نشریه مدیریت سلامت
پیاپی 67 (بهار 1396)

Hospital information systems play an important role in improving coordination between different sectors of a hospital and increasing the efficiency of its managerial processes. Despite great advantages of these systems, many hospitals have encountered some obstacles at the implementation stage that deviate the systems from their primary goals. These events caused the hospital information systems to fail. The aim of this paper is to present a model for identification of these events and categorize them and finally prioritize them in order to manage or hinder their occurrence.
Firstly, by reviewing the related literature, we identified the risks that possibly can occur in this area. After extracting the risks, their consequences have been quantified by means of expert judgment. Experts were selected from Milad hospital. Then the risk's priorities have been determined by applying fuzzy analytic hierarchy process. The reliability and the validity of the related questionnaire have been determined
Between four identified factors, the highest scores were related to the managerial factors with the weight of 0.84 while the organizational factors gained the lowest score by the weight of 0.01. After multiplying the probability of each risk factor to its consequence, the priorities changed.
Employing qualified personnel in the field of health information systems, providing necessary trainings to them and preparing technical infrastructure before the implementation of the system along with the assignment of the required resources, and paying special attention to interpersonal relationships are very crucial in the reduction of failure risk of hospital information system implementation project.

Meta-heuristic and combined algorithms have a great capability in modelling medical decision making. This study used neural networks in order to predict Type 2 Diabetes (T2D) among high risk individuals.
This study was an applied research. Data from 545 individuals (diabetic and non-diabetic), in Diabetes Clinic of Hamedan University of Medical Sciences, were used to develop predictive diabetes models. Memetic algorithms which are a combination of genetic algorithm (GA), local search algorithm, and neural networks were applied to update weights and improve predictive accuracy of neural network models. In the first step, optimum parameters for neural networks such as momentum rate, transfer functions, and error functions were examined through trial and error and other studies.
The preliminary analysis showed that the accuracy of neural networks was 88 percent. The use of memetic algorithm improved its accuracy to 93.2 percent. Among models, regression model had the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve were 96.2, 95.3, 93.8, 92.4, and 0.958, respectively. These parameters for GA were 98.0, 84.8, 88.6, 98.2, and 0.952 and for the logistic regression model were 95.6, 84.5, 94.7, 87.0, and 0.916, respectively.
Models developed by neural networks have a higher predictive accuracy than the regression model. The results of this study can contribute to risk management and planning of health services by providing healthcare decision makers with more accurate predictive models based on clinical and life style characteristics of individuals.

Hospital Information System (HIS) is universal software for integrating information of patients, with the aim of improving the quality and reducing the costs. So, given the importance of these systems, continuous evaluation of hospital information is essential. The main goal of this study is to find and select pattern(s) suited for evaluating efficiency of these systems.
In this study, we evaluated the efficacy by novel multidimensional Data Envelopment Analysis (DEA) methods. Statistical population included 28 of 34 hospitals supported by Mashhad University of Medical Sciences using hospital information systems.
The estimated scores of different models showed that various methods are different from each other, as the average and standard deviation of scores of the superior performance model and the conventional model were 0.2656 ± 0.74 and 0.0541 ± 0.04 respectively. However, despite this difference in scores, linear relationship between all the relationships (except for one relationship) was significant (p-value Based on theoretical justification and practical interpretation of the results and to cut the complexity in applying the operational model of performance evaluation, the superior performance method could be introduced as the preferred method to evaluate hospital information systems.

It seems permanent presence of resident specialists affects the performance indicators of emergency department (ED). Therefore, the aim of this study is evaluating the performance of EDs in hospitals affiliated to Urmia University of medical sciences following the implementation of specialist residency program under the health sector evolution plan.
The present study is an applied type and descriptive-analytic. In this study the data related to performance indicators of 23 public hospitals of West Azerbaijan province is collected through data collection forms for two six month period before and after resident specialist presence and analyzed by using SPSS 22 software, Repeated Measure ANOVA test. Then semi-structured interviews were conducted with 15 personnel of the two selected hospitals to evaluate the causes of good or poor performance in the best and in the weakest hospitals in aspect of performance and analyzed by using MAXQDA 10 software, framework analysis technique.
The presence of resident specialists resulted in relative improvement of performance indicators such as «Mean time of triage (level 1) » (0. 17 Minutes decrease) and «the percentage of patients were disposed during 6-hour» (1. 36% increase). (P-Value > 0/05) The results of the interviews show that permanent presence of emergency specialists, management method in emergency department, how to triage patients and counseling, providing facilities and required physical space, training classes for staff, available diagnostic facilities and personnel motivation are factors resulted in overall improvement of ED performance In the best hospital.
Generally, if the specialist residency program implemented correctly results in improvement of performance indicators in ED. Meanwhile, physicians’ motivation is a requirement for better implementation of this program.

Medical tourism as one of the dimensions of sustainable development to help the country's economy dynamics. Little research has been conducted in the field of medical tourism. Based on this fact there is a lot of potential in the field medical tourist attraction in Iran, this study examines the factors in deciding the tourists to choose Iran as a country of destination.
This is a descriptive study of correlation kind. The statistical community, includes all visitors that came into the city of Shiraz for surgery and treatment. First, a preliminary study has been done through the distribution of a questionnaire among the 20 international medical tourists and variance estimated with the prototype at the level of 95% confidence. The sample size was calculated (96 cases). 87 people responded to the questionnaire. The questionnaire was designed based on the existing literature reviewed research. To assess the validity of the questionnaire comments of experts was used. Cronbach's alpha coefficient is 81.0 which shows the necessary reliability of the questionnaire.
The results of the research show that the path coefficient of information search (0/62) is a very important effect on the choice of Iran as a country of destination in the health tourist. Macro factors and stimulating demand with the path coefficients respectively (0/31 and 0/12) are in the next ranking. The results show that the advertising and media have critical impact on the Iranian elections as a destination country in health. Macro factors and stimulating demand are next in rank.
Due to the importance of medical tourism and income creation of this industry, one of the ways to attract tourists and influence on decision making of them is advertising and information. We can increase the demand for health tourism with using varied advertising tools.

Big volume of patient’s medical data is one of the medical error reasons in coronary care unit (CCU). The purpose of this study was the designing a system that can monitored the patient’s vital sign continuously and when there are abnormal, producing alarms and proposed appropriate medical interventions according to the patient’s conditions in CCU.
This was application-development study that done in cross-sectional method in Shahid Rajai hospital at Tehran in 2015. 15 physicians and 15 nurses of CCU were considered as non-random purposively sampling. MEAN.js technology and MIMIC II Physionet’s database were used for system designing.
Normal and abnormal ranges of Vital signs were assessed according to the environmental and population conditions in this study. Variety of therapeutic interventions due to the patients’ vital signs changing was identified with their priorities. The results showed that the clinical decision support system (CDSS) had accuracy (94/68 %), sensitivity (82/60 %) and specificity (100 %) in proposing of proper interventions and had (92/92 %) accuracy, (80 %) sensitivity and (100 %) specificity in producing of timely alarms.
There are several factors that impact on determining of normal and abnormal ranges of vital signs and interventions priorities. The results showed that CDSS can help professionals in appropriate medical interventions selecting in unanticipated conditions at clinical care processes. At clinical point, this system can improve the understanding of vital signs, patient health conditions and decision-making process that can help in reducing of medical errors.

Health expenditure, one of the households spending, is affected by family’s socio-economic status, and government health policies, such as health insurance. Therefore, this paper aimed to analyze the impact of these conditions on different levels of health expenditures of households in Iran.
In this applied study, an econometric model, Ordered Probit, was used. The related data on household expenditure and income in 2014 were collected from Statistical Center of Iran by means of a questionnaire. The population consisted of 23573 Iranian households living in different areas in Iran. Estimation of results and Data analysis was performed using STATA 14.0.
According to the findings, householder education level, age, gender (male householders), per capita income, size of household, and health insurance coverage were positively related to household health expenditure. There was also a negative marginal effects for above variables in group one of dependent variable. It means that households with better socio-economic situation, have a low tendency for low health expenditures which again becomes positive for higher health expenditures. Moreover, households with rural insurance, social security insurance, complementary insurance and medical treatment insurance paid lower health expenditures, respectively.
Households with better socio-economic status, spend more on their health care. Since households pay differently for different health insurances, it is recommended that government provide a fair basic insurance package of the same quality of services to cover all households, a package which is not affected by economic inequality.

Inadequate dialysis for patient's kidneys as a mortality risk necessitates the presence of a pattern to assist staff in dialysate part to provide the proper services for dialysis patients and also the proper management of their treatment. Since the role of buffer type in the adequacy of dialysis is determinative, the present study is aimed at determining hemodialysis buffer type.
Cross-sectional method was applied in the present study. The population included the data extracted from Ali Ibn Abi Talib hospitals in Zahedan from May-June 2016. In this study Clementine 12.0 has been used for data analysis. In the present study Decision trees C5.0, Classification and Regression Tree, Chi-Squared Automatic Interaction Detector, Unbiased and Efficient Statistical Tree and Neural Network algorithms were executed.
The obtained accuracy for executing decision trees C5.0, Classification and Regression Tree, Chi-Squared Automatic Interaction Detector, Unbiased and Efficient Statistical Tree and Neural Network equals 0.9263, 0.9047, 0.8872, 0.8720 and 0.8754, respectively. The results of indices including sensitivity, specificity, accuracy, precision, NPV, FM, GM, FPR, FNR, FDR, ER for C5.0 decision tree are indicators of better performance of this algorithm compared to the other algorithms.
The extracted rules for a new sample having specific features can predict proper dialysis buffer. Hence, the proposed model helps us in predicting more precise hemodialysis buffer type and also the proper management of patient treatment which result in better performance among health organization.