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 < or =5 days, whereas 41.2% of married patients 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.
Comorbidity ; Coronary Artery Disease ; Coronary Vessels ; Data Mining ; Decision Trees ; Heart ; Hemorrhage ; Humans ; Hypertension ; Insurance ; Length of Stay ; Lung ; Sensitivity and Specificity ; Social Security ; Support Vector Machine

OBJECTIVES: Healthcare institutions need timely patient information from various sources at the point-of-care. Evidence-based medicine (EBM) is a tool for proper and efficient incorporation of the results of research in decision-making. Characteristics of medical treatment processes and practical experience concerning the effect of EBM in the clinical process are surveyed. METHODS: A cross sectional survey conducted in Tehran hospitals in February-March 2012 among 51 clinical residents. The respondents were asked to apply EBM in clinical decision-making to answer questions about the effect of EBM in the clinical process. A valid and reliable questionnaire was used in this study. RESULTS: EBM provides a framework for problem solving and improvement of processes. Most residents (76%) agreed that EBM could improve clinical decision making. Eighty one percent of the respondents believed that EBM resulted in quick updating of knowledge. They believed that EBM was more useful for diagnosis than for treatment. There was a significant association between out-patients and in-patients in using electronic EBM resources. CONCLUSIONS: Research findings were useful in clinical practice and decision making. The computerized guidelines are important tools for improving clinical process quality. When learning how to use IT, methods of search and evaluation of evidence for diagnosis, treatment and medical education are necessary. Purposeful use of IT in clinical processes reduces workload and improves decision-making.
Cross-Sectional Studies ; Decision Making ; Delivery of Health Care ; Education, Medical ; Electronics ; Electrons ; Evidence-Based Medicine ; Humans ; Learning ; Medical Informatics ; Outpatients ; Problem Solving ; Surveys and Questionnaires