Javad Zarei; Amir Abbas Azizi; Khosro Keshavarz; Elham Seyavashi
Abstract
Introduction: Every year, the considerable amounts of hospital income are deducted by the insurance organizations as deductions. The aim of this study was to survey the deductions applied by medical services and social security insurances organizations on bills of teaching hospitals affiliated to Ahvaz ...
Read More
Introduction: Every year, the considerable amounts of hospital income are deducted by the insurance organizations as deductions. The aim of this study was to survey the deductions applied by medical services and social security insurances organizations on bills of teaching hospitals affiliated to Ahvaz Jundishapur University of Medical Sciences. Methods: This is cross-sectional study which was conducted in 2009-2010. All of the bills sent by five teaching hospitals affiliated to Ahvaz Jundishapur University of Medical Sciences, to medical services and social security insurance organizations in years 2008-2009 were studied. Instrument for data gathering was checklists designed according to the bills sent to insurance organization. The Checklist validity was confirmed by expert's opinions. Data analyzed by using descriptive statistics in SPSS software. Results: The total deduction for outpatient and inpatient bills applied by Medical Services Insurance organization was 6.62 %. The total deduction for outpatient and inpatient bills applied by Social Security Insurance was 4.9%. In comparison, the deductions of inpatient hospital bills were more than outpatient hospital bills (6.99% to 3.36%). Conclusion: Results showed that although small percentage of accounts involves deductions but the low amount of this deduction had significant financial imposes to hospitals. Thus, planning to reduce deductions is recommended by the Universities of Medical Sciences. Keywords: Insurance; Social Security; Health Services; Hospitals
Ehsan Nabovati; Amir Abas Azizi; Ebrahim Abbasi; Hassan Vakili-Arki; Javad Zarei; Amir Reza Razavi
Volume 10, Issue 6 , December 2012, , Pages 789-799
Abstract
Introduction: In the past decades, machine learning algorithms have become a useful tool for data mining within huge amounts of health data to create prediction models. Burn is one of the diseases that predicting of its outcome has high importance. The aim of this study was to survey two widely used ...
Read More
Introduction: In the past decades, machine learning algorithms have become a useful tool for data mining within huge amounts of health data to create prediction models. Burn is one of the diseases that predicting of its outcome has high importance. The aim of this study was to survey two widely used machine learning algorithms; neural network and decision tree, and compare them with logistic regression method to predict the outcome of burn patients. Methods: In this retrospective observational study, following preprocessing of the data and determining the outcome of patient (live or death), two well-known machine learning algorithms (neural network and decision tree) and logistic regression method were used to create prediction models using data from 4804 burn patients hospitalized in Taleghani Burn Center in Ahvaz during the years 2001-2007. The preprocessing of the data was performed using SPSS (Version16.0), and in the modeling phase, Clementine (Version 12.0) software was used. Moreover, 10-fold cross validation technique was used to validate the model and criteria for evaluating the performance of models were measured and compared. Results: The results showed that the neural network algorithm with accuracy of 97% resulted the most accurate model on the studied data. The decision tree model with 95% accuracy was in the second place and the logistic regression model with an accuracy of 90% was the least accurate. Moreover other evaluating criteria such as sensitivity, specificity, PPV, NPV and AUC showed that performance of the neural network model was better than the others. Conclusion: The current study shows that machine learning algorithms compared with statistical methods create more accurate models. In analyzing the current data, the model created by artificial neural network is more accurate than the other machine learning algorithm, decision tree. Keywords: Data Mining; Machine Learning; Forecasting; Decision Tree; Artificial Neural Network; Burns