Journal of

---

Background: Human Resources in the health sector not only is the most crucial source in health providing but account for nearly three-quarters of the health sector's costs. The purpose of this study was to estimate the required Human Resources of the CT scan department of the Reza Radiotherapy-Oncology Center based on the workload indicator of staffing needs in 2019.
Materials and Methods: This cross-sectional descriptive study used human resources determination based on staffing needs' workload indicators. The method of conducting expert meetings was used to determine the components of workload and standard time. Also, Interviews and rules, and personnel systems were used to determine the amount and factors related to available working time, and to determine the annual workload, the hospital management system and observation of activity logs were used. Excel and SPSS19 software were used to analyze the data for determining the required human resources and timing data, respectively.
Results: 7 factors related to staff annually available work time were identified. The available work time for CT scans staff was 1113 hours per year. In this study, imaging and simulation were determined as the main activities of the CT scans ward. The results of the workload indicator calculations showed that the CT scan section lacked 3 Personnel.
Conclusion: This study showed that Reza Radiotherapy Oncology Center is experiencing a shortage of professional CT scan staff, and the work pressure is (0.4). Therefore, CT scans are a top priority to provide the human resource.

---

Background and Objectives
In recent years, medicine supply chain management has become more significant, especially after the Covid-19 pandemic. The most important issue is supply chain cost control. If the drug inventory is not properly managed, it will lead to issues such as the lack of inventory of certain drugs, provision of excess inventory, increased costs, and, finally, patient dissatisfaction.
Materials and Methods
In this study, an attempt has been made to predict and manage the pharmaceutical needs of hospitals using an efficient deep-learning algorithm. The drug consumption data for ten years of Besat General Hospital in Hamedan are extracted from the HIS database. As a case study, the accuracy of the predictive model is evaluated, especially for cefazolin. We use a deep model to analyze the medical time-series data efficiently. This model consists of a Long Short-Term Memory network, which can sufficiently recognize the change history in time-series prediction applications. The proposed model with many adjustable parameters in the deep architecture will bring good performance to overcome the complexities of the learning problem.
Results
Using the deep learning method can increase robustness by reducing the effects of complexity and uncertainty in medical data. The average forecasting error for the proposed method is 0.043, and the measured values for RMSE, MAE, and R2  are 0.335, 0.260, and 0.851, respectively.
Conclusion
A comprehensive comparison between some other predictive methods and the implemented model shows the outperformance of the proposed approach. Additionally, the evaluation results indicate the efficiency of the proposed approach.