AI-Driven Decision Support Systems: Transforming Hospital Management Strategies

Abstract

Introduction
The healthcare sector faces many challenges, including more patients, rising costs, and the need for high-quality care. AI-driven decision support systems (AI-DSS) can enhance hospital management by improving workflows, cutting administrative errors, and better allocating resources to improve patient outcomes.
Aim
The aim of this study is to evaluate the impact of AI-DSS on hospital management efficiency, patient outcomes, and operational strategies. It explores the factors influencing successful AI-DSS implementation and examines healthcare professionals' perceptions of these systems in clinical decision-making. The study seeks to provide evidence on the role of AI-DSS in enhancing both operational and patient care outcomes in healthcare settings.
Methodology
This survey focused on healthcare professionals from private and government hospitals, including doctors, nurses, administrators, and allied health staff. We collected data through an online questionnaire that covered demographics, AI usage, perceived effects, challenges, and overall satisfaction. We used Likert-scale and multiple-choice questions for quantitative data.
Results
The study found that only 9.4% of hospitals currently use AI-DSS, showing that adoption is still in its early stages. Respondents noted several benefits, with 59.6% stating that AI helps with clinical decision-making and 63% saying it improves operational efficiency and resource use. However, opinions about AI's impact on diagnostic accuracy were mixed, with 48.8% disagreeing that AI significantly enhances diagnostic processes. Key barriers to adoption included cost (50.7%), staff resistance (40.4%), and lack of training (35%). Despite these issues, many respondents felt that AI boosts staff confidence in clinical decisions and allows for timely interventions, which positively affects patient outcomes.
Conclusion
AI-DSS shows great potential to improve hospital management by enhancing decision-making and efficiency. However, to increase adoption, we need to tackle barriers like costs, training issues, and staff resistance. Future efforts should focus on strong training programs, affordable solutions, and strategies for better integration of AI in healthcare settings.