فهرست مطالب

  • Volume:6 Issue: 3, 2019
  • تاریخ انتشار: 1398/04/10
  • تعداد عناوین: 5
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  • Saeed Saeedinezhad, Amirreza Naghsh* Pages 85-95

    The purpose of emergency management planning is to enable the management to makequality decisions under the pressure of time to avoid or minimize the damage Planning isa complex issue, which involves key actions and critical decisions. It improves the responseto the effects of a disaster by organizing timely delivery, effective rescue, relief and timelyand reliable assistance; also, it ensures that the right people are functioningat the right time.Effective plans are also intended to provide and possibly include the resources and proprietaryfunds that are provided through powerful rules. Written and well documented programsincrease the likelihood of successful outcomes.This research aims to develop an information technology (IT) maturity model for prehospitalemergency management that merges the most known IT frameworks’ practices.Our proposal intends to help the organizations overcome the current limitations ofmultiframework implementation by informing the organizations about the frameworks’overlap before their implementation. ITIL is the most popular “best practices” framework formanaging Information Technology (IT) services. However, not only implementation of ITILis very difficult, but also there are no recommendations and guidelines for it. As a result, ITILimplementations are usually long, expensive and risky. In this paper, we proposed a maturitymodel to assess an ITIL & COBIT implementation and provided a roadmap for improvementbased priorities, dependencies, and guidelines. We finally concluded that considering ITIL &COBIT implementation in pre-hospital emergency management could be very useful.

    Keywords: IT Services, Pre-Hospital Emergency Management, COBIT, ITIL
  • Saghar Foshati*, Ali Zamani, Malihe Sabeti Pages 96-105
    Introduction

    Diabetes mellitus is a prevalent disease and its late diagnosis leads todangerous complications and even death. One of the serious complications of this diseaseis diabetic retinopathy, the leading cause of blindness in the developed countries. Becauseof slowly progressive nature and lack of symptoms in the early stages of the disease, it isessential to predict the probability of developing diabetic retinopathy promptly to implement the appropriate therapy.

    Methods

     Our dataset contains 29 extracted features from 310 patients with types 2 diabetic disease, 155 patients of whom sufferred from diabetic retinopathy. The patients were selected randomly from Motahari clinic in Shiraz, Iran between 2013 and 2014. First, the genetic algorithm, (GA) as a feature selection process, was implemented to select the most informative features (high-risk factors) for prediction of diabetic retinopathy. Then, three well-known classifiers including k-nearest neighbors (kNN), support vector machine (SVM), and decision tree (DT) were applied to the optimized dataset for classification of the two mentioned groups.

    Results

    Our finding showed that GA selected 13 factors for better prediction of diabeticretinopathy; these factors were the duration of the disease, history of stroke, family history, cardiac diseases, diabetic neuropathy, LDL, HDL, blood pressure, urine albumin, 2HPPG, HbA1c, FBS, and age. Given the selected risk factors, the classification accuracy was obtained 69.35%, 81.29% and 96.13% by SVM, DT, and kNN, respectively. Our results showed that kNN had the highest accuracy in the prediction of diabetic retinopathy compared to SVM and DT, and the difference between kNN and the other algorithms was statistically significant.

    Conclusion

     The proposed approach was compared and contrasted with recently reportedmethods, and it was shown that a considerably enhanced performance was achieved. Thisresearch may aid healthcare professionals to determine and individualize the required eyescreening interval for a given patient.

    Keywords: Diabetic Retinopathy, Feature selection, Genetic algorithm, classification
  • Mohammad Reza Zare Banadkouki* Pages 106-118
    Introduction

     Given the key role of universities and higher education institutes in thesocial and economic development of countries, it is necessary to evaluate their performance regularly with appropriate methods and measures. Since research and science production are among the essential functions of universities, measurement of scientific outputs is an important part of university performance evaluation. The aim of this study was to rank the Iranian medical universities by scientometric indicators.

    Methods

     One way to evaluate the scientific outputs is to use one of many scientometricindicators defined over the years for quantitative and qualitative evaluation of the researchers. This approach can also be expanded for evaluation at the university level. In the descriptivesurvey presented in this paper, 152597 scientific articles published by the authors affiliatedwith 50 Iranian medical universities were investigated. The scientific output data extractedfrom the Scopus database of each university were analyzed separately using the cumulativenumber of scientific papers, number of citations, citation impact, h-index, m-parameter,and g-index. The universities were then ranked according to each indicator. This study isan applied research based on the results. The sample number in this study was all scientificoutput of the universities studied.

    Results

     Among the studied universities, Tehran University of Medical Science ranked firstin terms of cumulative number of scientific papers, citations, h-index, and g-index, AlborzUniversity of Medical Science ranked the first in terms of m-parameter, and Arak Universityof Medical Sciences ranked the first in terms of citation impact.

    Conclusion

     The obtained rankings were compared with the results of Islamic World ScienceCitation Database (ISC) ranking system. This comparison showed that the rankings of Iranian medical universities based on cumulative number of papers, number of citations, and h-index were strongly correlated with the results of ISC ranking system.

    Keywords: Scientific Performance, Medical University Ranking, Scientometric, Scientific Output
  • Zahra Kavosi, Effat Norouz Sarvestani, Najmeh Bordbar, Mohsen Bayati, Farhad Lotfi* Pages 119-125
    Introduction

    One of the most reliable sources of financing healthcare costs is healthinsurance. Covering all the services by basic health insurance is not affordable economically, so that some services are covered by supplementary health insurances. This study aimed to determine the factors influencing buying the different levels of Kowsar supplementary health insurance by the staff of Shiraz University of Medical Sciences in 2014-2015.

    Methods

    This is a cross-sectional study. Two data collection forms were used to collect thedata. A sample size of 500 was determined using the rule of thumb. The individuals wereselected via using two-stage stratified and systematic sampling. To do the estimation, theordinal logistic regression model (link function was logit) was specified by the one-sidedsignificant variable tests at the first step. Then, the independent variables were examined by the link test, and the linear relationship among variables was also investigated. The software Excel 2010 and STATA 11.0 (stata corp LLC) were used in this paper.

    Results

     The findings showed that among the people with supplementary insurance, themajority were males (60%), married (85%), with the basic Tamin Ejtemaei insurance (72.3%). Among those who have not chosen the supplementary health insurance, the largest number were women (69%), unmarried (53%), and insured by Tamin Ejtemaei (80%), respectively. The findings suggest that some factors such as the age, gender, income and cost of insurance packages are the most influential factors on buying different levels of health care insurance. In the first model that included people with supplementary insurance, the income elasticity was significant and positive (Beta=3, P=0.047) and price elasticity of demand was negative (Beta=-0.06, P=0.001). In the second model that complemented those with and without supplementary insurance, the income elasticity was insignificant (Beta=2.46, P=0.085), and the demand price elasticity was negative (Beta=-0.06, P=0.001).

    Conclusion

     The economic factor seems to be the most influential factor in choosingsupplementary insurance. Since this problem causes the low-income households not to usethe insurance; therefore, the government is required to allocate some subsidies for low income household to be covered by supplementary health insurance for special services.

    Keywords: Supplementary Health Insurances, None for Profit Insurance, Logistic Models, Health Financing
  • Payam Shojaei, Maryam Najibi, Najmeh Bordbar, Peivand Bastani*, Amin Amiri Pages 126-132
    Introduction

     Performance appraisal and efficiency evaluation of schools and universitieshave had remarkable growth over the past two decades. The present study evaluated theperformance of the schools of Shiraz University of Medical Sciences.

    Methods

     This is a cross-sectional study, conducted in 2017 on 10 schools of Shiraz University of Medical Sciences using data of the year 2016 related to 5 inputs and 12 outputs. In order to determine the weights of the inputs and outputs, fuzzy weighting was performed based on the experts’ views. Then, by utilizing an integrated approach of data envelopment analysis (DEA) and goal programming (GP), the efficiency of the schools was determined using model Minimax. The final rankings were made by employing the super-efficiency ranking method (Anderson-Peterson). The results were exported using TORA software after producing the relevant linear models for each school. The software uses the notation and procedures developed in Taha Hamdi, Operation Research: an introduction, 5/e, Macmillan1992 ,.

    Results

    Results from the Minimax model, which presented the best answer, showed thatthe Schools of Dentistry, Pharmacy, Nutrition and Food Sciences, Paramedical Sciences, andHealth were efficient with respect to the 5 inputs and 12 outputs. By employing the superefficiency ranking method of Anderson-Peterson, the highest ranks and points were related to the Schools of Nutrition and Food Sciences, Paramedical Sciences, and Dentistry. The average efficiency score of the schools was 0.89

    Conclusion

     According to the results some schools must enhance their outputs. Thecontinuous evaluations and publication of research results leads to awareness of the relative status and ranks, and ultimately causes increased competition and efforts to improve the efficiency of the schools.

    Keywords: Zahra Kavosi, Effat Norouz Sarvestani, Najmeh Bordbar, Mohsen Bayati, Farhad Lotfi*