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All Journal Jurnal Inovasi Teknologi dan Edukasi Teknik Journal Health of Indonesian
Okechukwu Chiedu Ezeanyim
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology Neuroscience Nursing Public Health

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Patient Flow and Service Efficiency in Public Hospitals: Data-Driven Approaches, Strategies, Challenges, and Future Directions Okechukwu Chiedu Ezeanyim; Emeka Celestine Nwabunwanne; Nkemakonam Chidiebube Igbokwe; Charles Onyeka Nwamekwe
Journal Health of Indonesian Vol. 3 No. 02 (2025): Journal Health of Indonesian, July 2025
Publisher : Paspama Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/health.v3i02.228

Abstract

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.
DECISION INTELLIGENCE SYSTEMS FOR AUTONOMOUS PRODUCTION PLANNING AND CONTROL Okechukwu Chiedu Ezeanyim; Kenechukwu Favour Anagwu
Jurnal Inovasi Teknologi dan Edukasi Teknik Vol. 5 No. 11 (2025)
Publisher : Universitas Ngeri Malang

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Abstract

This review examines decision intelligence systems for autonomous production planning and control in Industry 4.0 manufacturing. It addresses the limits of conventional production planning and control systems, which depend on deterministic models, historical data, human expertise, and a two-tier structure of schedule generation and execution control. Such systems perform poorly when demand variability, resource constraints, machine workload changes, material shortages, and quality disruptions make released schedules suboptimal. The review synthesizes evidence from multi-agent systems, holonic manufacturing, cyber-physical production systems, digital twins, machine learning, reinforcement learning, data-driven scheduling, and prescriptive analytics. It shows how decision intelligence links data acquisition, analytics, optimization, and execution through a closed-loop sense-decide-act structure. The framework rests on four core components: data acquisition and management, analytical models, decision algorithms, and execution mechanisms. It evaluates five operational domains: dynamic scheduling, inventory and supply chain coordination, resource allocation, predictive maintenance, and resilient production control. Numerical evidence strengthens the review. Digital twin literature shows 38% use simulation models to represent physical systems and predict future states, while 29% integrate optimization into simulation models. A validated multi-agent logistics case reported savings of 2.6 million pallet-days per year through better use of terminal free time. The review also identifies six unresolved barriers: data quality and integration, model interpretability, scalability, real-time implementation, human-system interaction, and lack of standard frameworks. Future systems should combine explainable AI, edge-cloud computing, digital twin-based optimization, hybrid control, and standardized evaluation protocols to achieve adaptive, scalable, and trusted autonomous production control.
Co-Authors Charles Onyeka Nwamekwe Emeka Celestine Nwabunwanne Kenechukwu Favour Anagwu Nkemakonam Chidiebube Igbokwe