Background: Previous studies mentioned four organizational structures for hospitals, which are budgetary, autonomous, corporate, and private. Nevertheless, healthcare decision-makers are still required to select the most organizational structure specific to their circumstances. The present study aims to provide a framework to prioritize and select the most suitable organizational structure using multicriteria decision-making (MCDM) methods in Iranian hospitals. Methods: First, a multicriteria decision-making model consisted of the respective criteria, and alternatives were developed. The pertinent criteria were identified through a systematic literature review. The coefficient weights of the identified criteria were then calculated using FUCOM-F. Finally, organizational structures were prioritized in accordance with the identified criteria using FMARCOS. Results: The findings reveal that income is the most significant criterion in selecting organizational structures for hospitals whereas the number of outpatient visits is the least important. Also, the private structure is the most appropriate, and budgetary style is the least suitable organizational structure for Iranian hospitals. Conclusion: Providing a framework in order to select the most appropriate organizational structure could help managers and policymakers of the healthcare sector in Iran and other countries, mainly similar developing countries.

Introduction: The use of telemedicine for the prehospital management of emergency conditions, especially stroke, is increasing day by day. Few studies have investigated the applications of telemedicine in Emergency Medical Services (EMS). A comprehensive study of the applications of this technology in stroke patients in ambulances can help to build a better understanding. Therefore, this systematic review was conducted to investigate the use of telemedicine in ambulances for stroke patients in 2023.Methods: A systematic search was conducted in PubMed, Cochrane, Scopus, ProQuest, Science Direct, and Web of Science from 2013 through March 1, 2023. The authors selected the articles based on keywords and criteria and reviewed them in terms of title, abstract, and full text. Finally, the articles that were related to the study aim were evaluated.Results: The initial search resulted in the extraction of 2,795 articles. After review of the articles, and applying the inclusion and exclusion criteria, seven articles were selected for the final analysis. Three (42.85%) studies were on the feasibility and intervention types. Also, randomized trials, feasibility, feasibility and prospective-observational, and feasibility and retrospective-interventional studies were each one (14.28%). Six (85.71%) of the studies were conducted in the United States. The National Institutes of Health Stroke Scale (NIHSS) and RP-Xpress were the most commonly used tools for neurological evaluations and teleconsultations.Conclusion: Remote prehospital consultations, triage, and sending patient data before they go to the emergency department can be provided through telemedicine in ambulances. Neurological evaluations via telemedicine are reliable and accurate, and they are almost equal to in-person evaluations by a neurologist.

Objective: Quality of work life is one of the most important variables recently considered by many managers who seek to improve the quality of their human resources. Considering the vital factor of job satisfaction of family physicians as a service provider, this study was conducted to evaluate the quality of working life of family physicians in Bam. Materials and methods: This research is a cross-sectional and descriptive type. Research population includes all family physicians working at health centers in Bam. The Van Larr Quality of Work Life Questionnaire, which was validated by Nekoei Moghadam, was used. The questionnaire data is analyzed by SPSS 24 from statistical-descriptive tests (mean and standard deviation) and T-test and ANOVA. Results: Quality of work life score in family physicians is obtained 2.93 out of 5. Control ambiance in work (CAW) has the highest mean (3.16) and Work-life balance (WLB) has the lowest mean (2.29). Work experience has a meaningful relationship just with the two components of WLB and SAW in Quality of work life (p = 0.036). Furthermore, in the factor of GWB, the average score of unmarried physicians (Mean = 3.18) were more than the married ones (Mean = 3.05) (p = 0.010). The mean score of unmarried physicians was more than the married ones. Conclusion: The quality of work life from the family physicians is about upper-intermediate. Thus, authorities of the family physician concept in the ministry of Health, and also in the Bam city should pay more attention to the family physician’s work life and consider programs and solutions in order to improve it.

Background: The emergence of big data holds the promise of aiding healthcare providers by identifying patterns and converting vast quantities of data into actionable insights facilitating the provision of precision medicine and decision-making. Objective: This study aimed to investigate the factors influencing use of big data within healthcare services to facilitate their use. Method: A systematic review was conducted in February 2024, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Database searches for articles published between 01 January 2020 and 18 February 2024 and included PubMed, Scopus, ProQuest and Cochrane Library. The Authority, Accuracy, Coverage, Objectivity, Date, Significance ( AACODS) checklist was used to evaluate the quality of the included articles. Subsequently, a thematic analysis was conducted on the findings of the review, using the Boyatzis approach. Results: A final selection of 46 studies were included in this systematic review. A significant proportion of these studies demonstrated acceptable quality, and the level of bias was deemed satisfactory. Thematic analysis identified seven major themes that influenced the use of big data in healthcare services. These themes were grouped into four primary categories: performance expectancy, effort expectancy, social influence, and facilitating conditions. Factors associated with “effort expectancy” were the most highly cited in the included studies (67%), while those related to “social influence” received the fewest citations (15%). Conclusion: This study underscored the critical role of “effort expectancy” factors, particularly those under the theme of “data complexity and management,” in the process of using big data in healthcare services. Implications: Results of this study provide groundwork for future research to explore facilitators and barriers to using big data in health care, particularly in relation to data complexity and the efficient and effective management of big data, with significant implications for healthcare administrators and policymakers.