Health Information management
Afsaneh sadat Hashemi; Fathemeh Makkizadeh
Abstract
Introduction: This research investigates the articles' structure and thematic relationships in environmental health. Studying scientific outputs using scientometric indicators is an effective tool for understanding scientific research.Methods: The present research was conducted with a scientometric approach ...
Read More
Introduction: This research investigates the articles' structure and thematic relationships in environmental health. Studying scientific outputs using scientometric indicators is an effective tool for understanding scientific research.Methods: The present research was conducted with a scientometric approach to content analysis of texts using the co-occurrence method of words and social network analysis. The statistical population includes 7438 articles indexed in the Web of Science Core Collection (WOSCC) in the field of environmental health in the period 2011-2020. Data analysis was done using cluster analysis method and strategic diagram.Results: The findings showed that in terms of the frequency of public health keywords, air pollution and climate change were the most frequent in environmental health researches. The findings related to hierarchical clustering led to the formation of 10 clusters in this area, and the clusters of "heavy metals" and "air pollution" were identified as clusters with high centrality and density.Conclusion: According to the abundance of keywords and clusters obtained from the strategic diagram, it was found that the topics of heavy metals and air pollution are emerging fields in this field. Some important issues related to environmental health, such as environmental health management, water quality management, etc., have received less attention.Keywords: Environmental Health, Thematic Clusters, Thematic Structure, Scientometrics, Co-Word Analysis.
Health Information management
mohammad reza ahmadi varzaneh; ali rashidpour; Hamid Reza Peikari; amir reza naghsh
Abstract
Introduction: Mobile value-added services in health encompass all services beyond voice calls and their implementation carries many benefits. The aim of the present study is to rank the factors related to mobile value-added services in the health sector.Research Method: This research is of the applied ...
Read More
Introduction: Mobile value-added services in health encompass all services beyond voice calls and their implementation carries many benefits. The aim of the present study is to rank the factors related to mobile value-added services in the health sector.Research Method: This research is of the applied and descriptive-cross-sectional type with the statistical population of all information technology experts in the Social Security Organization of Tehran province, including 84 people. The measurement tool, with 64 items in 18 components, and its reliability was obtained with Cronbach’s alpha of 0.916. The validity of the questionnaire was confirmed by 5 experts. For data analysis, confirmatory factor analysis method and SmartPLS software were used. For ranking related factors, a pairwise comparison questionnaire was designed and made available to 15 specialized experts and their opinions were calculated and ranked using Expert choice software.Findings: The indices and coefficients obtained from the model of implementing mobile value-added services in the health sector have sufficient validity. The themes of effects and outcomes with a weight of 0.558, user understanding with a weight of 0.165, reliability with a weight of 0.115, mentality and expectations with a weight of 0.071, effective environmental conditions with a weight of 0.054, technology development with a weight of 0.037 have the most impact on the implementation of mobile value-added services in the health sector.Conclusion: Organizations providing health services can implement by considering effective factors such as effects and consequences for using these services and other factors based on priority, in order to improve the acceptance rate, in order to improve processes and increase satisfaction
Health Information management
Hoda Memarzadeh; Nasser Ghadiri; Maryam Lotfi shahreza
Abstract
Introduction: Identifying similar patients is effective in designing many secondary applications to improve the quality of treatments and research services. The similarity of the final diagnoses is one of the aspects of similar patient groups. In order to measure similarity between patients, it is crucial ...
Read More
Introduction: Identifying similar patients is effective in designing many secondary applications to improve the quality of treatments and research services. The similarity of the final diagnoses is one of the aspects of similar patient groups. In order to measure similarity between patients, it is crucial to convert their information into a comparable format. There are different types of data in electronic health records (EHR). An important part of patient EHR are clinical notes, which face challenges to process. Therefore, the present study aims to design a clinical language processing model to identify definitive diagnoses. Research method: In this study, the clinical notes of more than 26,000 patients from the MIMIC-III database were represented as vectors using modern language models, and these vectors were used as input for the diagnostic prediction model.Results: According to the results of the experiments, the BIO-BERT model with 0.715 and then the SciBERT model with 0.713 the best result between the biomedical language models. The results also show that using unique concepts extracted from clinical notes resulted in an increase in model accuracy. Conclusion: Representation models trained with specific biomedical data can be used to map latent clinical note information to embedding vectors and provide the ability to use notes in machine learning algorithms, including prediction of the final diagnostic group.
Health Information management
Rahim Shahbazi
Abstract
Introduction: Identifying the components affecting people's health literacy is one of the important areas in the field of medical sciences. In this study, the construction and validation of a health literacy questionnaire has been done.Methods: The current research is descriptive. The statistical sample ...
Read More
Introduction: Identifying the components affecting people's health literacy is one of the important areas in the field of medical sciences. In this study, the construction and validation of a health literacy questionnaire has been done.Methods: The current research is descriptive. The statistical sample of the research is 327 graduate students of Azarbaijan Shahid Madani University who were selected by simple random method. To identify the items of the questionnaire, databases and related sources were checked first. The result of the investigation was the preparation of the initial version of the health literacy questionnaire with 28 items. Exploratory factor analysis was used to extract dimensions and items, and Cronbach's alpha was used to check the reliability. Data statistical processing was done using SPSS and Monte Carlo software. Cronbach's alpha of the questionnaire was equal to 0.89.Results: Before the factor analysis, the correlation coefficients of the scores between the questionnaire questions were checked. According to the results of Kaiser-Meyer-Olkin test and Bartlett's Test of Sphericity, exploratory factor analysis on the questionnaire was deemed justified. The findings of factor analysis showed that by removing two items from the whole questionnaire and analyzing the rest and rotating the results with the Varimax method, four dimensions of "access to health information"; "understanding health information"; "evaluation of health information"; and "use of health information" is obtained. Conclusion: According to the findings, the proposed health literacy questionnaire has the necessary validity to assess the health literacy of adults.
Health Information management
skandar shirazi
Abstract
Introduction: The purpose of the present study is to identify the compensation strategies for dealing with the covid-19 pandemic among the medical staff.Research method: The research method is exploratory in terms of mixed type and has been done in two parts, qualitative and quantitative. In the qualitative ...
Read More
Introduction: The purpose of the present study is to identify the compensation strategies for dealing with the covid-19 pandemic among the medical staff.Research method: The research method is exploratory in terms of mixed type and has been done in two parts, qualitative and quantitative. In the qualitative part, through thematic analysis method and using semi-structured interviews with 19 experts who were selected through purposive sampling, policies affecting the management structure of medical centers were identified and categorized. The data of this section were categorized and analyzed using the open, central and selective three-stage coding method. In order to comment on each of the obtained dimensions and rank them, at this stage, the obtained data were tested using the analysis hierarchy process (AHP) using the geometric mean method.Findings: During this research, 179 sub-themes were categorized in the form of 43 main themes and 8 categories based on the results of calculating the final weight of the special vector, the categories of welfare services with a coefficient of 0.172, human resources policies with a coefficient of 0.152 and motivational solutions with a coefficient of 0.148 They ranked first to third respectively, followed by behavioral measures with a coefficient of 0.120, management solutions with a coefficient of 0.108, organizational solutions with a coefficient of 0.107, open communication with a coefficient of 0.103 and social support of the organization with a coefficient of 0.091 respectively. were rankedConclusion: The results of the hierarchical analysis test and calculating the geometric mean of indicators show that the main themes of democratic management styles, increasing the level of motivation of the treatment staff, implementing fun programs for members and their families, increasing financial support, in Prioritizing the physical health of the treatment staff, increasing the welfare services for the treatment staff, implementing support pol
)
