Patient Flow and Service Efficiency in Public Hospitals: Data-Driven Approaches, Strategies, Challenges, and Future Directions

Public hospitals in resource-constrained environments face persistent challenges in patient flow and service efficiency, often resulting in overcrowded emergency departments, delayed admissions, and prolonged waiting times. This review synthesizes literature on data-driven and operational strategies that address these inefficiencies, focusing on predictive analytics, discrete-event simulation, artificial intelligence, and digital dashboards. Findings reveal that integrating simulation-based capacity planning, workforce scheduling, and proactive bed management with digital decision-support tools can significantly enhance throughput and reduce systemic bottlenecks. However, the successful adoption of these strategies requires overcoming barriers such as limited data interoperability, inadequate infrastructure, staff resistance to change, and ethical concerns related to patient data use. Emerging trends, including digital twins, mobile health solutions, and AI-driven predictive models, highlight opportunities for scalable and context-appropriate interventions. The review emphasizes the critical role of governance, interdisciplinary collaboration, and policy support in sustaining efficiency gains. Ultimately, structured, data-enabled frameworks are necessary to build resilient hospital systems that advance equitable healthcare access and contribute to achieving Sustainable Development Goals (SDGs) in low- and middle-income countries.