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Supply chain management in healthcare is evaluated with a particular focus on the distribution of medicines from a wholesaler to clinics. Currently, there are issues with service levels to clinics that need addressing. The value of the paper arises from providing a detailed analysis of a healthcare supply chain in the developing world and Diagnosis the parameter involved in inventory.

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Background and Objectives: Teaching hospitals often face budget limitations and lack of investment. Therefore, the optimal allocation of budget and resources plays an important role in improving the performance and service quality of these hospitals. This research aims to evaluate the performance and determine the efficiency of medical science university hospitals in Tehran. Methods: This study identified and categorized 47 effective factors in the performance assessment of hospital units using the Preference Ranking Organization METHod for Enrichment of Evaluation II (PROMETHEE II) method. Moreover, the performance of 40 medical science university hospitals in Tehran was evaluated using an outcome-based model of data envelopment analysis (DEA) with 4 input and 8 output factors and the assumption of scale-dependent efficiency. The hospitals were also ranked according to the Andersen-Petersen (AP) method. Results: PROMETHEE II results identified 12 factors as the most important in hospital performance evaluation. DEA indicated that 16 hospitals had performance scores below 1 and are thus inefficient. The AP method identified Hospital 28, which had an efficiency value of 4.533, as the best hospital. Conclusion: Given the results of this approach and the identification of a considerable number of teaching hospitals as inefficient hospitals, top managers of medical centers must adopt the necessary planning to improve system performance and realize the optimal application of resources.

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BackgroundDisease diagnosis is complicated since patients may demonstrate similar symptoms but physician may diagnose different diseases. There are a few number of investigations aimed to create a fuzzy expert system, as a computer aided system for disease diagnosis.MethodsIn this research, a cross-sectional descriptive study conducted in a kidney clinic in Tehran, Iran in 2012. Medical diagnosis fuzzy rules applied, and a set of symptoms related to the set of considered diseases defined. The input case to be diagnosed defined by assigning a fuzzy value to each symptom and then three physicians asked about each suspected diseases. Then comments of those three physicians summarized for each disease. The fuzzy inference applied to obtain a decision fuzzy set for each disease, and crisp decision values attained to determine the certainty of existence for each disease.ResultsResults indicated that, in the diagnosis of seven cases of kidney disease by examining 21 indicators using fuzzy expert system, kidney stone disease with 63% certainty was the most probable, renal tubular was at the lowest level with 15%, and other kidney diseases were at the other levels. The most remarkable finding of this study was that results of kidney disease diagnosis (e.g., kidney stone) via fuzzy expert system were fully compatible with those of kidney physicians.ConclusionThe proposed fuzzy expert system is a valid, reliable, and flexible instrument to diagnose several typical input cases. The developed system decreases the effort of initial physical checking and manual feeding of input symptoms.

Background: Artificial neural networks (ANNs) can be used in various medical cases due to their high performance in learning the relationship between variables. Periodontal diseases are common oral infectious diseases that can cause tooth loss, if not treated.