Enhancing Healthcare Outcomes Through Data-Driven Decision Making: A Business Analytics Approach
DOI registered with Crossref
https://doi.org/10.37547/ijmsphr/Volume06Issue10-04
Keywords
Business Analytics
Data-Driven Decision-Making
Healthcare Outcomes
Predictive Analytics
Abstract
Healthcare systems worldwide are buckling under the pressures of rising costs, increases in patient demand and the complexity of chronic disease management. It is in this context that data-driven decision-making (DDM) backed by business analytics (BA) have become a critical enabling factor for efficiency, precision, and better patient outcomes. This paper investigates the role of BA in improving healthcare outcomes by means of a systematic review of the recent empirical studies and a secondary analysis of quantitative datasets from sources worldwide, such as the World Health Organization (WHO), the Organization for Economic Co-operation and Development (OECD) and the Centers for Medicare & Medicaid Services (CMS). The study combines descriptive analytics, predictive analytics, and prescriptive analytics models to determine their contribution to operational efficiency and cost reduction and clinical effectiveness. Findings include that predictive analytics can lead to a saving of up to 20% in hospital readmission, descriptive dashboards can enhance resource allocation and increase staff productivity, and prescriptive analytics can be used to optimize treatment pathways, leading to measurable improvements in patient satisfaction and clinical outcomes. Unlike previous studies that tend to separate the clinical/operational benefits, this paper introduces a holistic framework of the linkages between BA adoption and strategic decision-making at organizational and policy levels. The novelty of this study is the cross-functional nature of the approach, which emphasizes the synergy of clinical, managerial, and policy decisions which are grounded in analytics. Ultimately, the results highlight the importance of healthcare organizations investing in the capabilities of BA and aligning the data strategy with patient-centered care goals, all while achieving sustainable improvements in both outcomes and efficiency.
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Keya Karabi Roy, Maham Saeed, Mahzabin Binte Rahman, Kami Yangzen Lama, & Mustafa Abdullah Azzawi. (2025). Leveraging artificial intelligence for strategic decision-making in healthcare organizations: a business it perspective. The American Journal of Applied Sciences, 7(8), 74–93. https://doi.org/10.37547/tajas/Volume07Issue08-07
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Kazi Sanwarul Azim, Maham Saeed, Keya Karabi Roy, & Kami Yangzen Lama. (2025). Digital transformation in hospitals: evaluating the ROI of IT investments in health systems. The American Journal of Applied Sciences, 7(8), 94–116. https://doi.org/10.37547/tajas/Volume07Issue08-08
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