Evaluating the process of online health information searching: a qualitative approach to exploring consumer perspectives.

BACKGROUND: The Internet is a common resource that patients and consumers use to access health-related information. Multiple practical, cultural, and socioeconomic factors influence why, when, and how people utilize this tool. Improving the delivery of health-related information necessitates a thorough understanding of users' searching-related needs, preferences, and experiences. Although a wide body of quantitative research examining search behavior exists, qualitative approaches have been under-utilized and provide unique perspectives that may prove useful in improving the delivery of health information over the Internet. OBJECTIVE: We conducted this study to gain a deeper understanding of online health-searching behavior in order to inform future developments of personalizing information searching and content delivery. METHODS: We completed three focus groups with adult residents of Olmsted County, Minnesota, which explored perceptions of online health information searching. Participants were recruited through flyers and classifieds advertisements posted throughout the community. We audio-recorded and transcribed all focus groups, and analyzed data using standard qualitative methods. RESULTS: Almost all participants reported using the Internet to gather health information. They described a common experience of searching, filtering, and comparing results in order to obtain information relevant to their intended search target. Information saturation and fatigue were cited as main reasons for terminating searching. This information was often used as a resource to enhance their interactions with health care providers. CONCLUSIONS: Many participants viewed the Internet as a valuable tool for finding health information in order to support their existing health care resources. Although the Internet is a preferred source of health information, challenges persist in streamlining the search process. Content providers should continue to develop new strategies and technologies aimed at accommodating diverse populations, vocabularies, and health information needs.

Incorporating expert terminology and disease risk factors into consumer health vocabularies.

It is well-known that the general health information seeking lay-person, regardless of his/her education, cultural background, and economic status, is not as familiar with-or comfortable using-the technical terms commonly used by healthcare professionals. One of the primary reasons for this is due to the differences in perspectives and understanding of the vocabulary used by patients and providers even when referring to the same health concept. To bridge this "knowledge gap," consumer health vocabularies are presented as a solution. In this study, we introduce the Mayo Consumer Health Vocabulary (MCV)-a taxonomy of approximately 5,000 consumer health terms and concepts-and develop text-mining techniques to expand its coverage by integrating disease concepts (from UMLS) as well as non-genetic (from deCODEme) and genetic (from GeneWiki+ and PharmGKB) risk factors to diseases. These steps led to adding at least one synonym for 97% of MCV concepts with an average of 43 consumer friendly terms per concept. We were also able to associate risk factors to 38 common diseases, as well as establish 5,361 Disease:Gene pairings. The expanded MCV provides a robust resource for facilitating online health information searching and retrieval as well as building consumer-oriented healthcare applications.

Scalable representations of diseases in biomedical ontologies.

BACKGROUND: The realm of pathological entities can be subdivided into pathological dispositions, pathological processes, and pathological structures. The latter are the bearer of dispositions, which can then be realized by their manifestations - pathologic processes. Despite its ontological soundness, implementing this model via purpose-oriented domain ontologies will likely require considerable effort, both in ontology construction and maintenance, which constitutes a considerable problem for SNOMED CT, presently the largest biomedical ontology. RESULTS: We describe an ontology design pattern which allows ontologists to make assertions that blur the distinctions between dispositions, processes, and structures until necessary. Based on the domain upper-level ontology BioTop, it permits ascriptions of location and participation in the definition of pathological phenomena even without an ontological commitment to a distinction between these three categories. An analysis of SNOMED CT revealed that numerous classes in the findings/disease hierarchy are ambiguous with respect to process vs. disposition. Here our proposed approach can easily be applied to create unambiguous classes. No ambiguities could be defined regarding the distinction of structure and non-structure classes, but here we have found problematic duplications. CONCLUSIONS: We defend a judicious use of disjunctive, and therefore ambiguous, classes in biomedical ontologies during the process of ontology construction and in the practice of ontology application. The use of these classes is permitted to span across several top-level categories, provided it contributes to ontology simplification and supports the intended reasoning scenarios.

The ACGT Master Ontology and its applications--towards an ontology-driven cancer research and management system.

OBJECTIVE: This paper introduces the objectives, methods and results of ontology development in the EU co-funded project Advancing Clinico-genomic Trials on Cancer-Open Grid Services for Improving Medical Knowledge Discovery (ACGT). While the available data in the life sciences has recently grown both in amount and quality, the full exploitation of it is being hindered by the use of different underlying technologies, coding systems, category schemes and reporting methods on the part of different research groups. The goal of the ACGT project is to contribute to the resolution of these problems by developing an ontology-driven, semantic grid services infrastructure that will enable efficient execution of discovery-driven scientific workflows in the context of multi-centric, post-genomic clinical trials. The focus of the present paper is the ACGT Master Ontology (MO). METHODS: ACGT project researchers undertook a systematic review of existing domain and upper-level ontologies, as well as of existing ontology design software, implementation methods, and end-user interfaces. This included the careful study of best practices, design principles and evaluation methods for ontology design, maintenance, implementation, and versioning, as well as for use on the part of domain experts and clinicians. RESULTS: To date, the results of the ACGT project include (i) the development of a master ontology (the ACGT-MO) based on clearly defined principles of ontology development and evaluation; (ii) the development of a technical infrastructure (the ACGT Platform) that implements the ACGT-MO utilizing independent tools, components and resources that have been developed based on open architectural standards, and which includes an application updating and evolving the ontology efficiently in response to end-user needs; and (iii) the development of an Ontology-based Trial Management Application (ObTiMA) that integrates the ACGT-MO into the design process of clinical trials in order to guarantee automatic semantic integration without the need to perform a separate mapping process.