Search

Objective: The length of stay is an important indicator of hospital performance and efficiency. Regarding the importance of the length of stay, this study aimed to design a structural model of the inpatients' length of stay in the educational and therapeutic health care facilities of Iran in order to identify the influencing dimensions., Methods: The present study was an analytical and applied study. The face validity of the data gathering tool was investigated by the expert judgment and the construct validity was examined by using the exploratory factor analysis. In order to verify the reliability of the tool, the internal consistency was also trialed by using the Cronbach's alpha. For ranking the influencing dimensions and factors and also in order to examine the causal relationships between the variables in a coherent manner and presenting the final model, the structural equation modeling technique was used in AMOS software at a significant level of 0.05., Results: The mentioned structural model consists of 4 dimensions and 29 factors influencing the length of stay of hospitalized patients. The independent variables are based on priority and importance as follows: patients' conditions, the underlying factors, the clinical staff performance, and hospitals' service delivery, which were examined by second-order factor analysis in order to study the relationship between them and the inpatients' length of stay., Conclusion: Considering the importance of each one of the proposed dimensions from the point of view of service providers in some therapeutic centers of the country by paying attention to the role of each one of them in preventing prolonged hospitalization can be essential in the effectiveness of the treatment and cost reduction., .

Objective: Gastric cancer is one of the most common types of cancers, which will result in irreparable harm in the case of misdiagnosis or late diagnosis. The purpose of this study is to investigate the capability of data mining techniques and disease risk factor characteristics to predict and diagnose the gastric cancer. Methods: In this retrospective descriptive-analytic study, we selected 405 samples from two groups of patient and healthy participants. A total of 11 characteristics and risk factors were examined. we used four Machine learning methods, Include support vector machine (SVM), decision tree (DT), naive Bayesian model, and k nearest neighborhood (KNN) to classify the patients with gastric cancer. The evaluation criteria to investigate the model on the database of patients with gastric cancer included Recall, Precision, F-score, and Accuracy. Data was analyzed using MATLAB® software, version 3.2 (Mathworks Inc., Natick, MA, USA). Results: Based on the results achieved from the evaluation of four methods, the accuracy rates of SVM, DT, naive Bayesian model, and KNN algorithms were 90.08, 87.89, 87.60, and 87.60 percent, respectively. The findings showed that the highest level of F-Score was related to the SVM (91.99); whereas, the lowest rate was associated with the KNN algorithm (87.17). Conclusion: According to the findings, the SVM algorithm showed the best results in classification of Test samples. So, this intelligent system can be used as a physician assistant in medical education hospitals, where the diagnosis processes are performed by medical students.

Background: Considering the increasing incidence of different cancers, use of modern technologies such as TTCC can make a dramatic change in treatment of these diseases. So, if the evolution strategy conflicts with the organizational culture, national culture, and organizational structure of experts in this field, resistance will rise. Objective: The purpose of this study was to determine the effective factors on the use of TTCC in hospitals affiliated to Tehran University of Medical Sciences using the AHP model. Materials and Methods: This current descriptive study was carried out in 2018 on specialist physicians from five hospitals of Tehran University of Medical Sciences. The data gathering tool was a questionnaire consisting of two sections; demographic items and 27 effective factors influencing the implementation of TTCC technology, which was evaluated for validity and reliability. Data analysis was performed using analytical hierarchy process with expert choice and for empirical illustration are used to discuss the use of covariance-based SEM versus smart PLS software. Results: Based on AHP comparisons in the studied factors, the highest priority was the Cultural factor. In other words, commitment of senior executives to support the provision of a new technology with a weight of 0.327. The lowest priority, however, was related to support diverse approaches, innovation, creativeness, and acceptance of new ideas with a weight of 0.038. In regard with technical and organizational factors, out-of-hospital access to the intranet network and support provided by the doctors, with the weight of 0.221 and 0.205 in order, acquired the highest rates. Conclusion: Considering the high prevalence of cancer in Iran and the necessity of using new technologies in its treatment and by addressing the specialists’ views and opinions in this field, organizational and national culture in the application of TTCC technology should be promoted. This target can be hit through acceptance, change in attitude, and successful use of TTCC technology by medical professionals., (Creative Commons Attribution License)

Background: Patient safety culture plays an important role in healthcare systems, especially in chemotherapy and oncology departments (CODs), and its assessment can help to improve quality of services and hospital care. Objective: This study aimed to evaluate and compare items and dimensions of patient safety culture in the CODs of selected teaching hospitals of Iran and Tehran University of Medical Sciences. Materials and Methods: This descriptive-analytical crosssectional survey was conducted during a six-month period on 270 people from chemotherapy and oncology departments selected through a cluster sampling method. All participants answered the standard questionnaire for “Hospital Survey of Patient Safety Culture” (HSOPSC). Statistical analyses were performed using SPSS/18 software. Results: The average score for patient safety culture was three for the majority of the studied CODs. Statistically significant differences were observed for supervisor actions, teamwork within various units, feedback and communications about errors, and the level of hospital management support. (p<0.05). Relationships between studied hospitals and patient safety culture were not statistically significant (p>0.05). Conclusion: Our results showed that the overall status of patient safety culture is not good in the studied CODs. In particular, teamwork across different units and organizational learning with continuous improvement were the only two properly operating items among 12 dimensions of patient safety culture. Therefore, systematic interventions are strongly required to promote communication., (Creative Commons Attribution License)