Automated Decision Support Systems for Enhancing Operational Efficiency in Healthcare Management

Abstract

 Healthcare management faces significant challenges, including rising operational costs, increasing patient loads, data fragmentation and the need for timely and accurate decision-making. Traditional decision-making approaches often struggle to handle large-scale, real-time healthcare data, leading to inefficiencies and delays in patient care delivery. Automated Decision Support Systems (ADSS) have emerged as a transformative solution to address these issues by integrating advanced analytics, machine learning and data-driven insights into healthcare operations. This study proposes an ADSS framework designed to enhance operational efficiency in healthcare management. The methodology incorporates data collection from multiple hospital information systems, followed by preprocessing, predictive modeling and optimization techniques. Machine learning algorithms are employed to analyze patient flow, resource allocation and treatment outcomes, enabling informed decision-making. The results demonstrate that the proposed system significantly improves operational efficiency by reducing patient wait times, optimizing resource utilization and minimizing administrative workload. Additionally, the system contributes to cost reduction through better resource planning and enhances decision accuracy by providing real-time, evidence-based recommendations. Overall, the implementation of ADSS offers a scalable and effective approach to modernizing healthcare management and improving service quality.