A model for online consumer health information quality

Abstract

This article describes a model for online consumer health information consisting of five quality criteria constructs. These constructs are grounded in empirical data from the perspectives of the three main sources in the communication process: health information providers, consumers, and intermediaries, such as Web directory creators and librarians, who assist consumers in finding healthcare information. The article also defines five constructs of Web page structural markers that could be used in information quality evaluation and maps these markers to the quality criteria. Findings from correlation analysis and multinomial logistic tests indicate that use of the structural markers depended significantly on the type of Web page and type of information provider. The findings suggest the need to define genre-specific templates for quality evaluation and the need to develop models for an automatic genre-based classification of health information Web pages. In addition, the study showed that consumers may lack the motivation or literacy skills to evaluate the information quality of health Web pages, which suggests the need to develop accessible automatic information quality evaluation tools and ontologies.

Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

“…Various qualitative methods such as interview and reports analysis, usually interpreted using grounded theory have been implemented to evaluate usability (Kerr et al 2007), privacy (Stvilia et al 2009), comparability (Kerr et al 2007) and relevance (Kerr et al 2007). Consistency ) of data has been assessed with concept mapping in non-health contexts.…”

“…Consistency ) of data has been assessed with concept mapping in non-health contexts. Timeliness (currency) (Huaman et al 2009;Kerr et al 2007), accuracy (precision) (Stvilia et al 2009), reliability (Britt et al 2007), representativeness (Britt et al 2007), correctness (Gillies 2000a) and completeness (Kiragga et al 2011) were assessed with quantitative statistical methods.…”

“…The majority of these studies used precision and recall as metrics to assess the accuracy and validate the ontological approaches (Brank et al 2005;Brewster et al 2004;Euzenat 2007;Gangemi et al 2006;Li 2010;Min et al 2009;Pathak et al 2012aPathak et al , 2012bSpasic and Ananiadou, 2005;Stvilia et al 2009;Valencia-Garcia et al 2008;Wang et al 2007).…”

“…Defined purpose Assessed of fitness for purpose using DQ Context Assessment (7 papers) (Jacquelinet et al 2003) To develop semantic data interoperability -Apply an ontological tool to develop semantic data interoperability through domain terminologies using quantitative analysis of the existing coding information system and a qualitative analysis checking completeness, consistency, ambiguity and implicitness of terms Failure, dialysis and transplant datasets from National information system in France -Represent DQ factors such as completeness of data, appropriated terms, structured thesaurus, and terminology standard -Authors state usefulness of ontology based approach to support the processing of texts, and extending a terminological basis for medical experts (Maragoudakis et al 2008 Gene expression data which involve the use of microarrays in UK -Guide the development and use of metrics to measure the complexity and cohesion of ontologies -Authors state that ontology helps to allow a practical division of the work between providers and consumers, in order to minimize the costs to all concerned 2012b; Spasic and Ananiadou 2005;Stvilia et al 2009;Valencia-Garcia et al 2008;Wang et al 2007). Table 9 illustrates various definitions to identify the most common criteria to assess validity of ontologies and data models.…”

eHealth-as-a-Service: A Service Based Design Approach for Large Scale eHealth Architecture

“…However, a general criticism within the information quality domain is a lack of methods for the assessment Chapter 2: Literature Review 41 of quality dimensions. In this respect, operationalisation and metrics tend to change with the context of their application (Stvilia, Mon, & Yi, 2009). Thus, the challenge for this thesis is not only the identification of relevant quality dimensions to inform the design process but also the development of metrics in order to evaluate the design artifact, (this is explored further in Chapter 6).…”