Journal of

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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.