Blood supply managers in the blood supply chain have always sought to create enough reserves to increase access to different blood products and reduce the mortality rate resulting from expired blood. Managers’ adequate and timely response to their customers is considered vital due to blood perishability, uncertainty of blood demand, and the direct relationship between the availability/lack of blood supply and human life. Further to this, hospitals’ awareness of the optimal amount of requests from suppliers is vital to reducing blood return and blood loss, since the loss of blood products surely leads to high expenses. This paper aims to design an optimal management model of blood transfusion network by a synthesis of reusable simulation technique (applicable to all bases) and deep neural network (the latest neural network technique) with multiple recursive layers in the blood supply chain so that the costs of blood waste, return, and shortage can be reduced. The model was implemented on and developed for the blood transfusion network of Khorasan Razavi, which has 6 main bases active from October 2015 to October 2017. In order to validate the data, the data results of the variables examined with the real data were compared with those of the simulation, and the insignificant difference between them was investigated by t test. The solution of the model facilitated a better prediction of the amount of hospital demand, the optimal amount of safety reserves in the bases, the optimal number of hospital orders, and the optimal amount of hospital delivery. This prediction helps significantly reduce the return of blood units to bases, increase availability of inventories, and reduce costs.
Introduction: Service quality is one of the most important management aspects of service organizations, and customer-centricity is the first strategy of all organizations worldwide. Therefore, the present study aimed to Prioritization of the affecting components of the patient experience evaluation of healthcare services in hospitals affiliated with Mashhad University of Medical Sciences using the fuzzy analytic hierarchy process (FAHP). Methods: The present cross-sectional study was carried out in hospitals affiliated to Mashhad University of Medical Sciences in 2022. The data collection instrument included a researcher-made questionnaire including questions on demographic variables and pairwise comparison tables consisting of 12 dimensions related to the components affecting the patient experience evaluation, which 30 members of the expert panel answered. The expert panel members included heads and managers of hospitals, managers and executive experts of hospitals, and faculty members of health and medical services management. The collected data were analyzed using Excel software to compare and rank the contribution of each factor affecting the Prioritization of the patient experience evaluation of healthcare services using fuzzy analytic hierarchy process. Results: According to the panel of experts, the quality of the nurse-patient relationship was the most critical priority (0.28), followed by the quality of the physician-patient relationship (0.24), provision of medical services (0.17), provision of information to the Patient (0.09) and method of pain management (0.07). The lowest priority was access to the necessary medications (0.0004). Conclusion: According to the results of the present research, hospital managers can have the most significant effect on improving the quality of hospital services and, thus, patient satisfaction by employing experienced and specialized nurses and physicians or by empowering them to establish better communication with patients and provide better medical services.
Inventory managers in the blood supply chain always seek timely and proper response to their customers, which is essential because of the perishability and uncertainty of blood demand and the direct relationship of its presence or non-presence with human life. On the other hand, timely and regular delivery of blood to consumers is vital, as the weakness in delivery and transportation policies results in increased shortages, returns, blood loss and significant decrease in the quality of blood required by patients. Given the significance of this for the blood transfusion network, the paper tried to design a comprehensive and integrated optimal model of blood transfusion network logistics management by blood group to reduce the cost of losses, returns and blood shortages. This model is divided into two parts: Inventory management and routing. A combination of simulation techniques and neural network with several recurrent layers was used to evaluate the optimal inventory management and a multi-objective planning model was designed to determine the delivery and distribution of blood to consumers. The model designed was implemented in Khorasan Razavi Blood Transfusion Network with a main base, six central bases and 54 hospitals. Solving the model led to estimating the f consumer demand, the optimal value of target inventory and re-ordering point of central bases and hospitals, and blood distribution from the supplier to its consumers that decreased the units of blood returned to bases, increased inventory availability, and reduced costs significantly.
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