JOURNAL OF HEALTH ADMINISTRATION
Year:2018 | Volume:21 | Issue:73 |Papers Count:9

Introduction: Hospital services are considered to be the principal factors contributing to increasing healthcare costs in many countries; therefore, considering productivity issues and identifying background factors affecting them is one of the top priorities of health care management. This study aimed to determine factors affecting the culture of productivity from the perspectives of health services providers. Methods: In this ethnography study, data were collected through interviews, observations and cultural artifacts in two major hospitals affiliated with Mashhad University of Medical Sciences. Semi-structured interviews were conducted with 35 key informants among clinical managers, and other health professionals. Data analysis was performed using Miles and Huberman methods. To determine the rigor of data, Guba and Lincoln’ s criteria were used. Results: Data was conceptualized in the two major domains of resources, i. e., management and healthcare quality. Conscientiousness in the workplace, knowledge and experience, facilities and equipment, motivations and management factors were the main factors affecting the areas under study. Conscientiousness in the workplace was turned out to be the most important factor affecting the culture in the study group and was influenced by knowledge and clinical experience and motivational factors in the workplace. Conclusions: Excessive work pressure, lack of motivation and knowledge regarding the maintenance of equipment, resource constraints and weak supervision were the factors affecting the individual’ s productivity which is mainly under the influence of conscientiousness and commitment in the workplace. Therefore, it is proposed to take measures to foster Conscientiousness and commitment of professional staff in the workplace, pay greater attention to motivational factors, modify the system of funding hospitals and organize training courses related to productivity and inventory management.

Introduction: Quality of life is one of the key concepts of sustainable development. Social capital, on the other hand is considered both as an important variable affecting the success of development programs as well as a social determinant of health. Therefore, this study aimed to explore the relationship between quality of life and social capital among the community health service staff in Malayer city. Methods: In this cross-sectional study, conducted in 2017, a sample of 186 participants were selected using stratified sampling method. A standard questionnaire including questions on demographic information (N=5), quality of life (N=36) and social capital (N=36) was used to collect data which were then analysed using one-way ANOVA, Pearson correlation coefficient and independent t-test in SPSS 20. Results: According to the findings, the mean scores of social capital and quality of life were 119/13 ± 3/34 and 68/4 ± 16/71, respectively. There was a positive significant relationship between social capital and the general health (r=0/22, p=0/003), mental health (r=0/24, p=0/001), social functioning (r=0/25, p=0/000), physical functioning (r=0/38, p=0/001), role limitations due to physical pain (r=0/25, p=0/001), role limitations due to mental problems (r=0/20, p=0/007) and vitality and happiness (r=0/23, p=0/001). There was a low but not significant positive relationship between physical pain and social capital (r=0/14, p=0/06) Conclusion: According of the findings the level of social capital can effectively improve the quality of life. It seems that enhancement of social partnerships and collaborative activities, development of social relationships, and reinforcement of a sense of security and trust among employees can improve the quality of life.

Introduction: Increasing the productivity of health care spending is important for all countries. In order to increase productivity, input and output data can have fuzzy values. This study aimed to present a new model of inadequate envelopment analysis to determine the productivity of provinces in the health sectors, highlight productive provinces and detect sensitive indexes to increase productivity. Methods: This descriptive-analytical study was conducted on the health system of Iran in 2016. Data were collected through library documents, Internet and the center of statistics and information technology management of medical universities. Analytical hierarchy process was used to determine health care indexes among which five indicators were extracted. Data were analyzed using inadequate envelopment analysis technique by General Algebraic Modeling System. Results: According to the proposed model, for and, only Semnan, Kurdistan and Mazandaran provinces were considered to be efficient based on their indexes on patient satisfaction and physician-to-population ratio, and were regarded as referral units for other provinces. The two indexes of physician-to-population ratio and nurse-to-population ratio were introduced as the most and the least sensitive indicators, respectively. Conclusions: Kurdistan, based on the most sensitive indicator, had the highest referral patients. Other provinces can increase their efficiency through improving the quality and quantity of their services, increasing patient satisfaction, continuous evaluation of performance and increasing the number of physicians working in medical universities.

Introduction: Congenital anomalies are the leading causes of infant death at birth and during infancy, and are considered as significant and costly health problems. Therefore, a national registry of congenital anomalies is needed to calculate health indexes because collecting and reporting birth anomalies facilitate assessment, and health planning. Consequently, this study aimed to determine the minimum number of birth defects at the national level for planning and controlling the anomalies. Methods: This applied and descriptive-comparative research was conducted in three phases in 2016-2017. That is, defining the registers of congenital anomalies in the countries of excellence, designing and validating a minimum data set using the Delphi technique. Results: After the revision of elements in selected registries, a minimum data set was developed including 35 elements which were then, based on the result of the Delphi technique, reduced to 25 elemetns in four subsets including infant records, maternal records, maternal socioeconomic status and history of the anomaly. Conclusion: A national minimum Data Set plays an important role in disease monitoring and registry. It facilitates data collection and transmission throughout the country and enhances the potential for using data and responding to critical public health issues. Therefore, it is recommended to incorporate the national minimum data set into the maternal and child systems (IMAN) to collect information about congenital anomalies more efficiently and comprehensively.

Introduction: The present study aimed to explore the effective resources in the emergency department and present an optimal combination of these resources using the Meta-Modeling optimization approach to reduce the waiting times for patients visiting emergency department. Methods: The research model was based on the Response Surface method and designed experiments. The dependent variable, the average waiting time for patients visiting emergency department, was made through the implementation of the created experiments of factor design in the simulated model. Patient data were collected from archived files from 3rd of April 2017 to 9th of September 2017. Results: The results indicated that Community health workers and beds were significant in the estimate function of average waiting time for patients. Although the effects of other major variables (emergency secretary, nurses and cardiologists) were not statistically significant, they were deliberately used in this model to determine the optimal combination of these variables created by solving the problem. Conclusion: Optimization of Patients waiting time in the emergency department was performed using simulation, Design of Experiment and Regression Meta-model. The reduction of waiting time and the expenditure in the emergency department caused by the optimal waiting time was, 49/6% and 51%, respectively.

Introduction: The current study aimed to investigate the effectiveness of Iranian scientific outputs on common cancers published in PubMed database during 2011-2015. Methods: In this applied, descriptive-analytical study, a scientometrics analysis was used. The study was conducted on all research papers which were written by Iranian scholars on stomach, colon, breast, prostate, and lung cancers and were indexed in PubMed database during 2011-2015. Data were extracted using Altmetrics Bookmarklet tool and analyzed by excel software. Results: According to the findings, the number of articles published by Iranian scholars on stomach, colon, breast, prostate, and lung cancers in 2011 was 25, 40, 88, 20 and 15, respectively. The number of articles on the same subjects in 2015 was 52, 93, 256, 44, and 32, in that order; however, a small percentage of the research papers were assigned a digital object identifier or had altmetrics scores. Mendeley had the highest proportion among other social networks to represent Iranian scholars’ outputs. Conclusion: Although the rate of scientific publications of Iranian scholars on common cancers has totally increased, they were heterogeneous in terms of quality and Altmetrics score. Therefore, it is recommended that the researchers select journals that assign a digital object identifier to their articles and publish their scientific papers in social media as well.

Introduction: Public-private partnership can help governments deal with resource constraints in their health sectors. The aim of this study was to investigate the effects of different models of publicprivate partnership on hospital mid-indicators and identify factors affecting its successful implementation. Methods: This critical review was conducted from 2000 to 2017. Databases, such as Google Scholar, Ovid, Proquest, PubMed, Science Direct, Scopus, Springer, Magiran, SID, IranMedex and Irandoc were searched using Persian and English key terms. Critical Appraisal Skills Program was used to assess the quality of papers which were ranked based on evidence pyramid. The factors influencing the successful implementation of public-private partnership in hospitals were identified through content analysis method. Results: A total of 20 articles were included in the study. Six models of public-private partnership, that is, the Alzira Model, Design, Build, Finance, Operate Contract/ Private Finance Initiative, Management contract, Co-location and Outsourcing, and Privatization were used in hospitals. Each model has different effects on equity, quality, efficiency and accessibility. The factors influencing the successful implementation of public-private partnership projects included: legal support, political support, financial incentives and capacity building. Conclusion: Public-private partnership models showed different effects on hospital mid-indicators. It seems that the Design, Build, Finance, Operate Contract/ Private Finance Initiative model is more suitable for construction projects, management contract, and outsourcing and Alzira model is appropriate for delivering hospital services. Therefore, health policymakers can apply different models of public-private partnership on the basis of their effects on hospital mid-indicators.

Introduction: The Internet of Things is a system of connected physical objects that are accessible through the internet. It has been widely applied to connect available medical resources and provide reliable, effective and smart healthcare services to people. Therefore, the aim of this paper was to identify and rank the internet of things in healthcare services. Methods: In this applied research, conducted in 2018, a mixed-method approach was used. In the qualitative phase, library resources and in the quantitative phase, a pairwise comparison questionnaire were used. A panel of experts consisting of seven members ranked the internet of things services. An interval type-2 fuzzy Analytic Hierarchy Process method was used for ranking the services. Results: The results revealed the importance of chronic disease management, indirect emergency services fall detection, patient profile, adverse drug reaction, children health information in the internet of things healthcare services. Conclusion: Policymakers working in the field of the internet of things should be aware of its role in saving human lives. Moreover, investment in the internet of things can help monitor aging people, patients and children. Understanding the importance and ranking of internet of things services may help managers and policymakers manage healthcare sectors more effectively.