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 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.