The use of cloud computing, particularly virtualized infrastructure, offers scalable resources, reduced hardware needs, and energy savings. In Ethiopian public hospitals, the lack of integrated healthcare systems and a national data repository, combined with existing systems deficiencies and inefficient traditional data centers, contribute to energy inefficiency, carbon emissions, and performance issues. Thus, evaluating the energy efficiency and performance of a cloud-based model with various workloads and algorithms is essential for its successful implementation in healthcare systems and digital health solutions. The study experimentally evaluates a cloud-based model's energy efficiency and performance for smart healthcare systems, employing descriptive and experimental designs to simulate cloud infrastructure. Simulations are conducted on diverse workloads in CloudSim using power-aware (PA) algorithms (along with VmAllocationPolicy and VmSelectionPolicy), and dynamic voltage frequency scaling (DVFS). Results reveal that the number of VMs and their migrations significantly impact energy consumption, with some algorithms achieving notable energy savings. Lr/Lrr-based algorithms are particularly energy-efficient, with LrMc and LrrMc saving 29.36% more energy than IqrMu at 55 VMs, and LrrRs saving 30.20% more at 1,765 VMs. DVFS adjusts energy consumption based on the number of VMs, while non-power-aware (NPA) consumes maximum energy based on hosts, regardless of the number of VMs. VM migrations, energy consumption, and average SLAV are positively correlated, while SLA is negatively correlated with these factors. In PlanetLab, energy consumption and average SLAV show a strong positive correlation (0.956) at Workload6, while SLA at Workload2 and average SLAV at Workload1 show a weak negative correlation (-0.055). Excessive migrations can disrupt the system's stability/performance and cause SLA violations. Task completion time is influenced by VM processing power and cloudlet length, being inversely proportional to VM processing power and directly proportional to cloudlet length. Overall, the findings suggest that cloud virtualization and energy-efficient algorithms can enhance healthcare systems performance, patient care, and operational sustainability.

Hospitals are the primary hubs for healthcare service providers in Ethiopia; however, hospitals face significant challenges in adopting digital health information systems solutions due to disparate, non-interoperable systems and limited access. Information technology, especially via cloud computing, is crucial in healthcare for efficient data management, secure storage, real-time access to critical information, seamless provider communication, enhanced collaboration, and scalable IT infrastructure. This study investigated the challenges to standardizing smart and green healthcare information services and proposed a cloud-based model for overcoming them. We conducted a mixed-methods study in 11 public hospitals, employing quantitative and qualitative approaches with diverse stakeholders (N = 103). The data was collected through surveys, interviews, and technical observations by purposive quota sampling with the Raosoft platform and analyzed using IBM SPSS. Findings revealed several shortcomings in existing information systems, including limited storage, scalability, and security; impaired data sharing and collaboration; accessibility issues; no interoperability; ownership ambiguity; unreliable data recovery; environmental concerns; affordability challenges; and inadequate policy enforcement. Notably, hospitals lacked a centralized data management system, cloud-enabled systems for remote access, and modern data recovery strategies. Despite these challenges, 90.3% of respondents expressed interest in adopting cloud-enabled data recovery systems. However, infrastructure limitations, inadequate cloud computing/IT knowledge, lack of top management support, digital illiteracy, limited innovation, and data security concerns were identified as challenges to cloud adoption. The study further identified three existing healthcare information systems: paper-based methods, electronic medical catalog systems, and district health information systems2. Limitations of the paper-based method include error-proneness, significant cost, data fragmentation, and restricted remote access. Growing hospital congestion and carbon footprint highlighted the need for sustainable solutions.  Based on these findings, we proposed a cloud-based model tailored to the Ethiopian context. This six-layered model, delivered as a Software-as-a-Service within a community cloud deployment, aims to improve healthcare services through instant access, unified data management, and evidence-based medical practices. The model demonstrates high acceptability and potential for improving healthcare delivery, and implementation recommendations are suggested based on the proposed model.