JOURNAL OF HEALTH ADMINISTRATION
Year:2019 | Volume:21 | Issue:74 |Papers Count:9

Introduction: Hypotension during Hemodialysis often increases mortality in patients undergoing dialysis for a long time. Hypotension is the most frequent adverse event during hemodialysis; therefore, the present study was conducted to investigate hypotension value of patients and present a predictive model using descriptive data mining. Methods: In this cross-sectional study, conducted from May-June 2016, the data were extracted from Ali Ibn Abi Talib hospital in Zahedan and were then analyzed using Clementine 12. 0. The model was presented using K-Means, C5. 0 and CART algorithms. Results: According to the findings the parameters influencing hypotension were buffer type and blood flow the importance of which was verified through clustering and the extracted rules from the model. Discussion: The use of new modelling methods to analyze dialysis data and discover the existing relationships among them, changes the attitudes of dialysis personnel towards the process of dialysis and dialysis care. The evaluation of hypotension in hemodialysis patients helps a faster and more precise identification of hypotension. It would also facilitate proper and preventive management which enhances performance in dialysis centers. The study highlighted the importance of buffer type due to its effect on hypotension.

Introduction: Due to the complexity of health services, which require the engagement of different parties for the provision of integrated services, inappropriate network governance models can lead to financial conflicts, lack of transparency of accountabilities for medical errors, and difficulties on multi-professional team working. Therefore, this study aimed to examine factors influencing the design of an appropriate network governance model in the healthcare system. Methods: In this quantitative-correlational study, first factors influencing the domains of network governance were extracted from the literature. In the next step, a structural model was developed based on the hypotheses. To test the research hypotheses, structural equation modeling-partial least-squares method and Smart Pls2 software were used. The data were collected using a standard questionnaire which was distributed to two collaborative networks of diagnostic laboratories and one medical equipment supply network with 194 members. A total of 98 questionnires were collected. Results: Opportunistic behavioral, trust, commitment, information sharing, knowledge sharing were used as key factors influencing governance model within the structural model of this research. The results showed that commitment and information sharing had the most direct impact on the network governance. Moreover, opportunistic behavior had a negative and severe effect on trust in the network, which in turn affected the governance of the network. Conclusion: In order to design an appropriate network governance model in healthcare system, special attention should be paid to trust and commitment. These variables can also affect the governance of the network through improving information sharing. Furthermore, the governance model should be designed in such a way as to prevent the opportunistic behavior of the members.

Introduction: Reputable academic journals are known as the driving force behind the development of societies. In the meantime, scientific communication is considered as the most important and effective tool for the development of science and knowledge in the information society. The Journal of Health Administration is the second most popular journal in Iran, due to its impact factor in the field of medical sciences; therefore, the present study aimed to explore and illustrate the scientific collaboration networks of its researchers from a scientometric perspective. Methods: This study is a scientometrics-applied research. The corpus of the study included 165 articles published in the Journal of Health Administration from 2013 to 2017. USINET software was used to Visualize and analyze the Co-authorship networks, and VOS viewer software was utilized to visualize density Maps. Results: According to the findings, the group work was welcomed by the researchers and the scientific collaboration was high. It was shown that Rezapour, Sayedin, Bagheri Faradneboe, Abolghasem Gorji, and Nouri Motlagh had the highest density of co-authorship in the network. An analysis of the co-authorship network of collaborating universities showed Iran University of Medical Sciences, Tehran University of Medical Sciences, and Shahid Beheshti University as the centers of excellence. Conclusion: The researchers of this journal have a great tendency towards scientific collaboration and account for 94 percent of the journal articles.

Introduction: One of the most important events in the tourism industry of each country is the demand for a product or destination and its true prediction of tourism. It should be noted that there are distances and deviations between actual values and predictions. The use of modern scientific and forecasting methods will make the results far more than an objective estimate and closer to the truth; this article pursues the same goal in the field of medical tourism. Methods: In the first step, factors affecting the demand for domestic medical tourism in Tehran were identified by 31 experts using Fuzzy Delphi and Dematel Fuzzy methods. The factors were then processed by MATLAB2017a software. After determining the demand function, and collecting monthly data of each effective factor from 2001 to 2015, three regression prediction models, a fuzzy neural network, and SVR algorithm were implemented using MATLAB software to measure and compare forecast errors. Results: The demand function for domestic medical tourism included: economic factors (individual income and wealth), service prices and cost of living in the destination, the cost of accommodation facilities, air pollution, and the price of alternative products (foreign travel), the number of medical centers, hospitals and laboratories. Conclusion: The proposed hybrid approach for regression and SVR algorithm can make a better prediction compared with the other methods of forecasting domestic medical tourism. Therefore, it is recommended to use the demand function and forecasting model to lower the forecast error while planning for domestic medical tourism demand in Tehran.

Introduction: The Ministry of Health and Medical Education is one of the main providers of health information for patients and caregivers in Iran. The current study aimed to assess the quality and readability of online health information produced by the Ministry and its affiliated organizations. Methods: In this descriptive-survey study, the websites of the Ministry of Health and its affiliated medical universities (N=48) were assessed during April to July 2018. The corpus included 10780 records; however, only 7164 records produced for patient education were included in the study. Finally, a sample of 215 records were randomly selected and reviewed by two researchers. The quality and readability of the records were assessed by DISCERN questionnaire and Flash Dayani formula, respectively. Result: The mean scores for the quality (M=41. 14) and readability (M= 35. 27) of health information indicated a moderate quality and low readability. The highest mean scores on information quality (M= 49. 50) and health information readability (M= 54. 28) were obtained by Tabriz and Shahid Beheshti Universities of Medical Sciences, in that order. Conclusion: The quality of online health information produced by the Ministry of Health in Iran ranges from poor to moderate and it is written at difficult level; therefore, it is recommended to consider international standards for the content of patient education packages prior to developing any educational packages.

Introduction: Medication errors are considered as one of the most prevalent nursing errors the identification and disclosure of which are very important. The current study aimed to investigate the occurrence and reporting of medication errors in a teaching hospital in Isfahan. Methods: In this descriptive-analytical study, conducted in 2018, a questionnaire was used to collect the viewpoints of 220 nurses from 35 hospital wards about the frequency and types of medication errors. The questionnaire consisted of two sets of questions on demographic information and on types and reporting of medication errors. Data were analyzed using Chi-square and Mann-Whitney tests in SPSS 22. Results: The mean scores of medication errors and formal and informal reporting of errors were 66. 8%, 22%, and 55. 3%, respectively. The most prevalent errors were related to early or delayed doses (70%). The most formal reports belonged to the failure to observe proper drug time (8%) while the most informal report was associated with mixing two or more microcapsules of drug regardless of drug interactions (29%). There were significant relationships between medications errors and gender (P =0. 014), employment status (P =0. 031) and job experience. Conclusion: In order to promote the identification and reporting of medication errors, it is recommended to create blame free environments in hospitals. Moreover, educating nurses about various types of errors, encouraging them to report errors, and facilitating error reporting can all help planning and reduce errors.

Introduction: Osteoporosis is a common disease in women. Osteoporosis fractures may cause irreparable damages; therefore, early diagnosis and treatment before fractures is an important issue. The ojectiveof this study was to develop a decision support system for diagnosing osteoporosis using artificial neural networks. Method: This developmental study has been done in second half of 2017 based on crossectional method. In present study initially Osteoporosis affecting clinical factors were identified and the most significant clinical factors were selected through incorporation of a questionnaire-based survey. Subsequently, information of 256 female participants and their BMD test results, five years after initial data entry were used to train neural network. The information was obtained fromwomen who refered to department of Bone Mineral Densityof oushehr university of medical sciences. In order to identify the best network, trial and error method was used and neural networks with different topologies were trained using Scaled Conjugate Gradient and Levenberg-Marquardt algorithms. Confusion matrix was used to evaluate the network’ s accuracy, sensitivity and specificity. Results: In the first stage, out of 15 essential variables, 12 variables were selected as the most important risk factors. Multilayer perceptron neural network was designed. Results showed that the best structure of network was due to Scaled Conjugate Gradient algorithm with 10 neurons and Levenberg-Marquardt algorithm with 12 neurons in hidden layer. Accuracy comparison was showed that generally Levenberg-Marquardt algorithm had better result. The best sensitivity, specificity, and accuracy was 83. 1%, 89. 4%, and 86. 3% respectively. Conclusion: In this study developed a diagnostic tool based on local data that could be effective in tracking osteoporosis. Utilizing such a diagnostic tool as a timely referral of individuals and initiating therapy as soon as possible may prevent fractures from occurring and help avoiding the frequent complications of osteoporosis.

Introduction: Service quality is essential for service delivery. The Speech Therapy Clinic at the Rehabilitation Faculty of Iran University of Medical Sciences is one of the governmental centers providing rehabilitation services. This study aimed to study the service quality of speech therapy services using SERVQUAL model. Methods: In this descriptive-analytic stud, conducted in 2016 – 2017, a convenience sampling was used to recruit 59 patients referring to the Speech Therapy Clinic. Service quality was measured using a two-dimensional questionnaire to measure the perceptions and expectations of the patients and calculate the existing gaps between their perceptions and expectations. Data were analyzed using SPSS software. One-sample t-test was used to examine the significance of the gap, and Spearman and Chisquare tests were used to examine the relationship between quantitative and qualitative demographic variables with five dimensions of quality, respectively. Results: Regarding the validity and reliability of the questionnaire, the CVI was %0. 85 and Cronbach's alpha for the perceptions and expectations were %0. 95 and %0. 82, in that order. The results indicated a service gap in all five dimensions of quality. The largest and lowest gaps were related to empathy and responsiveness dimensions, respectively. There was no significant relationship between service gap and age, the frequency of visiting the clinic and gender. Conclusions: According to the findings, the Speech Therapy Clinic does not fully meet the expectations of the clients and there is a need to improve the quality of services; therefore, in order to achieve a higher level of service quality, the clinic officials are recommended to reduce the gap between their clients' perceptions and expectations.