Intelligent Healthcare Website Design with Keyword Cues to Facilitate Online Health Information Search

With the rapid growth of the internet, several healthcare websites emerge in the format of inquiries and answers, which can be of significant value to reuse for online health information searchers in retrieving their desired records as primary references. Unfortunately, most of those websites do not consider the challenges the searchers will face, as they quite often are inexperienced searchers with limited healthcare domain knowledge. In this study, we consider designing the intelligent healthcare website by supplementing keyword cues to facilitate the website survey environments in orientation and navigation for searchers. A three-phase design is proposed, involving selection of health concepts, common search queries, and search intentions. The design aims to ease searchers’ mental loads in the search process, and achieve good outcome quality worthy of searchers’ references. A prototype website on dermatology is developed to demonstrate its practical application. We conclude that the proposed design approach is feasible to facilitate online information searchers in acquiring the desired records in a pull selection manner.

[1]  Shelda Debowski,et al.  Wrong way: go back! An exploration of novice search behaviours while conducting an information search , 2001, Electron. Libr..

[2]  Samir Chatterjee,et al.  A Design Science Research Methodology for Information Systems Research , 2008 .

[3]  P. Jaccard THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .

[4]  Drew Foster,et al.  'Keep complaining til someone listens': Exchanges of tacit healthcare knowledge in online illness communities. , 2016, Social science & medicine.

[5]  Yi-Liang Zhao,et al.  Bridging the Vocabulary Gap between Health Seekers and Healthcare Knowledge , 2015, IEEE Transactions on Knowledge and Data Engineering.

[6]  Andrii Rozhok Orientation and Navigation in Vertebrates , 2008 .

[7]  Jon O Ebbert,et al.  Why patients visit their doctors: assessing the most prevalent conditions in a defined American population. , 2013, Mayo Clinic proceedings.

[8]  Gary Marchionini,et al.  Information Seeking in Electronic Environments , 1995 .

[9]  Gosse Bouma,et al.  Developing Offline Strategies for Answering Medical Questions , 2005 .

[10]  Georg Groh,et al.  Facilitating the Exchange of Explicit Knowledge through Ontology Mappings , 2001, FLAIRS.

[11]  Paul M. B. Vitányi,et al.  The Google Similarity Distance , 2004, IEEE Transactions on Knowledge and Data Engineering.

[12]  L. Zhu,et al.  Ontological modeling of electronic health information exchange , 2015, J. Biomed. Informatics.

[13]  Yan Zhang,et al.  Toward a layered model of context for health information searching: An analysis of consumer-generated questions , 2013, J. Assoc. Inf. Sci. Technol..

[14]  M. Numao,et al.  Effects of Individual Health Topic Familiarity on Activity Patterns During Health Information Searches , 2015, JMIR medical informatics.

[15]  Tzeng-Ji Chen,et al.  Skin care services and disease prevalence in Taiwan: A nationwide study , 2017, Dermatologica Sinica.