Journal of

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Background: Emergency department (ED) is the first place for providing diagnostic and therapeutic services to emergency patients. Due to importance of speed and accuracy in providing services the proper allocation of resources, the department must consider this matter in a particular way. Planning Emergency resources implements regardless of patient overcrowding which occurs at different times. In conclusion the emergency department may faces lack of resources and it results in delay of providing services, a whole mess in functions and decreasing in quality of services. This study is aimed to overcome these problems by suggesting a model for predicting the number of arrival patients at ED. Materials and Methods: The number of arrival patients is predicted based on the data colleted by counting arrival patients and using the data mining technique and neural network model (Multi-layer Perceptron). Results: The number of arrival patients during whole days of a week and 24 hours a day were calculated by sorting out 1, 2 and 3 priorities . The highest number of arrival patients counted was for Saturdays and the lowest for Fridays. Holidays and non-holidays` number of arrival patients differ . The number of arrival patients on formal holidays was similar to Fridays. The highest number of arrivals was between 9 am and 11 and also between 20 pm and 23 pm and the lowest arrivals was between 2 am and 7 am. Conclusion: prediction the number of ED arrival patients can be used for estimating required sources and distributing them appropriately and improving quality of services.

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Background: Visual disorders in old age is one of the most important factors in decreasing quality of life of older people. This study aimed to access the vision-related quality of life of older people in Tehran and to examine some of the underlying factors.
Materials and Methods: This was a population-based cross-sectional study among 566 older people aged 60 years and over, living in Tehran. A multistage cluster randomized sampling method was used to select study population, data was gathered using interviewing them at their home. NEI-25 VFQ (Visual-Functioning Questionnaire) was used to measure the vision-related quality of life of the participants. Multi-level linear regression analysis was used to data analysis.

Results: The average score of the vision-related quality of life was 80 out of 100. Among 12 dimensions of Visual-Functioning Questionnaire, the dimensions of color vision (CV), vision specificsocial functioning (VSSF) and peripheral vision with the scores of 96.8, 96.7 and 95 gained the highest and the dimensions of driving and vision specific dependency (VSD) with the scores of 42.6 and 50.3 had the lowest scores, respectively. Significant associations were observed between being a women, older, and having a lower education with poorer vision-related quality of life. 

Conclusion: This study results provides an evidence for policymakers in prioritizing visual health services based on more impaired visual function and also at risk older people.