The number of citations that a research paper receives can be used as a measure of its scientific impact. The objective of this study was to identify and to examine the characteristics of top 100 cited articles in the field of Medical Informatics based on data acquired from the Thomson Reuters' Web of Science (WOS) in October, 2016. The data was collected using two procedures: first we included articles published in the 24 journals listed in the "Medical Informatics" category; second, we retrieved articles using the key words: "informatics", "medical informatics", "biomedical informatics", "clinical informatics" and "health informatics". After removing duplicate records, articles were ranked by the number of citations they received. When the 100 top cited articles had been identified, we collected the following information for each record: all WOS database citations, year of publication, journal, author names, authors' affiliation, country of origin and topics indexed for each record. Citations for the top 100 articles ranged from 346 to 7875, and citations per year ranged from 11.12 to 525. The majority of articles were published in the 2000s (n=43) and 1990s (n=38). Articles were published across 10 journals, most commonly Statistics in medicine (n=71) and Medical decision making (n=28). The articles had an average of 2.47 authors. Statistics and biostatistics modeling was the most common topic (n=71), followed by artificial intelligence (n=12), and medical errors (n=3), other topics included data mining, diagnosis, bioinformatics, information retrieval, and medical imaging. Our bibliometric analysis illustrated a historical perspective on the progress of scientific research on Medical Informatics. Moreover, the findings of the current study provide an insight on the frequency of citations for top cited articles published in Medical Informatics as well as quality of the works, journals, and the trends steering Medical Informatics.

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Purpose -Medicine is heavily dependent on images and health care professionals use medical images for clinical, educational and research purposes. This paper aims to investigate the resources used by health care professionals while searching for medical images. Design/methodology/approach -The research is based on a qualitative study that uses the Straussian version of grounded theory and involved 29 health care professionals from various health and biomedical departments working within Sheffield Teaching Hospitals NHS (National Health Service) Foundation Trust. Data collection was carried out using semi-structured interviews and think-aloud protocols. Findings -The findings show that health care professionals seek medical images in a variety of visual information sources, including those found online and from published medical literature. The research also identified a number of difficulties that health care professionals face when searching for medical images in various image resources. Originality/value -There have been few studies that investigated the image resources used by health care professionals. Thus, this study contributes to the understanding of medical image resources and information needs of health care professionals. A clear understanding of the medical image information needs of health care professionals is also vital to the design process and development of medical image retrieval systems.

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This article explores the models and frameworks developed on “research impact’. We aim to provide a comprehensive overview of related literature through scoping study method. The present research investigates the nature, objectives, approaches, and other main attributes of the research impact models. It examines to analyze and classify models based on their characteristics. Forty-seven studies and 10 reviews published between 1996 and 2020 were included in the analysis. The majority of models were developed for the impact assessment and evaluation purposes. We identified three approaches in the models, namely outcome-based, process-based, and those utilized both of them, among which the outcome-based approach was the most frequently used by impact models and evaluation was considered as the main objective of this group. The process-based ones were mainly adapted from the W.K. Kellogg Foundation logic model and were potentially eligible for impact improvement. We highlighted the scope of processes and other specific features for the recent models. Given the benefits of the process-based approach in enhancing and accelerating the research impact, it is important to consider such approach in the development of impact models. Effective interaction between researchers and stakeholders, knowledge translation, and evidence synthesis are the other possible driving forces contributing to achieve and improve impact.

Objectives: This study aimed to acquire knowledge about the factors affecting smartphone adoption for accessing information in medical settings in Iranian Hospitals. Methods: A qualitative and quantitative approach was used to conduct this study. Semi-structured interviews were conducted with 21 medical residents and interns in 2013 to identify determinant factors for smartphone adoption. Afterwards, nine relationships were hypothesised. We developed a questionnaire to test these hypotheses and to evaluate the importance of each factor. Structural equation modelling was used to analyse the causal relations between model parameters and to accurately identify determinant factors. Results: Eight factors were identified in the qualitative phase of the study, including perceived usefulness, perceived ease of use, training, internal environment, personal experience, social impacts, observability and job related characteristics. Among the studied factors, perceived usefulness, personal experience and job related characteristics were significantly associated with attitude to use a smartphone which accounted for 64% of the variance in attitude. Perceived usefulness had the strongest impact on attitude to use a smartphone. Conclusion: The factors that emerged from interviews were consistent with the Technology Acceptance Model (TAM) and some previous studies. TAM is a reliable model for understanding the factors of smartphone acceptance in medical settings.Keywords: consumer health information; information management; information seeking behaviour; social media Key messages• Perceived usefulness, personal experience and job related characteristics were the main factors predicting attitude to use a smartphone for accessing information in medical settings.• The Technology Acceptance Model (TAM) provides a good framework for understanding the factors behind the adoption of smartphones for accessing information in medical settings.• Librarians in medical settings and hospitals should provide health care professionals with highly used and licensed mobile medical resources.

This paper reports the results of a study investigating the relevance criteria used by health care professionals when seeking medical images. Data was collected from 26 participants using a think-a-loud protocol and face-to-face interviews and analysed using the Straussian version of Grounded Theory (GT). Findings show that participants made use of 27 relevance criteria, although did not agree on the most important. Our findings suggest that users apply different criteria in different situations when evaluating the relevancy of medical images. In addition, we have investigated the coverage of relevance criteria to search statements from the medical track of ImageCLEF (ImageCLEFMed). Analysis indicates that some of the criteria identified by our participants could be included in future runs of the international evaluation campaign.

INTRODUCTION: Social media is becoming a new tool for developing health knowledge management. However, despite the rapid growth of research in this area, few attempts have been made to review previous research. This study tried to summarize the opportunities and challenges of using social media to managing health knowledge. METHODOLOGY: This article used a narrative approach to collect and review studies. In this review, published documents during 2010–2019 were retrieved by search in the following three electronic scientific databases: Web of Knowledge, PubMed, and Google Scholar search engine using keywords including social media, public health, health knowledge, knowledge management, and health promotion. RESULTS: Social media by overcoming geographical barriers, developing health promotion, facilitating decision-making, and providing public health education has been able to enhancing health awareness and improving health behavior. Doctors' unwillingness to interact with the public, lack of compliance with the principles of medical ethics, users' privacy concerns, and difficulty of managing negative comments are the four challenges to health knowledge management in social media. CONCLUSION: Social media can be a suitable tool for developing health knowledge management processes if medical professional ethics and users' privacy managed properly.