Health On The Net Foundation

mHealth is a huge market that provides users the opportunity to have better health and healthcare quality. Health apps support citizen’s empowerment through self-management, health promotion, disease prevention, providing personalized health advice and care. However, the rapid development of the mHealth sector raises concerns about the potential risk of health functions apps providing transmission of health data, the capture of these data via sensors, self-diagnoses, disease management or diagnosis and appropriate processing of the data collected. Since mHealth solutions and devices can collect large quantities of personal information, including personal health information, they can process them as well.The mHONcode is a set of ethical, honesty, transparency, quality, and security standards covering various aspects of health apps, including the disclosure of the qualifications of the authors, the funding sources, references, when the content was created and last updated, what the privacy policy is, and how data is stored and transmitted over the internet. The mHONcode motivates health apps editors to be transparent in the production process and in the way to use user’s data. The commitment of a health information provider to implement or comply with the HON code of conduct for health apps is shown by the displaying of a quality label (logo or HONcode seal) on the website.As the adaptation of an already proven trustworthy code of conduct of health websites (the HONcode), the mHONcode is well placed to provide guidance for the next generation of health information providers—mobile apps in this case.KeywordsAppsmHealthTrustworthinessCertificationQuality

based approaches to all areas of linguistics with a particular focus on the comparative and historical, and includes the widest possible range of languages.

The HONcode provided by Health On the Net (HON) foundation is the most successful third party certification initiative. In nearly 20 years, it has acquired a database of over 8000 trustworthy health websites, has been translated into over 30 languages and become not only a well-respected name amongst health information providers but also an increasingly well-recognised brand amongst health information end-users. The HONcode, having begun in the mid-1990s has managed to stay current and relevant two decades on, because of its ability to change and remain relevant to the present times. The past few years have been no different, and HON has been heavily involved in bringing out certain updates to further its mission of unrestricted trustworthy health information online. In this paper we present the HONcode certification process and the main changes taking place in the certification process to enable its continued success and sustainability.

This paper presents the results of a blind comparison of top ten search results retrieved by Google.ch (French) and Khresmoi for everyone, a health specialized search engine. Participants--students of the Faculty of Medicine of the University of Geneva had to complete three tasks and select their preferred results. The majority of the participants have largely preferred Google results while Khresmoi results showed potential to compete in specific topics. The coverage of the results seems to be one of the reasons. The second being that participants do not know how to select quality and transparent health web pages. More awareness, tools and education about the matter is required for the students of Medicine to be able to efficiently distinguish trustworthy online health information.

To evaluate the extent to which the Internet is accessed for health information and perceived as useful to varying groups classified primarily according to age. Synopsis of the articles on education and consumer health informatics selected for the IMIA Yearbook of Medical Informatics 2010. A growing number of individuals are actively seeking health information through a varying selection of resources. The Internet is now seen as a major source of health information alongside with books and other means of paper-based literature. However, it is not clear how the Internet is perceived by varied groups such as those coming from differing age groups. The papers selected attempt to obtain a better understanding about how the public perceives and uses the Internet as an information gathering tool-especially for health information. The papers also explore into how the Internet is used by different groups of people. As all online health information is not of uniform quality, it is important to access and rely on quality medical information. This issue is also dealt with, where the popularity of Wikipedia is measured with the popularity of reliable web sources such as Medline Plus and NHS Direct.

This article presents the 2009 selection of the best papers presenting the advancements in the field of Education and Consumer Informatics with emphasis on quality online health information and its uses in the medical field. Synopsis of the articles selected for the IMIA yearbook 2009 Six papers from international peer reviewed journals where selected for this section. The selection of articles gives an overview of the currently growing need for quality online health information and demonstrates the integration of online health information in medical care as well as the importance of continuing education through online training programs.

Many attempts have been made in the QA domain but no system applicable to the field of health is currently available on the Internet. This paper describes a bilingual French/English question answering system adapted to the health domain and more particularly the detection of the question's model. Indeed, the Question Analyzer module for identifying the question's model has a greater effect on the rest of the QA system. Our original hypothesis for the QA is that a question can be defined by two criteria: type of answer expected and medical type. These two must appear in the step of detection of the model in order to better define the type of question and thus, the corresponding answer. For this, questions were searched on the Internet and then given to experts in order to obtain classifications according to criteria such as type of question and type of medical context as mentioned above. In addition, tests of supervised and non-supervised classification were made to determine features of questions. The result of this first step was that algorithms of classification were chosen. The results obtained showed that categorizers giving the best results were the SVM. Currently, for a set of 100 questions, 84 are well categorized in English and 68 in French according to the type of answer expected. This figures fall to less than 50% for the medical type. Evaluations have showed that the system was good to identify the type of answer expected and could be enhanced for the medical type. It leads us to use an external source of knowledge: UMLS. A future improvement will be the usage of UMLS semantic network to better categorize a query according to the medical domain.