M. Ghiyasi, G. Ghesmati Tabrizi, S. Naderi,
Volume 18, Issue 1 (3-2021)
Abstract
Hospitals are the most important resource consuming units in the health sector, so paying attention to these parts is very important. The purpose of this study was to investigate and evaluate using resources in Department of Imam Reza Hospital of Mashhad using the concept of efficiency to how using resources to improve their activities. This study is an applied research that was conducted in Imam Reza hospital of Mashhad in a cross-sectional study during 1395. Data was collected through the Hospital Information System (HIS). At first, we identified hospital input and output indicators. In the next step, we measured the efficiency and analyzed resource allocation. The models used were the linear programming type and Lingo version 14 was used to solve them. The number of physicians, the number of nurses and the number of active beds, as input factors and bed occupancy, the number of hospitalized patients were considered as the output indicators. Assignment analysis results showed that there is a possibility of reallocating resources in order to increase efficiency. Reallocation of the resources on average can increase the efficiency of 36 percent for different sectors. Therefore, the optimal allocation of resources is one of the key factors for improving the performance of hospital departments. Considering the direct impact on the quality of service should be seriously considered by health managers.
A. Nourbakhsh, M. Mirmohammadian, M. H. Mehdizadeh Naderi,
Volume 22, Issue 1 (3-2025)
Abstract
In recent decades, due to the continuous increase in the cost of attracting new customers, it is very important and sensitive for the profitability of organizations to pay attention to maintaining customers and increasing their loyalty. Therefore, organizations implement various programs to increase the durability of their valuable customers (customers with less resource loss and high profitability). The current research, considering the capabilities of data mining in management and design, implements a model for predicting the behavior of customers in the field of industry, using the CRISP-DM standard methodology based on the RFM model and Random Forest and Growing Trees techniques. Increasingly, it has searched the database of customers of an automobile company, who have had more than one product purchase contract with that automobile company. By applying a model based on Random Forest, Growing Trees and a hybrid prediction model technique, customers who tend to turn away are identified and effective marketing strategies are planned for this group. The analysis of customer behavior shows that the length of active customer relationship, the frequency of relative purchases and the average time interval between purchases are among the best predictors. Also, the hybrid prediction technique has shown a better response than random forest and growing trees techniques.
