Journal of

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Background: Knowledge management plays an imperative role in the success of organizations. Many factors such as organizational culture affected on knowledge management. Therefore, this study aims at investigating the association between dimensions of organizational culture with knowledge management.

Materials and Methods: A cross-sectional descriptive analytical study was conducted in 2013. Three hundred twenty two employees of three hospitals related to the Zahedan University of Medical Sciences selected through a stratified-randomized sampling. Standard instrument of organizational culture and a self-designed questionnaire of knowledge management were used for data collection. Collected data was analyzed using SPSS version 18 by descriptively and inferential statistics methods.

Results: study results indicated that there was a positive and significant association between organizational culture and knowledge management. Also, results on other objectives pointed out a positive and significant association among dimensions of organizational culture (clan, market, adhocracy) and knowledge management. There was a negative and significant relation between organizational bureaucratic culture and knowledge management.

Conclusion: Organizational culture is one of the most important tools of a successful implementation of knowledge management in organizations. Modifications of organizational culture in health care teaching hospitals of Zahedan University of medical sciences should be set towards establishing knowledge management considering organizational tribe culture and organizational adhocracy culture more than other cultures.

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Background: Assessment of appropriate and inappropriate services offered at the hospital is a very important topic to improve resource allocation. Thus, this study performed to assess inappropriate admission and length of stay to modify extra costs and effective resource management.  
 
Materials and Methods: This study was a descriptive-analytic one which conducted as a cross sectional study in the first half of 2017. The Appropriateness Evaluation Protocol(AEP) was used to collect data. A total of 420 patients hospitalized in Haft Tir and Firoozgar Hospitals were selected using stratified sampling method. collecting data was analyzed using descriptive and analytical statistics by SPSS₁₈.
 
Result:  391 individuals were admitted appropriately and 29 were classified as inappropriate admission. The rate of inappropriate admission estimated about 7% in the hospitals. female Sex, type of admission, the length of admission and place of patient residence had effect on prediction of inappropriate admission rate (p ≤ 0.05).
 
Conclusion: Considering the high percentage of inappropriate admission and stay length of patients as well as high costs of health services in these hospitals, the problems can be greatly reduced using proper planning, admissions management between the hospital units.

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Background: The admission unit is the main entrance of the hospital and the first patient communication with the hospital is through this unit. The waiting time of patients, which is one of the main consequent of this unit, is not only one of the important factors affecting the satisfaction of the patients, but also is one of indicators of the quality of service of the hospital. This study aimed to provide scenarios at reducing patients’ waiting times.
 

Materials and Methods: This research in terms of methods and goals was a descriptive and an applied one, respectively. This study performed on 110 patients who had been admitted to the Mohb-e-Mehr hospital during 70 days and were uniformly trained on all days of the week. Information was also obtained using observation and data recording in prepared forms. The simulation model was designed and implemented with the Arena 14 software.

Results: Based on research findings, the para-clinical unit and the waiting room for hospitalization were two main bottlenecks in the studied system. In order to solve the problem, for each of the above units, a scenario designed and simulated. The implementation of these tests revealed that the proposed scenarios in comparison with the existing conditions had better results in reducing the waiting time and also increasing the number of admitted patients.

 
Conclusion: Improvement of the therapeutic processes will occur through the recognition of the hospital services system and analysis of the bottlenecks and its weakness points. According to the results, an increase in the number of para-clinical unit staff and hospital beds improves the hospital admission function. The implementation of mentioned scenarios reduces waiting time for patients by about 78% and reduces the waiting time for emptying the bed by about 50%.

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Background: with increasing demand for treatment, patients are monitored with help of Internet of Things(IOT). Patient's monitoring devices and technologies include heart rate measurement, blood pressure measurement, blood glucose and other vital signs. The purpose of study is to provide a model of clustering patient physical monitoring gadgets and apps in Healthcare Internet of Things (HIOT) environment using data mining techniques, so based on the needs and characteristics of the user, the more appropriate results of choosing technologies acquired.

Materials and methods: This study is a review and functional since its result. The data includes 6 unique features of 60 selected technologies including function, price, connectivity route, power supply, location and type of use that has been extracted from R&D and advertising sites of technologies and also relevant articles. data analysis method is clustering technique and K-medoids algorithm. to identify the most effective features, random forest algorithm has been used.

Results: the proposed clustering model takes into account 6 as inputs and clusters gadgets and apps in accordance with selected characteristics as the model outputs. clustering problem data is clustered in 4 categories.  Silhouette index is 0.45, which indicates the validity of the model. The type of application and then the price had the greatest impact on clustering.

Conclusion: By this model, patients or users can find the most appropriate technology based on the type of disease and other effective features, such as price. So with accurate physical and momentary monitoring, disease progression decrease and prevention of disease will improve.