Journal of Archives in Military Medicine
Volume:11 Issue: 4, Dec 2023

Estimating the total cost of services is crucial for utilizing available resources more efficiently and effectively. By employing the time-driven activity-based costing (TDABC) method, not only can costs be calculated, but also the efficiency and unused capacity of resources can be assessed.

This study aimed to calculate the cost of education for each medical student at one of the AJA Medical sciences Universities in 2022 using the TDABC method.

This quantitative study was conducted cross-sectionally and retrospectively in a descriptive-analytical manner. The data were collected and analyzed through interviews and observations. To analyze and allocate costs, administrative and educational departments were categorized into three levels: Overhead, intermediate, and final activity centers. Then, using the TDABC approach in Microsoft Excel 2013 software costs from the overhead and intermediate activity centers to the final activity center and students.

Thestudy estimated the average cost of educating each medical student tobeapproximately IRR6267 568 065. Additionally, the unused capacity of resources in overhead and intermediate centers was observed to be 30% (range: 4 - 44%) and 33% (range: 26 - 43%), respectively.

The findings indicated that the cost per student is somewhat reasonable; however, there is a high level of unused capacity in the university and faculty activity centers. The effective management of human resources and equipment is necessary to enhance service delivery processes, increase productivity, and reduce unused capacity.

Chronic kidney disease (CKD) poses a significant health burden worldwide, affecting approximately 10 - 15% of the global population. As one of the leading non-communicable diseases, CKD is a major cause of morbidity and mortality. Early identification of CKD is crucial for reducing its adverse effects on patient health. Prompt detection can significantly lessen the harmful consequences and enhance health outcomes for individuals with CKD.

This study aimed to evaluate and compare the effectiveness of various machine learning models in predicting the occurrence of CKD.

The study involved the collection of data from a sample of 400 patients. We applied the well-established cross-industry standard process (CRISP) methodology for data mining to analyze the data. As part of this process, we efficiently handled missing data using the mode approach and addressed outliers through the interquartile range (IQR) method. We utilized sophisticated techniques, such as CatBoost (CB), random forest (RF), and artificial neural network (ANN) models to predict outcomes. For evaluation, we used the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC).

An analysis of 400 patient records in this study identified that variables like serum creatinine, packed cell volume, specific gravity, and hemoglobin were most influential in predicting CKD. The results indicated that the CB and RF models surpassed the ANN in predicting the disease. Ten critical predictors were pinpointed for accurate disease prediction.

The ensemble models in this study not only showcased remarkable speed but also demonstrated superior accuracy. These findings suggest the potential of ensemble models as an effective tool for enhancing predictive performance in similar studies.

Patient’s shared decision-making (SDM) is an ethical standard for respecting patient autonomy.

This study aimed to investigate the level of SDMfor emergency surgery and its related factors in hospitals affiliated with the Zanjan University of Medical Sciences, Iran.

This cross-sectional study was performed on 306 patients candidates for emergency surgery in 2020. The research instruments included a 9-item SDM Questionnaire and an SDM-related factors questionnaire for surgery.

Our results showed that more than 50% of patients did not participate in choosing emergency surgery. Among the related factors, the SDM level of the family members, the patient’s marital status, and systolic blood pressure were the main predictors of the patient’s SDM for surgery (P < 0.05).

The emergency conditions of patients and the high workload of staff reduced participation in the decision-making of patients and their family members.

Assessing the interpersonal communication skills of nursing students during the COVID-19 pandemic enables us to understand their communication challenges and needs in crises and devise appropriate solutions to address them effectively.

This study aimed to determine the interpersonal communication skills of nursing students at Islamic Azad University, Karaj Branch, amidst the COVID-19 pandemic.

This descriptive cross-sectional study was conducted on 167 nursing students in the seventh and 8th semesters of the School of Nursing and Midwifery at Islamic Azad University, Karaj Branch, during the academic year 2020 - 2021. Sampling was performed using a purposeful sampling method. Data were collected through a demographic information form and the Interpersonal Communication Skills Test, which was completed via self-report. Data were analyzed using SPSS software version 26, employing descriptive statistics (mean, standard deviation, frequency, percentage) and inferential tests such as the Pearson correlation coefficient and t-test.

The majority (53.3%) of nursing students exhibited moderate interpersonal communication skills. The lowest mean score was related to assertiveness (13.72 ± 3.24), while the highest mean score was associated with the ability to receive and send messages (28.53 ± 4.62). A weak, significant inverse correlation was observed between the total score of interpersonal communication skills and age (r = -0.182, P = 0.019).

The results indicate that most nursing students during the COVID-19 pandemic possessed moderate interpersonal communication skills. The area of greatest weakness was assertiveness. These findings underscore the necessity of attention and training to enhance assertiveness skills among nursing students. Additionally, teaching nursing students interpersonal communication skills, particularly in critical conditions, is essential.

Optimizing resource allocation in emergency departments (ED) is challenging due to limited resources and high costs.

The objective of this study was to utilize data mining algorithms and simulation modeling to predict the length of stay (LOS) of patients and compare scenarios for increasing bed productivity.

Data mining algorithms, including Random Forest (RF) regression and CatBoost (CB) regression models, were used to predict the LOS based on patient demographic information and vital signs. The process of admission to discharge in the ED was simulated, and different scenarios were compared to identify strategies for increasing bed productivity.

The combination of RF regression and CB regression models performed better than other methods in predicting the LOS of patients. Simulation modeling demonstrated that optimal resource allocation and increased bed productivity could be achieved using predicted LOS values.

This study demonstrates that a combined approach of data mining and simulation can effectively manage ED resources and reduce congestion. The findings highlight the potential of advanced analytical techniques for improving healthcare service delivery and patient outcomes.

This article reports the measures related to the creation and establishment of a military field hospital by police medical workers in the procession of Arba’in as part of preventive preparation and national support in the field of health and treatment. After the multi-faceted investigations by the health deputy of the police, the University of Ilam province, Iran, and the governorate, considered to install four inflatable tents for the establishment of treatment areas in a land of 2 800 square meters in a part of the Arba’in walking path between the Mehran city and the border terminal with Iraq. The parking lot for the vehicles carrying troops, medical equipment, and ambulances was in the hospital area. The 40-bed military field hospital or compliance plan included the command room, men’s and women’s departments with two operation room beds, intensive care units, and support units, such as a pharmacy, drug storage, and medical equipment. Healthcare services were provided to more than two thousand five hundred pilgrims over 20 days. Telemedicine was connected with hospitals around the clock.