Artificial Intelligence and Cost Reduction Strategies for Healthcare Management: Opportunities and Limitations

Abstract

This study employs a traditional literature review approach to examine the role of artificial intelligence (AI) in cost-reduction strategies in healthcare management. As healthcare systems face increasing financial pressures, AI has been widely explored for its potential to enhance operational efficiency and optimize resource utilization. By synthesizing recent academic literature and theoretical discussions, this review aims to provide a comprehensive evaluation of AI-driven cost reduction strategies and their broader implications for healthcare management. This study explores AI applications in automating administrative workflows, enhancing predictive analytics, and optimizing clinical decision-making. AI’s contributions to supply chain management and early disease detection are also highlighted as significant factors in achieving operational cost savings. However, despite its transformative potential, AI adoption in healthcare presents notable challenges, including high initial investment costs, data security risks, ethical dilemmas, and regulatory constraints. These challenges necessitate a structured and strategic approach to ensure sustainable and effective AI implementation. Through an extensive review of the existing literature, this study critically analyzes both the opportunities and limitations associated with AI-driven healthcare cost reduction. The findings underscore the need for robust regulatory frameworks, ethical AI deployment, and continuous workforce adaptation to facilitate the successful integration of AI technologies into healthcare systems. By offering key strategic insights, this study contributes to the academic discourse on AI’s cost-effectiveness in healthcare and provides a foundation for future research and policy development.

Keywords

• Healthcare Management
• Artificial Intelligence
• Cost Reduction Strategies
• Digitalization in Healthcare
• AI-Driven Decision-Making

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