A Systematic Review of Online Value Co-Creation in the Healthcare Service Ecosystem

Nowadays, the role of patients in the healthcare domain is extending beyond being passive healthcare recipients to becoming “makers and shapers” of healthcare services. In the healthcare service ecosystem, Online Health Communities (OHCs) foster co-creation among the different actors. Over the last five years, a number of articles that focus on value co-creation in the healthcare services have surfaced that highlight the significance of the interactions and engagements between the healthcare ecosystem levels. Accordingly, this paper aims to conduct a systematic review of the literature focusing on the role of OHCs as facilitators of value co-creation in the healthcare service ecosystem. A systematic review of the literature was conducted with articles published between 2013 and 2018. Thematic analysis revealed three key themes including “value is ubiquitous”, “online resources connectivism”, and “informational and emotional support”. This paper provides a structured overview of the current literature and identifies opportunities for future research

Expertise Profiling in Evolving Knowledgecuration Platforms

Expertise modeling has been the subject of extensiveresearch in two main disciplines: Information Retrieval (IR) andSocial Network Analysis (SNA). Both IR and SNA approachesbuild the expertise model through a document-centric approachproviding a macro-perspective on the knowledge emerging fromlarge corpus of static documents. With the emergence of the Webof Data there has been a significant shift from static to evolvingdocuments, through micro-contributions. Thus, the existingmacro-perspective is no longer sufficient to track the evolution ofboth knowledge and expertise. In this paper we present acomprehensive, domain-agnostic model for expertise profiling inthe context of dynamic, living documents and evolving knowledgebases. We showcase its application in the biomedical domain andanalyze its performance using two manually created datasets

Semantic and Time-Dependent Expertise Profiling Models in Community-Driven Knowledge Curation Platforms

Online collaboration and web-based knowledge sharing have gained momentum as major components of the Web 2.0 movement. Consequently, knowledge embedded in such platforms is no longer static and continuously evolves through experts’ micro-contributions. Traditional Information Retrieval and Social Network Analysis techniques take a document-centric approach to expertise modeling by creating a macro-perspective of knowledge embedded in large corpus of static documents. However, as knowledge in collaboration platforms changes dynamically, the traditional macro-perspective is insufficient for tracking the evolution of knowledge and expertise. Hence, Expertise Profiling is presented with major challenges in the context of dynamic and evolving knowledge. In our previous study, we proposed a comprehensive, domain-independent model for expertise profiling in the context of evolving knowledge. In this paper, we incorporate Language Modeling into our methodology to enhance the accuracy of resulting profiles. Evaluation results indicate a significant improvement in the accuracy of profiles generated by this approach. In addition, we present our profile visualization tool, Profile Explorer, which serves as a paradigm for exploring and analyzing time-dependent expertise profiles in knowledge-bases where content evolves overtime. Profile Explorer facilitates comparative analysis of evolving expertise, independent of the domain and the methodology used in creating profiles

Online value co-creation in the healthcare service ecosystem: A review

Nowadays, the role of patients in the healthcare domain is extending beyond being passive healthcare recipients to becoming “makers and shapers” of healthcare services. In the healthcare service ecosystem, Online Health Communities (OHCs) foster co-creation among the different actors. Over the last five years, a number of articles that focus on value co-creation in the healthcare services have surfaced that highlight the significance of the interactions and engagements between the healthcare ecosystem levels. Accordingly, this paper aims to conduct a systematic review of the literature focusing on the role of OHCs as facilitators of value co-creation in the healthcare service ecosystem. A systematic review of the literature was conducted with articles published between 2013 and 2018. Thematic analysis revealed three key themes including “value is ubiquitous”, “online resources connectivism”, and “informational and emotional support”. This paper provides a structured overview of the current literature and identifies opportunities for future research

Reducing consumer uncertainty: Towards an ontology for geospatial user-centric metadata

With the increased use of geospatial datasets across heterogeneous user groups and domains, assessing fitness-for-use is emerging as an essential task. Users are presented with an increasing choice of data from various portals, repositories, and clearinghouses. Consequently, comparing the quality and evaluating fitness-for-use of different datasets presents major challenges for spatial data users. While standardization efforts have significantly improved metadata interoperability, the increasing choice of metadata standards and their focus on data production rather than potential data use and application, renders typical metadata documents insufficient for effectively communicating fitness-for-use. Thus, research has focused on the challenge of communicating fitness-for-use of geospatial data, proposing a more “user-centric” approach to geospatial metadata. We present the Geospatial User-Centric Metadata ontology (GUCM) for communicating fitness-for-use of spatial datasets to users in the spatial and other domains, to enable them to make informed data source selection decisions. GUCM enables metadata description for various components of a dataset in the context of different application domains. It captures producer-supplied and user-described metadata in structured format using concepts from domain-independent ontologies. This facilitates interoperability between spatial and nonspatial metadata on open data platforms and provides the means for searching/discovering spatial data based on user-specified quality and fitness-for-use criteria

Modelling expertise at different levels of granularity using semantic similarity measures in the context of collaborative knowledge-curation platforms

Collaboration platforms provide a dynamic environment where the content is subject to ongoing evolution through expert contributions. The knowledge embedded in such platforms is not static as it evolves through incremental refinements – or micro-contributions. Such refinements provide vast resources of tacit knowledge and experience. In our previous work, we proposed and evaluated a Semantic and Time-dependent Expertise Profiling (STEP) approach for capturing expertise from micro-contributions. In this paper we extend our investigation to structured micro-contributions that emerge from an ontology engineering environment, such as the one built for developing the International Classification of Diseases (ICD) revision 11. We take advantage of the semantically related nature of these structured micro-contributions to showcase two major aspects: (i) a novel semantic similarity metric, in addition to an approach for creating bottom-up baseline expertise profiles using expertise centroids; and (ii) the application of STEP in this new environment combined with the use of the same semantic similarity measure to both compare STEP against baseline profiles, as well as to investigate the coverage of these baseline profiles by STEP

Expertise profiling in evolving knowledge-curation platforms

Expertise modeling has been the subject of extensive research in two main disciplines: Information Retrieval (IR) and Social Network Analysis (SNA). Both IR and SNA approaches build the expertise model through a document-centric approach providing a macro-perspective on the knowledge emerging from large corpus of static documents. With the emergence of the Web of Data there has been a significant shift from static to evolving documents, through micro-contributions. Thus, the existing macro-perspective is no longer sufficient to track the evolution of both knowledge and expertise. In this paper we present a comprehensive, domain-agnostic model for expertise profiling in the context of dynamic, living documents and evolving knowledge bases. We showcase its application in the biomedical domain and analyze its performance using two manually created datasets