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.

The combination of machine learning (ML) and neural networks (NN), specifically deep learning (DL), is making a big breakthrough to breast cancer studies. This scientometrics survey studies 200 highly cited publications to map the intellectual landscape and studies trends in this dynamic field. The survey discloses a dominant concentration on computer-aided diagnosis (CAD) systems using convolutional neural networks (CNNs) for the classification of breast cancer from different imaging modalities, including mammography, histopathology, ultrasound, and magnetic resonance imaging (MRI). Key survey directions identified include: (1) the development of comprehensive deep learning techniques for image-based detection and classification; (2) the application of transfer learning to resolve data scarcity; (3) the combination of multi-omics and clinical data for personalized prognosis and treatment prediction; and (4) the exploration of explainability and robustness in ML-driven clinical tools. This study synthesizes the methodological advancements, sheds light on the evolution from traditional machine learning to deep learning, and surveys the challenges associated with data heterogeneity, model interpretability, and clinical integration. By giving a structured overview of the seminal work and emerging paradigms, the study serves as a reference for graduate students and other interested parties to have a better understanding about the current state and future trajectories of AI in breast oncology.

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.