In recent years, a new manner has been common practice in emergency DEPARTMENTs (EDs) of governmental hospitals in Iran, in which, of course illegally, patients are encouraged to self-discharge or refer to other EDs. This is done by giving incomplete or even false information and deceiving patients. The purpose of this study is to identify the underlying causes from the point of view of EDs' personnel to clarify the root of this burden for authorities and pave the way for more studies. This is a cross-sectional descriptive-analytic study, gathering opinions via questionnaire and/or direct interview with open-ended questions. During November and December 2009, hospital managers, hospital heads, faculty members, residents in specialties involved with the ED, interns, ED nurses, and nurse supervisors were interviewed regarding the frequency of such instances in their hospital, the main violators, and its possible causes and determinants. In this study 114 persons from different groups took part. From participants' point of view, the main violators in EDs are junior residents and interns 48 (42.1%) participants pointed to junior residents and 29 (25.4%) participants found interns responsible. The estimated number of patients which were refused during a week was stated as 18.1 ± 15.6 (range between 0 to 90). Among participants remarks, the most important factors which lead staff to non-ADMISSION were high work load, overcrowding of EDs, lack of facilities, lack of any motivation or incentive for doing tough tasks, inappropriate patient transfer by city emergency system, bad policies and rules, lack of sufficient supervision from deputies, and improper work sharing between different levels. Patient non-ADMISSION and diversion through deception is a common practice among EDs of educational hospitals in Tehran. High work load and overcrowding of these centers should be considered a high priority target for interventions. Further studies are highly suggested to find appropriate solutions. In future studies, junior residents and interns should be considered as main target groups.

Introduction: Today, hospitals face challenges such as overcrowding in the emergency ward and increased chaos and disruption in staff work, which leads to increased patient dissatisfaction. With the advancement of artificial intelligence and the expansion of data mining, predicting patient ADMISSION has become important. This study endeavors to predict patient ADMISSION in the emergency DEPARTMENT of Imam Ali Hospital in Shahrekord.Methods: In this study, 2180 patient records from the emergency ward of the hospital were examined. Initial patient information, including personal details, vital signs, and triage level which were recorded by nurses, were extracted. Using pairwise comparison matrix, the effective features were selected by experts. Then, using naive Bayes, decision tree, random forest, and support vector machine algorithms, the data were classified.Results: Out of the 15 collected features, 9 features were selected by experts, and the results revealed that the random forest algorithm had the good performance in predicting patient ADMISSION in this case study, with an accuracy of 92/2%.Conclusion: The results indicated that the accuracy of machine learning models increases with the use of expert opinions, and the random forest algorithm can predict patients' ADMISSION with high accuracy in this case study.v

Introduction: Renal transplantation are admitted to emergency DEPARTMENT (ED) more than normal population.The present brief report aimed to determine the reasons of renal transplant patient’s ED visits.Methods: This retrospective case series study analyzed the reasons of renal transplant recipients ADMISSION to one ED between 2011 and 2014. The patient data were collected via a checklist and presented using descriptive statistics tools.Results: 41 patients with the mean age of 40.63 § 10.95 years were studied (60.9% male). The most common ED presenting complaints were fever (36.6%) and abdominal pain (26.8%). Infections were the most common final diagnosis (68.3%). Among non-infectious causes, the most common was acute renal failure (9.7%).73.2% of the patients were hospitalized and no cases of graft loss and mortality were seen.Conclusion: The most common reason for ED ADMISSION was fever, and infections were the most common diagnosis. Acute gastroenteritis being the most frequent infection and among non-infectious problems, acute renal failure was the most frequent one.

Sepsis results in a life-threatening organ dysfunction due to the dysregulation of organ dysfunction detection, risk stratification, prognosis, and treatment is crucial for sepsis and septic shock. Several widely-available biomarkers are including white blood cells count (WBC), neutrophil-lymphocyte ratio (NLR) C-reactive protein (CRP), and procalcitonin (PCT), lactate levels. Presepsin (P-SEP) and mid-regional pro-adrenomedullin (MR-ProADM), are other two important biomarkers currently under investigation. In addition, clinical scoring system such as the Sequential Organ Failure Assessment (SOFA) and the Acute Physiology and Chronic Health Evaluation (APACHE)-II scores are combined. Furthermore, comorbidities and the underlying infections should be considered. In this study, we evaluated the potency of septic shock predictor in sepsis patients at Dr. Kariadi Hospital Semarang, Indonesia between June and August 2019. A total number of 59 patients, consisted of 19 sepsis and 40 septic shock who fulfilled the Sepsis-3 criteria were enrolled. Biomarkers, scoring system and other variables were evaluated within 24 hours of emergency DEPARTMENT (ED ADMISSION. Bivariate and multivariate logistic regression analyses as well as receiver operating characteristic curve analysis were used to predict the development of septic shock. A combination of five biomarkers (WBC count, PCT, P-SEP, MR-ProADM, and lactate) plus SOFA score and additional risk variables (skin and soft tissue infection, and hypertension) performed better for predicting septic shock than any single factor.