ABSTRACT
Objective: To systematically review the current literature of simulation in healthcare including the structured steps in the emergency healthcare sector by proposing a framework for simulation in the emergency department
Methods: For the purpose of collecting the data, PubMed and ACM databases were used between the years 2003 and 2013. The inclusion criteria were to select English-written articles available in full text with the closest objectives from among a total of 54 articles retrieved from the databases. Subsequently, 11 articles were selected for further analysis
Results: The studies focused on the reduction of waiting time and patient stay, optimization of resources allocation, creation of crisis and maximum demand scenarios, identification of overcrowding bottlenecks, investigation of the impact of other systems on the existing system, and improvement of the system operations and functions. Subsequently, 10 simulation steps were derived from the relevant studies after an expert's evaluation
Conclusion: The 10-steps approach proposed on the basis of the selected studies provides simulation and planning specialists with a structured method for both analyzing problems and choosing best-case scenarios. Moreover, following this framework systematically enables the development of design processes as well as software implementation of simulation problems
ABSTRACT
OBJECTIVES: Predicting the length of stay (LOS) of patients in a hospital is important in providing them with better services and higher satisfaction, as well as helping the hospital management plan and managing hospital resources as meticulously as possible. We propose applying data mining techniques to extract useful knowledge and draw an accurate model to predict the LOS of heart patients. METHODS: Data were collected from patients with coronary artery disease (CAD). The patient records of 4,948 patients who had suffered CAD were included in the analysis. The techniques used are classification with three algorithms, namely, decision tree, support vector machines (SVM), and artificial neural network (ANN). LOS is the target variable, and 36 input variables are used for prediction. A confusion matrix was obtained to calculate sensitivity, specificity, and accuracy. RESULTS: The overall accuracy of SVM was 96.4% in the training set. Most single patients (64.3%) had an LOS 10 days. Moreover, the study showed that comorbidity states, such as lung disorders and hemorrhage with drug consumption have an impact on long LOS. The presence of comorbidities, an ejection fraction <2, being a current smoker, and having social security type insurance in coronary artery patients led to longer LOS than other subjects. CONCLUSIONS: All three algorithms are able to predict LOS with various degrees of accuracy. The findings demonstrated that the SVM was the best fit. There was a significant tendency for LOS to be longer in patients with lung or respiratory disorders and high blood pressure.