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.