Modern hospital operations need more advanced optimization methods due to the factors such as the variation in patient demand, limited resources, and complicated workforce regulations. The initial research suggested a combined Linear and Mixed-Integer Linear Programming (LP/MILP) approach to the joint optimization of patient admissions, bed/OR utilization, and nurse scheduling. The model unified the operational costs and the staff preferences into a single weighted objective, thereby showing the very significant resource utilization and scheduling satisfaction improvements. We have extended the framework from its original version and in this extended work we are going to demonstrate how the nurse scheduling component is fashioned into an actual multi-objective optimization problem. Rather than addressing the problem via a single weighted aggregation, two opposing targets, minimizing overall operational cost and maximizing nurse preference satisfaction, are treated openly. Moreover, we introduce the Adaptive ε-Constraint method that allows us to take advantage of the division between coarse ε sweep and local refinement to produce a well-distributed approximation of the Pareto frontier. The progressive method not only addresses the clustering problem that has appeared in the naive ε sweeps but also creates a continuous and varied set of solutions that are not dominated by any other solution. With the extended model that utilizes synthetic but realistic nurse, demand, and preference data, a variety of feasible scheduling policies with obvious trade-offs between cost and employee satisfaction are provided. The Pareto frontier offers intermediate solutions which are able to achieve large increases in preference satisfaction at the expense of only negligible increments in operational costs when compared to the baseline cost-minimal and preference-maximal schedules. The findings emphasize the usefulness of multi-objective decision support in hospital practice and also prove that through the direct representation of staff preferences, it is possible to have even distributions of working time without losing the effectiveness of operations. On the whole, the extension demonstrates that the original "smart hospital" model is enriched and the decision-making process for the administrators is more flexible with the inclusion of multi-objective optimization, thus resulting in the enhancement of both efficiency and health of the staff.

This analysis delves into the evolving telemedicine landscape in India. It dissects the service models employed by both government and private healthcare providers, highlighting their distinct approaches in delivering telemedicine services. The study unveils how government initiatives strive to bridge geographical gaps and widen accessibility, while private players leverage technology for a more patient-centric experience. Furthermore, the research investigates patient perceptions of the impact of 5G technology on telemedicine services. It evaluates aspects crucial for effective consultations, such as connected devices, connection stability, video quality, speed of data transfer, and overall user satisfaction. This analysis reviews patient experiences with 5G and its potential advancements in transforming telemedicine delivery. The exploration then extends to the potential advantages and growth prospects for telemedicine service providers in India's healthcare sector. The analysis highlights key benefits like increased geographical reach, improved cost-effectiveness for both patients and providers and enhanced scalability to cater to a wider population. Additionally, it explores the possibilities of deeper technological integration within healthcare systems, market expansion into underserved regions, and the role of supportive regulations in fostering innovation. By examining potential investment opportunities and strategic partnerships, the research offers valuable insights for stakeholders interested in capitalizing on the burgeoning telemedicine market in India. This comprehensive examination provides critical insights into the current state and future prospects of telemedicine in India. It sheds light on the evolving landscape, the impact of technological advancements, and the potential for this innovative approach to revolutionize healthcare delivery across the nation.

This study presents a comprehensive comparative analysis of hospital efficiency in Iran using Data Envelopment Analysis (DEA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodologies. With increasing pressure on healthcare systems to optimize resource utilization while maintaining quality standards, measuring hospital efficiency has become crucial for evidence-based decision-making. The research employs four distinct DEA models, Charnes, Cooper, and Rhodes (CCR), Banker, Charnes, and Cooper (BCC) input-oriented, BCC output-oriented, and Additive models, alongside TOPSIS to evaluate the relative efficiency of Iranian hospitals. By comparing these methodological approaches, this study aims to identify the most suitable framework for hospital performance assessment in the Iranian healthcare context. The analysis incorporates multiple input variables including number of physicians, nursing staff, available beds, and operational costs, against output variables such as patient discharges, outpatient visits, surgical procedures, and bed occupancy rates. The findings provide insights into the consistency and reliability of different efficiency measurement techniques, offering healthcare administrators and policymakers a robust analytical framework for performance evaluation. The comparative approach reveals methodological strengths and limitations in different contexts, contributing to the advancement of healthcare efficiency measurement literature while providing practical implications for hospital management in emerging economies.

The Saudi Vision 2030 framework has catalyzed an ambitious digital health transformation within the Kingdom's healthcare system. This systematic review provides a comprehensive analysis of the landscape of research concerning the application of Artificial Intelligence (AI) in Saudi Arabia's health sector and its impact on healthcare efficiency. Utilizing a dataset of 1,250 records from the Scopus and Web of Science databases, with an in-depth analysis of 85 relevant studies, this review maps the conceptual structure and dynamics of this emerging field. The analysis examines publication trends, key research themes, leading contributors, and the methodological focus of the published papers. The results disclose a rapidly growing trend in publications, accelerating from 2021 onwards, driven by national strategic priorities and the need to optimize healthcare delivery. The research is characterized by strong institutional contributions from major Saudi universities and medical cities, with emerging international collaborations. Thematic clusters are dominated by AI in medical imaging and diagnostics, predictive analytics for patient management, AI-driven health informatics, and resource optimization. The findings indicate that AI applications are significantly enhancing diagnostic accuracy, streamlining administrative processes, predicting disease outbreaks, and optimizing resource allocation, thereby contributing markedly to healthcare efficiency. This survey offers a foundational overview of a critical domain within Saudi Arabia's health sector evolution, highlighting the synergistic role of national policy and technological innovation in shaping a future-ready healthcare system.

This study presents a comprehensive evaluation of 20 leading hospitals across 10 countries using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The analysis incorporates eight critical healthcare performance indicators: mortality rate, patient satisfaction, average wait time, readmission rate, cost per patient, staff-to-patient ratio, technology adoption, and infection control score. Results reveal that Apollo Chennai (India) achieved the highest TOPSIS score of 0.6487, followed by Massachusetts General Hospital (USA) at 0.5861 and Johns Hopkins Hospital (USA) at 0.5653. Country-level analysis indicates that India ranks first with an average score of 0.5975, followed by the United States (0.5416) and the United Kingdom (0.5137). Sensitivity analysis demonstrates the robustness of rankings across different weighting scenarios, with Apollo Chennai and Massachusetts General consistently performing well regardless of weighting emphasis. The study provides valuable insights for healthcare policymakers, hospital administrators, and patients seeking optimal care facilities, while demonstrating the efficacy of TOPSIS in healthcare performance assessment.

This study presents a comprehensive performance evaluation framework for 20 leading medical laboratories worldwide using an integrated Best-Worst Method (BWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The assessment incorporates ten critical criteria encompassing clinical accuracy, operational efficiency, research output, cost-effectiveness, and technological advancement. BWM was employed to determine optimal criterion weights through systematic pairwise comparisons, followed by TOPSIS for objective laboratory ranking based on relative closeness to ideal solutions. Results indicate that Memorial Sloan Kettering Labs (USA) and MD Anderson Cancer Center Labs (USA) consistently rank highest across multiple scenarios, demonstrating superior performance in clinical accuracy and quality accreditation. The analysis reveals significant performance variations across countries and laboratory categories, with academic/research institutions generally outperforming commercial laboratories. Sensitivity analysis confirms the robustness of rankings across different weighting scenarios. This framework provides healthcare administrators, policymakers, and laboratory managers with a validated tool for benchmarking and strategic decision-making in medical laboratory services optimization.

This study aims to examine total quality management, regulation, and blood production relationships of blood banks in Uganda. A structured questionnaire was used to collect data from 146 randomly selected respondents. The model was validated using Smart PLS-SEM analysis. The findings indicate that both total quality management and regulation have a significant and positive influence on blood production, accounting for a 17.8% variation at a 95% confidence interval. Regulation exhibited no mediation effect in the relationship between total quality management and blood production. Total quality management and regulation are essential factors enhancing blood production. Prioritizing total quality management practices in areas such as determining and meeting customer needs; customer and employee satisfaction surveys; and market research can optimize blood production. This study contributes to the blood bank management knowledge body and identifies areas to support blood production. Blood bank managers can apply these insights to improve operational performance.

Banana diseases remarkably influence the worldwide production of bananas. Innumerable studies have focused on timely recognition, prediction, and management of banana plant diseases using various chemical, biological, socio-economic, and AI-based methods. The survey scrutinizes 184 articles accumulated from Scopus, Web of Science, and Google Scholar using defined keywords. These findings reveal the global distribution of the previous studies on plant disease detection, the evolution of ML techniques, and the most frequently studied diseases. The literature shows a swift progress towards machine learning, deep learning, remote sensing, and IoT systems for banana plant disease detection. However, numerous AI models lack real-world validation, datasets are fragmented, and severity quantification mechanisms are understudied. The synthesis analyzes the strong dominance of CNN-based models, which account for the highest proportion of published works and remain the foundational architecture for banana disease detection. Countries such as India, China, the Philippines, Ecuador, and Indonesia have contributed significantly to disease detection. Despite notable progress, many existing systems still rely on single-source and limited datasets, which leads to a lack of cross-source robustness. Evolution of a robust framework integrating multiple datasets, explainable AI, decision support systems and socio-economic insights can lead to more enhanced farmer-friendly banana plant disease management in future This survey provides a detailed overview of the global research studies, highlighting key research gaps that need to be addressed and outlines future directions for building more reliable, interpretable, and comprehensive decision-support pipelines, which will guide the future research work.

This scientometric study gives a comprehensive survey of the highly influential scientific literature at the intersection of cardiovascular disease and scaffold technology. By testing a curated dataset of 200 highly cited articles, this review maps the intellectual landscape, determines key research fronts, and keeps tracking the evolution of this dynamic field. The analysis discloses a dominant concentration on tissue engineering applications, specifically for myocardial infarction repair, vascular graft development, and heart valve replacement. Key themes incorporate the exploration of novel biomaterials such as biodegradable polymers, decellularized matrices, hydrogels, and electrospun nanofibers, and the integration of advanced fabrication methods such as 3D bioprinting. The survey also determines seminal contributions from leading research groups and highlights the synergistic relationship between material science, cell biology, and clinical cardiology which drives innovation. In addition, the survey tracks the rising prominence of enabling technologies which include conductive scaffolds for cardiac patches and the application of stem cells. The study not only synthesizes the current state of knowledge but also determines emergent trends and potential future trajectories, underscoring the critical role of scaffold-based strategies in advancing cardiovascular regenerative medicine. The results consolidate a vast body of literature to inform researchers and funding agencies about the field's structure and its most essential avenues of investigation.

We present a scientometric analysis of the research landscape about the application of Total Quality Management (TQM) rules within surgical and broader healthcare contexts. The study utilizes a dataset of 200 highly cited articles extracted from Scopus and maps the intellectual structure, key themes, and evolving priorities in this critical field. The study discloses a mature yet dynamically evolving survey domain characterized by a distinct shift from theoretical process frameworks to patient-centric and data-driven methodologies. Key study clusters determined include Patient Safety Culture and Adverse Event Reduction, Specific Surgical Procedure Optimization, Methodological Frameworks for Quality Improvement (QI), and Ethical & Inclusive Care Considerations. Highly cited articles and authors as well as influential institutions are determined, representing a global collaboration network with strong representation from the United States and Northern Europe. The most effective publications, as stated by citation frequency, are studied in detail, briefing their contributions to building safety protocols, validating QI methodologies like DMAIC, and expanding the discourse on patient engagement and health equity. The present review summarizes that the field is advancing towards more predictive, equitable, and technologically integrated models of care, with future research poised to leverage artificial intelligence and federated learning to personalize and enhance surgical quality improvement on a global scale.