Artificial Intelligence based Strategic Management to Improve Operational Efficiency in Hospitals : Literature Review

Abstract

Artificial Intelligence (AI) is revolutionizing multiple sectors, including healthcare, by performing tasks that traditionally require human intelligence. AI's ability to analyze large datasets, adapt to new inputs, and automate processes offers substantial improvements in healthcare, particularly in strategic management and operational efficiency. In Indonesia, AI is increasingly seen as a transformative technology in hospitals, enhancing decision-making, patient care, and resource management. Despite its potential, AI implementation in healthcare faces significant challenges, including data privacy, ethical concerns, and integration complexities. This study employs a literature review approach combined with descriptive analysis. Data was collected from academic journals, databases such as Google Scholar, PubMed, and accredited national publications, focusing on AI's application in strategic management and operational efficiency in hospitals. The analysis includes data classification into key themes—strategic management, AI applications, and operational efficiency—and thematic analysis to identify trends and relationships across the literature. The literature was categorized based on the main themes of AI applications, operational efficiency, and strategic management frameworks in hospitals. Thematic analysis highlighted the role of AI in optimizing hospital operations, improving patient flow, automating administrative tasks, and enhancing diagnostic accuracy. The synthesis of the data provides insights into the integration of AI in hospital management and its impact on resource utilization, staff productivity, and patient satisfaction. AI is transforming hospital operations, particularly through predictive analytics, robotic process automation, and advanced diagnostic systems. It aids in predicting patient flow, optimizing staff schedules, and improving patient outcomes by automating administrative tasks. However, challenges such as algorithmic bias, data privacy issues, and a lack of standardization in AI models remain significant barriers. These challenges are particularly evident in Indonesia, where limited infrastructure and resistance to change hinder widespread AI adoption. Despite these hurdles, AI's potential to improve operational efficiency in hospitals is undeniable, especially in streamlining processes and enhancing decision-making. AI holds tremendous potential for enhancing operational efficiency in hospitals, optimizing resource management, and improving patient care. However, its successful implementation depends on overcoming significant challenges such as data privacy, algorithmic bias, and regulatory issues. For AI to be effectively integrated into Indonesian hospitals, a robust framework that includes infrastructure development, healthcare worker training, and regulatory support is necessary. With these efforts, AI can significantly improve hospital management, leading to more efficient, effective, and sustainable healthcare systems in Indonesia.