International Journal of Hospital Research
Volume:7 Issue: 1, Winter 2018

The kidneys of chronic kidney disease (CKD) patients do not have enough function and hemodialysis (HD) is a common procedure for their treatment. HD requires vascular access surgery (VAS) and arteriovenous fistula (AVF) is a low-complication method in VAS. However, different rates of AVF failure have been reported worldwide which can cause repeating surgeries and patient hospitalization. The goal of this study was to provide a system with the ability to predict VAS outcomes to reduce failures of surgeries.   The data of created AVF for 195 CKD patients – consisting 131 males (67.18%) and 64 females (32.82%), and aged from 15 to 87 years - were studied. Our provided system is based on “Fuzzy Inference System” (FIS) and learns rules by extracted results of decision tree algorithm. The number of diabetic patients was 73 and 117 persons had hypertension. Their hemoglobin range was 4.9 to 16. Their systolic blood pressure (BP) and diastolic BP were in the ranges [95-230] and [60-120], respectively. Using provided fuzzy control system, these results were investigated: (i) When the systolic BP increases, the AVF maturation improves (ii) In the young patients, the rate of AVF failure is higher than older patients; (iii) Growing patient from “Young” to “Middle-aged” causes switching from “AVF failure” status to “late Maturation”; (iv) In aged patients, high systolic BP with low diastolic BP, shifts from “late” AVF maturation to better statuses namely “good” and “excellent”. Using FIS can forecast surgery outcomes and thus reduce risk factors of patients. In the present developed fuzzy system, surgeons can configure the risk ranges of patient’s parameters before vascular surgery and configure changeable factors based on estimating postoperative outcomes.

Cancer patients treated with chemotherapy and other immunosuppressive drugs are always prone to various infections including opportunistic parasites. Since detection of infections in immunocompromised patients are frequently imperfect and the usual symptoms such as pyrexia are missing or hidden due to leukopenia, the importance of detection of opportunistic parasitic infections is well justified. Therefore, we aimed in this study to investigating the prevalence of enteric opportunistic parasitic infections among cancer patients of a Selected Teaching Hospital Affiliated to Iran University of Medical Sciences. This descriptive-analytical cross-sectional study was carried out on 150 cancer patients admitted to the oncology ward of a selected teaching hospital affiliated to Iran University of Medical Sciences in Iran from July 2016 to December 2017. Patients for this study were chosen by simple random selection method. Fecal samples from these patients were gathered and intestinal parasites were identified using direct wet mount, formalin-ether, chromotrope 2R staining and acid-fast staining methods. The obtained data from patients were analyzed using ANOVA, t-test and chi-square test. All statistical analyses were carried out through SPSS version 17.0. Among 150 samples investigated with direct wet mount method, 23 were reported positive for parasites with the most frequent parasite being Blastocystis (14%). Investigation of slides stained by hot acid-fast method revealed no cases contaminated by Cryptosporidium spp. or Isospora belli, yet in fecal samples stained with chromotrope 2R method 9 Microsporidia sp. infection cases were reported. It was believed that due to immunosuppressive effect of chemotherapeutic agents, the treated patients are more prone to opportunistic infections. Contrary to this belief our study showed lower prevalence of infections in these patients which could be related to more prophylactic drug use that are antibacterial as well as antiparasitic.

In recent decades medical errors have become a major issue for scientific investigation to avoid potential harmful failures threatening patients’ health and safety. Developing risk management culture has been considered not only to play an important role in detecting and coping effectively with such errors but also lead to high level of organizational performance. This study aimed to examine the impact of risk management culture on the performance of training hospitals affiliated by Yazd University of Medical Sciences (YUMS). 
This descriptive analytical study was conducted in three training hospitals affiliated by YUMS. Research sample consisted of 150 nurses working in the hospitals who’ve been selected by proportional randomized sampling method. Data were collected using a standard questionnaire developed by Dyck et al. Collected data were entered in SPSS version 20 and analyzed through descriptive analysis methods (Mean, Standard deviation), and Pearson correlation coefficient. 
the highest mean score related to error management and performance belonged to hospital A (3.84+0.32, 3.49+0.49). In both hospitals A and B, a significant statistical relationship between error management culture and organizational performance was approved. 
Study findings suggested that improvement in error management culture would lead to higher level of performance. In fact supportive culture in error management could be translated to high organizational performance through decreasing negative error consequences.

In the increasingly competitive market of the healthcare industry, the organizations providing health care services are highly in need of systems that will enable them to meet their clients' needs in order to achieve a high degree of patient satisfaction. To this end, health managers need to identify the factors affecting patient satisfaction focus. The purpose of this study is to provide a model based on recommender systems in order to increase patient satisfaction with the quality of hospital services, in which patients were clustered based on personal information and then dimensions of services were weighted to determine the most important dimensions.
Information technology can provide the possibility of moving towards better services by analyzing customer preferences and tailoring the content and process of service provision according to customer needs. On the other hand, the personalization of products and services is one of the most important factors affecting customer satisfaction.
In order to conduct the model, the data related to satisfaction forms of 556 discharged patients from Shariati Hospital in Tehran was used. By estimating the accuracy of the predictions of the model based on the mean absolute error criterion and the mean squared error, the values were respectively obtained as 40% and 49%.
In this study, through weighting the characteristic for different groups of patients, the more important services were identified where considering the number of 148 test data, it was determined that the model of the most important dimensions of the service for each cluster are correctly determined. Therefore, the hospital can decrease dissatisfaction of the new patients in each group through reinforcing the important services in each group, after discharge.

The Health System Evolution Plan was developed to improve quality and accessibility of health care services and reduce the costs to protect people from catastrophic out of pocket payments. The aim of current study is investigatation of Health System Evolution Plan effects on performance indices of governmental hospitals affiliated to Iran University of Medical Sciences, in Iran. This was a descriptive analytic study with retrospective approach based on extracted data from 16 hospitals of Iran University of Medical Sciences in Iran. Specific indices comprising income and expenses, paraclinical evaluations, bed performance indices and reform instructional indices were collected in 5 categories before (April , 2013) and after(May ,2014 to March, 2016 ) implementation of Health System Evolution Plan. Data were analyzed with SPSS software version 22, using paired t-test and Pearson’s correlation coefficient. After implementation of Health System Evolution Plan, indices of bed turnover rate, bed occupancy percentage, average active bed, number of emergency patients, the average length of stay of the patient, percentage of normal delivery, cash income, cost of consumables and equipment, percentage of armed forces insurance deductibles and percentage of social security insurance deductibles increased.    The Health System Evolution Plan imposed high economic burden on insurance companies due to increased  tariffs with no plan to control them; however, it has improved utilization and accessibility of services. It is necessary to supply consistant financial resources and apply effective supervising on continous performance of project to meet the objectives.

The selection of the sustainable supplier is important for any industry. Medical centers are not an exception in this case, and selecting the best sustainable supplier is a major step towards increasing their productivity. This paper, using the Data Envelopment Analysis and then using Multi-Attributed Utility Theory as a backup approach to fix errors, attempts to introduce the most important criteria and sub criteria for selecting the best sustainable supplier of medical equipment among domestic and foreign suppliers. After reviewing the previous papers, the 13 most important sub-criteria are extracted based on the 3 social, environmental and economic criteria. At first a Data Envelopment Analysis (DEA) model is used to find the initial ranking for sustainable suppliers. Then the Multi-Attributed Utility Theory (MAUT) is employed as a secondary and backup approach to find the utility function. The data obtained are first examined in DEA and then by MAUT and the results are presented in the form of figures, tables and analytical results in the relevant section. Based on the 13 sub-criteria introduced at the end of the two-stage ranking, supplier C is selected as the best sustainable supplier. For initial ranking, DEA is a good method, but to find the utility function of the ranking, the use of MAUT is an effective method with a high degree of proximity.

Background and objectives - A hybrid MCDM approach is presented to evaluate and prioritize the disruptions in the angiography process, in a fuzzy environment. The proposed approach is applied to a real case in a public hospital.Methods – In this study, a new approach is utilized based on fuzzy MCDM methods. The disruptions are identified using the experts' opinions. Then, the FMEA risk factors are compared in pairs and given weights by experts, using fuzzy AHP. The Experts were then asked to rate the disruptions according to the risk factors. Finally, the disruptions were ranked using Fuzzy VIKOR.Results and Conclusion - Results show that the risk factor occurrence has the most importance among the three risk factors. They also suggest that the top three disruptions in the angiography process are ‘absence of manual’ and ‘guideline on angiography procedure’, ‘inadequate training of personnel and exhaustion’, respectively.Practical implications - Results of this study may be may help hospital managers and practitioners avoid disruptions in the process and the improve healthcare service quality.Originality/value - The recent studies in the related literature were thoroughly investigated and it was found that no studies considered the disruptions identification and analysis in the angiography process. Therefore, the disruptions in the angiography process are investigated for the first time. Moreover, the efficiency and applicability of the proposed method and the rankings are validated by the experts.

Operating rooms (ORs) are precious resources in hospitals, as they constitute more than 40% of the hospital revenues.As such, surgical cancellations are very costly to hospitals. Same-day surgery cancellations or no-shows were found to be the main contributing factor to underutilization of operating rooms (ORs) in a public-sector hospital despite the existence of long surgical wait lists. To demonstrate the feasibility of overbooking surgical procedures, a Monte-Carlo simulation model to predict unused OR time was built and validated using six months of historical data. We first fitted statistical distributions to the random parameters, using Easyfit©software. Then, a surgical case-mix optimization problem was formulated to prescribe the surgeries for overbooking that maximize the profits over the predicted unused OR time.   The optimization model was solved both deterministically based on average historical surgery durations using Lingo Software Package and heuristically by taking into account the random nature of surgical durations using Risk Solver Platform© add-on to Microsoft Excel. We conducted simulation-optimization of the stochastic model for the three selected date. Results show significant improvements over the base case of no overbooking. The increases in the average surgical profits due to overbooking on three randomly selected dates were 89%, 36%, 93%. To the best of our knowledge, this study is the first applied demonstration of descriptive, predictive, and prescriptive analytics to improve surgical operations through overbooking. The analysis shows significant opportunities in generating surgical profits (36%-93% on randomly selected validation dates) and reducing average wait times (by 4.16 weeks) without risking OR overtime.