Machine Learning opportunities and challenges in health system management: Review of the existing literature

Abstract

Health system management plays a vital role in improving health outcomes and overall system performance. It entails organizing, planning, directing and managing the resources, procedures, and structures of a health system. In recent years, the use of machine learning in healthcare system management has demonstrated tremendous potential in several areas, including predictive modeling, resource optimization, and quality control. Machine learning can be used to optimize resource allocation, forecast outcomes, identify potential dangers, and find patterns in vast datasets. That can lead to improved patient outcomes and lower costs, and overall improved quality of health systems. However, there are also challenges to incorporating machine learning into healthcare system management, including ethical concerns, privacy issues, and lack of data standardization. In this paper, we provide a comprehensive review of the opportunities and challenges of applying machine learning in healthcare system management. The findings suggest that machine learning can play a significant role in enhancing healthcare quality, reducing costs, and improving patient outcomes, but its integration must be done ethically and with proper oversight.