Journal of

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Background: A variety of methods are available for identifying and measuring adverse events and medical errors in healthcare. The aim of this study is to review these methods with their strengths and weaknesses.

Methods: Electronic databases including Medline, Embase, Google Scholar and Iran Medex were searched to identify and summaries relevant studies.

Results: Different methods have been used to identify adverse events and their nature, causes and consequences. Record review seems to be the most common and the best method for measuring the rate and consequences of adverse events. However, this method is relatively expensive and time consuming and still underestimates the rate of adverse events. It is also not suitable for identifying the underlying causes of adverse events. The other method commonly used is reporting system including critical incident reporting system. This method is relatively cheap and more suitable for exploring the underlying causes of adverse events, but it is not suitable for identifying the rate of adverse events as it may underestimate many adverse events. Other methods include critical incident technique, interview, questionnaire, direct observation and review of claims and none of them are suitable for identifying the rate of adverse events but they might be useful for other purposes including assessment of the causes of adverse events.

Conclusion: Several methods can be used for study of adverse events. No single method can serve all purposes. Each method has some strengths and weaknesses. Using a combination of methods is more suitable, but this approach is more expensive and time consuming. Which combination of methods are more appropriate, depends on several factors including the aim of the study and resources available.

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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 purpose: The Emergency Operations Center (EOC) is a centralized facility designed to coordinate disaster management activities across all phases of the disaster management cycle. EOCs play a vital role in facilitating efficient and effective responses by coordinating information and resources. This study aims to highlight the importance of establishing a standardized framework for EOCs in Iranian universities of medical sciences and to examine its key dimensions.
Methods: This study is a commentary article that compiles information from published literature on the framework of emergency operations centers. It focuses on two prominent global frameworks: the World Health Organization’s Public Health Emergency Operations Center Framework and the U.S. Federal Emergency Management Agency’s Emergency Operations Center Guidelines. The study emphasizes the necessity of creating a standardized framework for emergency operations centers in Iranian universities of medical sciences.
Results: EOCs across different countries vary in terms of their missions, authorities, and resources. Consequently, the frameworks used in these centers are not uniform, and are largely shaped by the governance and regulatory requirements specific to each country.
Conclusion: A standardized framework, tailored to the specific needs and conditions of Iran, is essential for EOCs at universities of medical sciences. Such a framework could enhance the operational capabilities of these centers during incidents and disasters, improving their responsiveness and effectiveness.