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.

Comparative analysis of online health queries originating from personal computers and smart devices on a consumer health information portal.

BACKGROUND: The number of people using the Internet and mobile/smart devices for health information seeking is increasing rapidly. Although the user experience for online health information seeking varies with the device used, for example, smart devices (SDs) like smartphones/tablets versus personal computers (PCs) like desktops/laptops, very few studies have investigated how online health information seeking behavior (OHISB) may differ by device. OBJECTIVE: The objective of this study is to examine differences in OHISB between PCs and SDs through a comparative analysis of large-scale health search queries submitted through Web search engines from both types of devices. METHODS: Using the Web analytics tool, IBM NetInsight OnDemand, and based on the type of devices used (PCs or SDs), we obtained the most frequent health search queries between June 2011 and May 2013 that were submitted on Web search engines and directed users to the Mayo Clinic's consumer health information website. We performed analyses on "Queries with considering repetition counts (QwR)" and "Queries without considering repetition counts (QwoR)". The dataset contains (1) 2.74 million and 3.94 million QwoR, respectively for PCs and SDs, and (2) more than 100 million QwR for both PCs and SDs. We analyzed structural properties of the queries (length of the search queries, usage of query operators and special characters in health queries), types of search queries (keyword-based, wh-questions, yes/no questions), categorization of the queries based on health categories and information mentioned in the queries (gender, age-groups, temporal references), misspellings in the health queries, and the linguistic structure of the health queries. RESULTS: Query strings used for health information searching via PCs and SDs differ by almost 50%. The most searched health categories are "Symptoms" (1 in 3 search queries), "Causes", and "Treatments & Drugs". The distribution of search queries for different health categories differs with the device used for the search. Health queries tend to be longer and more specific than general search queries. Health queries from SDs are longer and have slightly fewer spelling mistakes than those from PCs. Users specify words related to women and children more often than that of men and any other age group. Most of the health queries are formulated using keywords; the second-most common are wh- and yes/no questions. Users ask more health questions using SDs than PCs. Almost all health queries have at least one noun and health queries from SDs are more descriptive than those from PCs. CONCLUSIONS: This study is a large-scale comparative analysis of health search queries to understand the effects of device type (PCs vs. SDs) used on OHISB. The study indicates that the device used for online health information search plays an important role in shaping how health information searches by consumers and patients are executed.

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.