Total Quality Management (TQM), a comprehensive and strategic management philosophy based on continuous quality improvement, strong customer relations, and involvement of the entire organization, has moved from its industrial roots to become the primary support of modern healthcare deliveries. The present article offers a thorough review of the literature that synthesizes 30 years of scientific work from all over the world and provides a critical analysis of the application, effectiveness, and evolution of TQM in the complicated context of hospitals. The analysis starts with the historical and theoretical foundations of TQM in healthcare, connecting basic quality frameworks to modern practices. It then assesses with great care the large volume of evidence proving TQM's positive effect on the most important hospital performance indicators such as patient safety, satisfaction, clinical outcomes, operational efficiency, and economic viability. The discussion devotes a considerable part to the strong challenges and barriers to TQM being successfully implemented, bringing in studies from both developed and developing countries to point out the hurdles that are common and the ones that are particular to a given context. Moreover, the review goes into the interaction between TQM and other process improvement approaches like Lean and Six Sigma. Lastly, it outlines the future path of quality management in the healthcare sector by looking into the alliance of TQM with digital transformation, AI, telemedicine, and innovation management. The paper wraps up by asserting that TQM is still a 'must-have' strategic option for hospitals but that it needs strong and determined leadership, a broad-based quality culture, and an adaptive to change technology and methodology for its successful running.

The paper discusses the advanced simulated annealing metaheuristic approach to solving the difficult operating room (OR) scheduling issue. A detailed mathematical formulation for the multi-day OR scheduling problem is presented that takes into account patient urgency scores, surgeon compatibility, room capacity limits, and time restrictions. Realizing that exact methods would be computationally untenable for larger-sized instances, we propose a highly sophisticated simulated annealing algorithm that uses a new way of representing solutions, makes strategic neighborhood moves, and applies adaptive penalty functions. The algorithm shows solid performance over many different scales of problems and easily deals with instances that mixed-integer linear programming methods find prohibitive. Computational tests have shown that the method suggested secures high-quality solutions while keeping computational cost low, thus giving hospitals a useful tool for increasing OR scheduling efficiency.

In a pioneering way, this thorough research develops and tests a new mathematical programming framework for Human Factors and Ergonomics (HFE) optimization in hospitals. The main focus is a mixed-integer linear programming model that takes into account, at the same time, operating room scheduling on the basis of patient safety, staff well-being, operational efficiency, and resource allocation. By conducting very large computational trials through the MATLAB optimization toolbox, we show the model's ability to produce schedules that not only put critical clinical tasks first but also keep the staff workloads balanced. Our findings demonstrate different fundamental aspects: optimal solutions in a way prioritize high-risk procedures, disclose the natural capacities of the system, and point out where the workflow can be improved. The optimization has been able to assign all the critical tasks (appendectomy, intubation, and code blue) without workload imbalances among the clinical staff. Nonetheless, it also revealed that the resources indeed were not fully utilized and thus more patients could be treated. This study is a very reliable tool for the healthcare managers to make evidence-based scheduling decisions that are in the time period of the hospitals and are reconcilable with the other objectives in the complex clinical environment as well.

In consideration of the growing intricacy in the provision of healthcare services, the demand for the efficiency of clinical workflows that would coexist with human well-being has risen sharply. On one hand, Operations Research and Management Science have been the main tools in the area of healthcare optimization to the extent that their application has been widespread. However, traditional methods have often disregarded the fundamental HFE factors and considerations. The present study aims to dissect and compare HFE and clinical workflow optimization research through their methodologies, applications, and trends within the different health care facilities. It does so by performing systematic analysis on about 60 essential papers, which brings to light three major HFE dimensions that are part of optimization models: cognitive workload management, physical ergonomics, and system reliability. One of the discoveries in this review is the change in models from deterministic and efficiency-oriented to multi-objective frameworks that take care of staff well-being, patient safety, and operational performance at the same time. We trace the history of the methods used from simple mathematical programming to hybrid simulation, optimization techniques, and the use of metaheuristics that are sophisticated. Also, large research gaps are indicated, such as the requirement for real-time adaptive systems, better engineering integration of human factors and ergonomics metrics, and long-term studies on impact. The paper ends with the proposal of a strategic framework for future research directions which includes human-AI collaboration, explainable optimization, and organizational implementation strategies. The presented review is a solid base for researchers and practitioners to build on for advancing the field of human-centered clinical workflow optimization.

The use of Artificial Intelligence (AI) in healthcare systems is a major change or rather a shift of paradigms that can potentially change through and through the whole medical practice, that is from the administrative logistics to the clinical diagnostics and therapeutic interventions. This review is an amalgamation of decade-long research which provides a holistic view of the applications of AI in healthcare continuum, hence consulting its role in the optimization of hospital operations and scheduling, the improvement of diagnostic accuracy in radiology and pathology, and personalization of treatment plans in fields like oncology and chronic disease management, and so on along with the engagement of patients through chatbots and wearable technology. Moreover, the article has critically assessed the operational efficiencies obtained in the areas such as supply chain management, resource allocation, and clinical workflow automation among others, thus highlighting the importance of alive and kicking in the healthcare. On the downside, the author pointed out the main hurdles which have the power to put a brake on the adoption of these advanced technologies in medical practices like data privacy issues, algorithmic bias, the "black box" problem in clinical decision-making, and the moral dilemmas of using autonomous systems in life-or-death situations. By studying the whole journey from basic to very advanced applications, this review argues that the future of healthcare is still. It should be a collaborative portraithuman-oriented whereby the AI becomes a partner of the clinician instead of a competitor thus not only creating more robust, effective and patient-activated care systems paving the way for better health outcomes.

In this study, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to rank the telemedicine performance of seven major Japanese cities: Fukuoka, Sapporo, Yokohama, Kyoto, Osaka, Nagoya, and Tokyo. The ranking was consistent throughout the analysis based on the five criteria—Infrastructure, Healthcare System Integration, Accessibility & Equity, Service Breadth & Quality, and Regulatory Environment. Fukuoka was the best performer, followed by Sapporo, and Tokyo, even with its high medical resources, ranked last. The same ranking was observed even when the cost criterion was given a 50% weight, which means that structural and governance factors have more influence than affordability in determining performance. The findings question the view that telemedicine success relies exclusively on economic and medical scale. Rather, they point to the effectiveness of Japan's regional innovation policies; for example, proactive local governance, as in the case of Fukuoka's Special Zone status, and a clear demographic mandate for rural healthcare access, as in Hokkaido, are considered more important drivers of a robust and equitable telemedicine ecosystem.

This study evaluates the relative efficiency of digital health adoption across ten major German cities and regions. In a landscape where digital transformation is critical, this research moves beyond mere technological assessment to determine which locales most effectively convert inputs into outputs. A multi-criteria decision-making framework is employed, integrating Data Envelopment Analysis (DEA) under both constant and variable returns-to-scale assumptions and the TOPSIS method. Key metrics include Infrastructure, Healthcare System Integration, Service Breadth, Regulatory Environment, and a critical cost factor, Accessibility & Equity. Results from DEA models highlight a system-wide scale inefficiency, yet identify specific efficient units under variable returns, including Berlin, Rhineland-Pfalz, Leipzig, and Aachen. The TOPSIS analysis, particularly when prioritizing cost-equity, reveals a distinct ranking: Rhineland-Pfalz, Leipzig, and Aachen emerge as top performers, while major economic hubs like Munich, Frankfurt, and Stuttgart demonstrate lower efficiency due to high costs and systemic fragmentation. The findings challenge the presumption that economic scale guarantees digital health efficiency, instead underscoring the superior performance of strategically coordinated and equity-focused models. This study provides policymakers with a robust framework for benchmarking and highlights governance and integration, not just investment, as the key levers for enhancing digital health system performance.

Hospital departments are continuously challenged to allocate their limited resources—beds, nursing staff, and operating room (OR) time—among patients with different clinical priorities on the waiting list. This paper presents a solution to this intricate planning problem by constructing a novel Integer Linear Programming (ILP) model to schedule elective patient admissions optimally. The goal is to increase the total priority score of the patients admitted, where the score indicates clinical urgency and waiting time, while at the same time honoring the restrictions on bed capacity, nursing hours, and OR availability through a multi-day planning horizon. The model uses binary decision variables for patient admission as well as pre-processed parameters to map the individual patient resource consumption over the time they are expected to stay in the hospital. We showcase the model's application through a realistic scenario with synthetic data. The outcomes reveal that the suggested framework not only effectively creates an optimum admission timetable but also considerably enhances the use of vital resources when compared to a first-come-first-served standard. The ILP model is not only potent but also clear and just decision-support tool for hospital management, according to the study's findings. By assigning priority to clinical criteria and making patient access more equitable, it provides a data-guided way to improve operational productivity, as well as to decide the trade-offs that come with the limitations of healthcare facilities in terms of resources. Besides, the model is computationally capable of meeting the needs for the inclusion of extra real-world constraints and adjustments.

Modern healthcare's growing intricacy, which is a result of an aging population and increased use of technology, necessitates the use of very advanced operational management. A solution is provided to the issue of hospital optimization through the use of a unified mathematical programming method that combines two main areas, resource allocation and workforce management. We present a new multi-objective Linear Programming (LP) and Mixed-Integer Linear Programming (MILP) framework made especially for the "smart hospital" setting. The model is able to do several things at once; it is able to come up with the best patient admission scheduling, use the beds and operating rooms in the most efficient way possible, and schedule the nurse staff all while being subjected to the real-world constraints of demand fluctuations, staff preferences, skill mixes, and labor regulations. A detailed case study is presented which clearly shows the effectiveness of the model, revealing a 15% boost in resource utilization, a 12% decrease in operational costs, and increased nurse schedule satisfaction, all whilst still being able to offer high standards of patient care coverage. The findings point out the great opportunity of using integrated optimization models to make healthcare delivery systems more efficient, cheaper, and more adaptive, thus giving hospital administrators a strong decision-support tool.

The Quadratic Assignment Problem (QAP) is still considered to be one of the most difficult and widely used models in combinatorial optimization. The layout of healthcare facilities has been its most significant application area since the 1970s, representing a crucial field of study for increasing operational efficiency, patient safety, and staff flow. The QAP context has been continually altered and supplemented to cover the particular intricacies of the healthcare sector. After Elshafei's groundbreaking paper in 1977, the QAP framework was reinvented and extended to the point where it gained acceptance in the healthcare facility location planning area. This review offers a synthesis of the existing literature from 1977 to 2025 and classifies the research into ten different methodological streams: Exact Solution Methods, Classical Heuristics, Metaheuristics, Hybrid Approaches, Robust Optimization, Fuzzy QAP, Stochastic Programming, Multi-Objective QAP, Special Structure Exploitation, and Parallel & Dis-tributed Computing. The critical assessment of the transition of solution procedures and how the techniques for handling uncertainty have been developed shows how the research has progressed from modeling with one deterministic objective to a sophisticated data-driven approach where multiple objectives are characterized as well as the inherent uncertainties of the system. The analysis indicates the integration and hybridization trend—in the case of algorithms, objectives, and data sources is quite strong—pointing out the future lines of research in areas such as real-time adaptive layouts, deep learning integration, and pandemic-responsive design.