ULearn: Personalized Medical Learning on the Web for Patient Empowerment
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 they 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, deepening, 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.
KeywordsPatient empowerment Search as learning e-health Health literacy Health seeking behavior
This work was partially supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754489 and by Science Foundation Ireland grant 13/RC/2094 with a co-fund of the European Regional Development Fund through the Southern & Eastern Regional Operational Programme to Lero - the Irish Software Research Centre (www.lero.ie).
- 1.World Health Organization: Framework on integrated, people-centred health services: Report by the Secretariat. World Health Assembly, (A69/39), pp. 1–12 (2016)Google Scholar
- 2.Alfano, M., Lenzitti, B., Lo Bosco, G., Taibi, D.: Development and practical use of a medical vocabulary-thesaurus-dictionary for patient empowerment. In: Rachev, B., Smrikarov, A. (eds.) Proceedings of the ACM 19th International Conference on Computer Systems and Technologies (CompSysTech 2018), pp. 88–93. ACM, New York (2018). https://doi.org/10.1145/3274005.3274017
- 3.Alfano, M., Lenzitti, B., Taibi, D., Helfert, M.: Provision of tailored health information for patient empowerment: an initial study. In Proceedings of the ACM International Conference on Computer Systems and Technologies (CompSysTech 2019) (2019)Google Scholar
- 5.Ghosh, S., Rath, M., Shah, C.: Searching as learning: exploring search behavior and learning outcomes in learning-related tasks. In: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval (CHIIR 2018), pp. 22–31. ACM, New York (2018). https://doi.org/10.1145/3176349.3176386
- 6.Barker, J., Kupersmith, J.: Recommended search strategy: analyze your topic & search with peripheral vision (2009). http://www.lib.berkeley.edu/TeachingLib/Guides/Internet/Strategies.html
- 7.Taibi, D., Rogers, R., Marenzi, I., Nejdl, W., Ahmad, Q.A.I., Fulantelli, G.: Search as research practices on the web: the SaR-Web platform for cross-language engine results analysis. In: Proceedings of the 8th ACM Conference on Web Science (2016)Google Scholar
- 8.Fulantelli, G., Marenzi, I., Ahmad, Q.A.I., Taibi, D.: SaR-Web-A tool to support search as learning processes. In: SAL@ SIGIR (2016)Google Scholar
- 9.Alfano, M., Lenzitti, B.: U-search: a meta engine for creation of knowledge paths on the web. In: Proceedings of the ACM International Conference on Computer Systems and Technologies (CompSysTech 2010), pp. 442–447. ACM, New York (2010). https://doi.org/10.1145/1839379.1839457
- 10.Alfano, M., Lenzitti, B., Lo Bosco, G.: U-MedSearch: a meta search engine of medical content for different users and learning needs. In: Proceedings of the International Conference on e-Learning (e-Learning 2015) (2015)Google Scholar
- 12.Campos, J., Dias de Figueiredo, A.: Searching the unsearchable: inducing serendipitous insights. In: Proceedings of the Fourth International Conference on Case-Based Reasoning, Vancouver, Canada (2001)Google Scholar
- 13.Shraddha, S., Naganna, S.: A review on K-means data clustering approach. Int. J. Inf. Comput. Technol. 4(17), 1847–1860 (2014). ISSN 0974-2239Google Scholar
- 15.Alfano, M., Lenzitti, B., Taibi, D., Helfert, M.: Facilitating access to health web pages with different language complexity levels. In: Proceedings of the 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2019), Heraklion-Crete, 2–4 May 2019, pp. 113–123 (2019)Google Scholar