Journal of

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Background: In recent years, Hashemi Nejad Hospital was outsourced or insourced some of their services to private sector or will have decision to do it. Selection and assessment of suppliers in outsourcing of hospital services is a critical issue. In this study, selecting and evaluating suppliers for outsourcing services in hospitals was evaluated.
Materials and Methods: In order to achieve the goal, evaluating and selecting outsourcing service providers with studies and using opinion of the experts and medical experts, consisting of hospital manager, quality manager, HR managers, officials outsourced parts and other experts in this respect which includes 14 criteria. Identified criteria were clustered in three areas of service features, characteristics and criteria for communications suppliers in the supply chain; supplier selection problem is the problem multi-criteria decision. So, criteria were ranked and weighted using the Expert choice 11 software and AHP.
Results: Based on the study results, sub-criteria of the quality of service, management systems, customer care, and information security had greatest impact on the selection of suppliers and sub-criteria, geographic location, flexibility and problem solving had the lowest priority.
Conclusion: C supplier had the highest priority according to the communication criteria and A supplier had the highest priority according to two other criteria. In total, the supplier A had the first priority, supplier B had the second priority and supplier C had the third priority.

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Background: The complexity and intensity of environmental fluctuations combined with unexpected accidents and dangers have increased the probability of hospital supply chain disruptions. Supply chain resilience has been suggested as a strategy for dealing with such challenges and for continued provision of appropriate and efficient services in hospital at the time of disaster. The present study intends to identify the factors influencing hospital supply chain resilience.
Methods and Materials: This qualitative study was based on the content analysis of semi-structured interviews with 14 experts in the university hospitals in Bandar Abbas in 2018. Participants were selected through purposive and snowball sampling. The interviews continued until data saturation was reached. The obtained data from interviews were coded and analyzed using MAXQDA Software.
Results: After analyzing the data, the factors influencing hospital supply chain resilience were identified and classified into six main components and 30 themes. The main components included: staffs’ attendance, suitability, infrastructures safety, disaster management, support and capacity systems, and external factors.
Conclusion: Based on the obtained results, it can be concluded that staff training, disaster management planning, command system, and surge capacity are the key factors influencing hospital supply chain resilience. Therefore, they should be taken into consideration while planning to promote hospital supply chain resilience.
 

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Background: Scenario planning is one of the most crucial future study methods in uncertain and complex situations. Hospital supply chain resilience also requires an understanding of future events due to the complexity of relationships and exposure to unexpected circumstances. The purpose of this study is to formulate scenarios for the future development of hospital supply chain resilience.
Materials & Methods: This research is the second stage of research with a mixed approach, and it is in the category of normative scenarios based on intuitive logic. Participants in this study were purposefully selected from the experts of two hospitals. In the first phase, the impact-uncertainty questionnaire and the effect-uncertainty diagram were used to determine the critical uncertainties. After forming the scenario team, based on the impact diagram and the scenario matrix, The cause and effect relationships of the variables were determined in the second phase.
Results: Drivers of Hospital supply chain resilience were clustered into 14 main categories, and the results of the impact-uncertainty diagram showed that "people's culture" and "accident nature" play a more significant role in scenario development as critical uncertainties. Four scenarios were developed based on the opinion of experts for these two drivers.
Conclusion: four scenarios, "compatible," "turbulent," "broken," and "combative," were developed based on the critical drivers in supply chain hospital resilience. Use the inspirational feature of these scenarios can help managers in health and crisis management be more prepared to face the crisis. Scenarios based on intuitive logic can be used for futures studies in other areas of the health system.

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Background and Aim: As difficulties increase, the level of uncertainty and risk in the supply chain increases. Medicine is a strategic product and is directly related to community health. The aim of this study is to evaluate the risk factors of pharmaceutical supply chain with artificial intelligence methods.
Materials and Methods: By reviewing the texts and interviewed 6 adept experts who had a Master’s degree and Ph.D. and had experience between 7 and 15 years in the field of risk and pharmaceutical supply chain, risk factors were identified. Finally, using multilayered perceptron neural networks and support vector machines with polynomial linear kernel functions and radial base in two low-risk and high-risk classes were classified in Python software.
Results: 22 factors were identified and classified using neural networks in 5 categories: assets, network and transportation, government and market, strategy and supplier. Shift in interest and inflation, Changes in exchange rates, Inflexibility in production and disruption of customer service are the most important risks in the pharmaceutical supply chain, respectively. The results of evaluation criteria showed that the multilayer perceptron model had better performance than the support vector machines with linear, polynomial and radial basis functions.
Conclusion: The results showed that artificial neural networks are able to classify pharmaceutical supply chain risk factors with acceptable accuracy. As a result, classification of risk factors with an accuracy of 97/07% indicates the high ability of multilayer perceptron network in risk assessment of pharmaceutical supply chain.

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Background and Objectives
In recent years, medicine supply chain management has become more significant, especially after the Covid-19 pandemic. The most important issue is supply chain cost control. If the drug inventory is not properly managed, it will lead to issues such as the lack of inventory of certain drugs, provision of excess inventory, increased costs, and, finally, patient dissatisfaction.
Materials and Methods
In this study, an attempt has been made to predict and manage the pharmaceutical needs of hospitals using an efficient deep-learning algorithm. The drug consumption data for ten years of Besat General Hospital in Hamedan are extracted from the HIS database. As a case study, the accuracy of the predictive model is evaluated, especially for cefazolin. We use a deep model to analyze the medical time-series data efficiently. This model consists of a Long Short-Term Memory network, which can sufficiently recognize the change history in time-series prediction applications. The proposed model with many adjustable parameters in the deep architecture will bring good performance to overcome the complexities of the learning problem.
Results
Using the deep learning method can increase robustness by reducing the effects of complexity and uncertainty in medical data. The average forecasting error for the proposed method is 0.043, and the measured values for RMSE, MAE, and R2  are 0.335, 0.260, and 0.851, respectively.
Conclusion
A comprehensive comparison between some other predictive methods and the implemented model shows the outperformance of the proposed approach. Additionally, the evaluation results indicate the efficiency of the proposed approach.

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Background and purpose: In response to evolving external environments, organizations must renew their valuable resources to sustain competitive advantage. Dynamic capabilities empower organizations to effectively navigate these continual changes. Essentially, dynamic capabilities foster a stable behavioral orientation within organizations, facilitating integration, reformulation, renewal, and reconstruction of resources and capabilities, particularly enhancing and revitalizing core capabilities in response to dynamic environments to achieve sustainable competitive advantage. This study explores the influence of dynamic capabilities on constructive collaboration and supply chain performance within healthcare centers. Dynamic capabilities are categorized into four perspectives: sensitivity, learning, coordination, and integration. Constructive collaboration serves as a mediating variable, while technological orientation acts as a moderating variable in the model.
Methods: This study adopts an applied purpose and descriptive-survey method. The statistical population comprises employees at Ayatollah Taleghani Gonbadkavus Hospital. Using a questionnaire adapted from Mandal's (2022) study, the research establishes relationships between variables, categorized as descriptive-analytical. The questionnaire's validity was assessed using convergence and divergence methods, and reliability was confirmed using Cronbach's alpha and composite reliability. Data analysis employed structural equation modeling and Smart-PLS software.
Results: Data analysis reveals a significant relationship between the learning, coordination, and integration perspectives of the hospital and constructive collaboration. However, no significant relationship is observed between the sensitivity perspective and constructive collaboration, nor between constructive collaboration and the performance of the healthcare system's supply chain. A significant relationship exists, and technological orientation does not moderate the relationship between the sensitivity perspective and learning with constructive collaboration, but it moderates the relationship between the coordination and integration perspective with constructive collaboration.
Conclusion: Improvements in collaborative efforts across various hospital departments, decreased risks of medical errors, enhanced service quality, and elevated professional status of staff are among the outcomes of assessing the performance of hospitals' sustainable supply chains.

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Background and Purpose: Health status is undeniably one of the most critical indicators of social development and progress. Providing healthcare poses a significant challenge for human life, and managing the healthcare supply chain is of strategic importance. The aim of this research is to analyze and compare the results of meta-synthesis with thematic analysis in identifying the risks of the pharmaceutical industry's supply chain.
Methods: This research follows a qualitative approach, utilizing both meta-synthesis and thematic analysis to identify supply chain risks in the pharmaceutical industry. In the first step, a meta-synthesis and systematic review of related studies over the past twenty-three years were conducted, identifying one hundred articles, which were refined to twenty-six key articles for the research. In the next step, risks specific to Iran's pharmaceutical supply chain were identified through thematic analysis and semi-structured interviews with experts, using targeted sampling. Finally, the results from these two approaches were compared and analyzed.
Results: The meta-synthesis approach identified ten general supply chain risks in the global pharmaceutical industry. Similarly, the thematic analysis approach identified ten specific supply chain risks in Iran's pharmaceutical industry. Six risks were common to both approaches: low quality of raw materials, complexity and incompatibility of information systems, supply of foreign currency and financial payments, transportation and insurance issues, increase in the price of raw materials, and unavailability of medicines. These common risks are critical for both the global and Iranian pharmaceutical supply chains.
Conclusion: Stakeholders in Iran's pharmaceutical supply chain (including hospitals) should prioritize managing these six common risks to improve supply chain performance. Additionally, they should focus on the four unique risks identified through thematic analysis specific to Iran's pharmaceutical supply chain, applying appropriate control measures and activities.

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Background and purpose: The complexity and extreme fluctuations in the healthcare environment, along with the occurrence of unforeseen disasters and risks, have increased the likelihood of disruptions in hospital supply chains. Strengthening supply chain resilience is a key strategy to mitigate these challenges and ensure the continued delivery of efficient and effective hospital services during crises. This study aims to conduct a structural analysis of the factors influencing the resilience of the hospital medical equipment supply chain using the Fuzzy MICMAC approach.
Methods: This mixed-method (qualitative-quantitative) study included faculty members knowledgeable in the field, heads of medical equipment departments in universities and hospitals, senior managers from medical equipment manufacturing and importing companies, and healthcare administrators. Data were collected through checklists and semi-structured interviews. Fuzzy matrix completion and MICMAC analysis were used for data processing, performed using Fuzzy MICMAC software.
Results: Among the identified factors, logistics management was found to have the highest impact on the resilience of the hospital medical equipment supply chain. In contrast, factors such as integration and coordination, cooperative relationships, competition, flexibility, human resource management, and risk/crisis management had the lowest direct impact. Environmental conditions, transparency and protection, information-sharing systems, and human resource management were identified as the least affected factors. Economic factors emerged as a highly influential indirect factor, significantly affecting supply chain resilience.
Conclusion: To enhance the resilience of hospital medical equipment supply chains, logistics management and economic factorsmust be prioritized. Additionally, agility and speed, risk/crisis management, competition, and the development of integrated and cooperative relationships play a moderately indirect role and should be considered in strategic planning.