Strategic Decision-Making in Healthcare Using Advanced Business Analytics Techniques - Mohammad Yasin; Mahzabin Binte Rahman; Sunil Kanojiya; Mahbub Hasan (International Journal of Medical Science and Public Health Research, 2025)
The issue of strategic decision making in the healthcare sector has grown more complicated with healthcare providers struggling to balance their patient outcomes management with the growing operational and financial demands. However, the conventional decision-making processes that are usually founded on ex-post reviews and intuition fail to work well in the workplace where uncertainty, risk and multidimensional stakeholder requirements are prevalent. This paper will focus on how the use of modern business analytics methods, including predictive modeling, machine learning models, and artificial intelligence (AI)-driven models, will be utilized to reinforce healthcare decision-making. The paper utilizes a data-driven approach to review the effect of analytics-based strategies on increasing accuracy, timeliness, and alignment of decisions made by the stakeholders in terms of resource allocation, patient safety, and financial management use based on real-world evidence of hospital systems and secondary databases. As the results show, AI-assisted analytics are effective not only to enhance predictive accuracy of clinical and operational risks but also to implement more transparent and evidence-based strategies to balance patient-centered and organizational performance. Additionally, the research points out the importance of stakeholder-based models that are aided by business analytics to lessen uncertainties, systemic risks, and deliver effective healthcare. Combining decision-making theory and the practical implementation of AI and analytics, the study can give a structure that will be followed by the healthcare leaders to achieve agility in the strategic domain, increase the level of trust among the involved parties, and become resilient in the long run. This work is novel because it combines both decision science and AI-driven analytics to tackle clinical and managerial needs and provide policy, practice, and future research recommendations that can be implemented across the healthcare systems of the world.