ULearn: Personalized Medical Learning on the Web for Patient Empowerment

Abstract

Health literacy constitutes an important step towards patient empowerment and the Web is presently the biggest repository of medical information and, thus, the biggest medical resource to be used in the learning process. However, at present web medical information is mainly accessed through generic search engines that do not take into account the user specific needs and starting knowledge and so are not able to support learning activities tailored to the specific user requirements. This work presents "ULearn" a meta engine that supports access, understanding and learning on the Web in the medical domain based on specific user requirements and knowledge levels towards what we call "balanced learning". Balanced learning allows users to perform learning activities based on specific user requirements (understanding, deepinning, widening and exploring) towards his/her empowerment. We have designed and developed ULearn to suggest search keywords correlated to the different user requirements and we have carried out some preliminary experiments to evaluate the effectiveness of the provided information.

Tailored Retrieval of Health Information from the Web for Facilitating Communication and Empowerment of Elderly People

“…It calls for a paradigm shift on the relation between patients/citizens and health. In fact, empowered patients have the necessary knowledge, skills, attitudes and self-awareness about their condition to understand their lifestyle and treatment options, make informed choices about their health and have control over the management of their condition/health in their daily life (European Health Parliament, 2017;Alfano et al, 2019a;Alfano et al, 2019b;Bodolica et al, 2019;Bravo et al, 2015, Cerezo et al, 2016Fumagalli et al, 2015). As seen in Section 1, the acquisition of medical/health information is a basic step in the empowerment process and the main source of health/medical information is, nowadays, the Web (Pew Research Center, 2013; Taylor, 2010; UK national statistics, 2010; Instituto Nacional de Estadística, 2010).…”

“…3) and receiving, at most, the top fifty results. Notice that a user usually analyses the first twenty-five-thirty results when using a generic search engine such as Google™ (Alfano et al 2019b). Thus, from this point of view, the user is not penalized by using FACILE even though it is not using the whole Web but only the part that contains schema.org structured data.…”

Language Complexity in On-line Health Information Retrieval

“…Finally, other user requirements, such as the quality of information and the information classification/customization, have to be taken into account and other types and properties of the schema.org vocabulary have to be included in the proposed method in order to provide users with on-line resources that satisfy the different user requirements and allow them to easily acquire, comprehend and learn health/medical information by exploiting the Web [26], [27], [28].…”

“…Since this computation takes some time, the search, in this case, is not in real time in the sense that it is not providing the user with an answer in a time comparable to that of a generic search engine. Notice that the interface allows to specify the number of Google results (maximum fifty, higher than the twenty-thirty results usually analysed by a user [26]).…”