitsios, F. (2023). Investigating the Influence of Artificial intelligence on business Value in the digital Era of Strategy: a literature review. Information, 14(2), 85. https://doi.org/10.3390/info14020085
Schmitt, M. (2022). Deep learning in business analytics: A clash of expectations and reality. International Journal of Information Management Data Insights, 3(1), 100146. https://doi.org/10.1016/j.jjimei.2022.100146
Secundo, G., Shams, S. R., & Nucci, F. (2021). Digital technologies and collective intelligence for healthcare ecosystem: Optimizing Internet of Things adoption for pandemic management. Journal of Business Research, 131, 563–572. https://doi.org/10.1016/j.jbusres.2021.01.034
Sipola, J., Saunila, M., & Ukko, J. (2023). Adopting artificial intelligence in sustainable business. Journal of Cleaner Production, 426, 139197. https://doi.org/10.1016/j.jclepro.2023.139197
Terekhov, M. A., Demirezen, E. M., & Aytug, H. (2023). Business Analytics: Emerging practice and research issues in the health insurance industry. Production and Operations Management, 33(2), 432–455. https://doi.org/10.1111/poms.13976
Wang, Y., & Byrd, T. A. (2017). Business analytics-enabled decision-making effectiveness through knowledge absorptive capacity in health care. Journal of Knowledge Management, 21(3), 517–539. https://doi.org/10.1108/jkm-08-2015-0301
Wang, Y., Kung, L., & Byrd, T. A. (2016). 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