Managing blood inventory is challenging due to the perishable and unstable nature of the product needed for transfusions in healthcare facilities. In this paper, we consider a periodic review blood inventory model with two priority demand classes, namely emergency and regular patients. We propose a dynamic programming model for determining the optimal ordering policy at the hospital given the uncertainty regarding received donated blood units. The optimal policy deals with placing orders for blood units that will expire within a fixed period. The objective is to minimize total expected costs within a planning horizon while maintaining a specified expected service level. Our model considers uncertain demands and donated blood units with discrete probability following known distributions. A tabu search algorithm is developed for large-scale problems. The performance of these ordering policies is compared against the optimal fixed order quantity and the order up-to-level policies using real-life data. The numerical results show the benefit of our model over the optimal fixed order quantity and the order up-to-level policies. We measure the total expected cost and the expected service level obtained from the optimal and near-optimal policies and provide a sensitivity analysis on parameters of interest.

Considering the increasing demand for cosmetic surgery and the number of private cosmetic surgery clinics, it is essential to measure and manage the quality of services provided by these clinics. Obtaining sufficient knowledge about the content perceived by the clients of the quality of services provided by specialized clinics can affect identifying improvement opportunities and criteria that will cause their competitive advantage, and on the other hand, it also prevents wasting resources. For this purpose, this study aims to identify, evaluate, and prioritize the criteria for quality improvement in cosmetic surgery clinics. First, the effective criteria focus on the quality of medical services have been identified by reviewing the research background. Then, the identified criteria in the case study are customized by the Delphi method, and then the DEMATEL-based analytic network process method (DANP) is applied to reveal their causal relationships between criteria and sub-criteria to determine the direct and indirect influences, and finally, all of them are prioritized. In the end, based on the obtained results and knowledge of experienced medical experts in the case study, some managerial solutions are proposed to improve the quality of the provided medical services.

This study investigates the impact of supply chain agility on supply chain performance in Jordan’s healthcare settings. The moderation role of human capital in this relationship was also investigated. This study adopted a quantitative approach and utilized an online questionnaire to collect data from 139 managers at different levels in various hospitals in Jordan. The Statistical Package for Social Sciences (SPSS) version 23 was used for data analysis. The results of hypothesis testing using multivariate regression analysis showed that human capital has a positive and statistically significant impact on supply chain performance and supply chain agility. Human capital has been identified as an important organizational resource that can result in favorable performance. Such an organizational resource may also help healthcare institutions obtain a competitive edge by responding to patients’ treatment, which is urgently required.

This study examines the impact of organizational culture on the effectiveness of Project Portfolio Management (PPM) practices. Organizational culture influences employee behavior and their way of working by providing a conducive work environment. Thus, managers are able to delegate better while ensuring a balanced workload, collaborative team efforts, and prudent resource allocation to achieve desired project portfolio deliverables. By shaping practices and values, a supportive culture enables efficient task performance, effective delegation, teamwork, and resource allocation toward project goals. Conducted with 35 individuals in a healthcare organization’s data and digital unit in New Zealand, the study used convenience sampling and email surveys. Regression analysis was performed in IBM SPSS Statistics to test the research hypothesis. Findings suggest that supportive organizational culture significantly enhances PPM execution and informs policy-making for improved PPM practices. This study facilitates directors, strategists, and managers in taking steps to form a culture that ensures effective execution of PPM practices to achieve better results. Future research could explore this topic with larger samples and alternative methodologies to deepen insights into culture’s role in PPM effectiveness.

This study is carried out to investigate the effect of five factors of the Total quality management practices in healthcare on employee’s performance in public hospitals in Turkey. The TQM practices are Top management commitment, customer focus, people management, continuous improvement, and process management. The study adopted a quantitative research method through a survey questionnaire at three public hospitals, 200 valid responses were collected and used as a source of data to test the hypotheses of the study. Hierarchical multiple regression was used for analyzing the collected data, the study indicated that TQM factors have a significant effect on employees’ performance and the moderating effect of training has increased the correlation between employees’ performance and the TQM factors.

Due to the increasing technological advances in all fields, a considerable amount of data has been collected to be processed for different purposes. Data mining is the process of determining and an-alyzing hidden information from different perspectives to obtain useful knowledge. Data mining can have many various applications, one of them is in medical diagnosis. Today, many diseases are regarded as dangerous and deadly. Heart disease, breast cancer, and diabetes are among the most dangerous ones. This paper investigates 168 articles associated with the implementation of data mining for diagnosing such diseases. The study concentrates on 85 selected papers which have received more attention between 1997 and 2018. All algorithms, data mining models, and evaluation methods are thoroughly reviewed with special consideration. The study attempts to determine the most efficient data mining methods used for medical diagnosing purposes. Also, one of the other significant results of this study is the detection of research gaps in the application of data mining in health care.

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.

The introduction of Large Language Models (LLMs) in the healthcare sector is a major step, which can for sure transform all aspects of medicine including practice, research, and education. This review of publications presents the first 200 most cited articles which were obtained from a Scopus search on the terms "large language models" and "healthcare". The purpose was to elucidate the knowledge and trends in this fast-growing area. The analysis looks at the yearly publications, the main journals, the most prominent authors and institutions, the leading research areas, the methodological approaches used, and the ethical and regulatory issues that are the most talked about. The results show an increase in the number of scholars interested in this field, especially in 2023 and 2024. Besides that, it was found that there is a lot of high-impact publications going on in the leading multidisciplinary and specialized medical journals areas. The largest research areas that were found are: clinical trials and their outcomes, ethical and governance frameworks, educational integration, and technological advancements and surveys. The research is mainly focused on testing the performance of LLMs like ChatGPT in particular medical tasks but in the background, there are profound concerns about their accuracy, bias, and safety. This review presents the current knowledge state, points out the most active and leading research fronts, and recognizes the gaps with future directions hence it offers the most basic reference for researchers, doctors, and policymakers who will be dealing with the LLMs incorporation into the healthcare system.

The analysis of this research is based on the comparative technical efficiency processes of computed tomography (CT) and magnetic resonance imaging (MRI) service delivery in the seven corresponding regions of the National Health Service (NHS) in England. Two inputs—estimated numbers of radiographers and scanner systems—and one output—the volume of exams performed (2022/23)—were used in a Data Envelopment Analysis (DEA). Four distinct DEA models: CCR, BCC Input-Oriented, BCC Output-Oriented, and the Additive model were used to compute the efficiency. The outcome of the analysis was that the majority of the NHS regions witnessed the relative technical efficiency of a high level. For the MRI services, London and the East of England turned out to be the most efficient decision-making units (DMUs). The East of England and the Midlands scored the highest in efficiency for CT services. This implies that there might be certain factors like economies of scale, specialized high-throughput centers, and optimal resource allocation strategies that substantially contribute to such performance. The identified benchmarks, although performance is overall strong, provide health policy with insights. The results point out specific regions whose operational practices can act as a model for others, implying that targeted efficiency improvement is possible without the need for increasing the resource levels but rather through better use of existing assets.

This paper assesses the comparative technical efficiency of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) service delivery in all U.S. states and the District of Columbia, totaling 50. Data Envelopment Analysis (DEA) was applied and two inputs were used—the estimated number of scanners and radiographers—and one output—the estimated annual volume of exams. Efficiency was calculated according to the CCR, BCC Input-Oriented, BCC Output-Oriented, and Additive models. The investigation disclosed that almost every U.S. region performs relatively efficiently in a technical way to a great extent for MRI and CT services. Wyoming and Montana stood out as the best-evaluated decision-making units for MRI. In the case of CT, Montana once more was the winner. Consequently, these states with a small population manage to get the best results from their resources. The high scores all over the country signal a healthcare facility that is very good at turning imaging resources into the volume of service. The U.S. is rated highly in technical efficiency with respect to diagnostic imaging, but this is probably the result of a system that rewards high-volume service production. The study reveals an important difference between technical efficiency and wider value, implying that high performance in volume output is not automatically equal to cost-effectiveness or best patient outcomes. Quality and cost metrics should be incorporated in the future research.