Assisting Consumer Health Information Retrieval with Query Recommendations

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 13; no. 1; pp. 80 - 90
• Main Authors: Zeng, Qing T., Crowell, Jonathan, Plovnick, Robert M., Kim, Eunjung, Ngo, Long, Dibble, Emily
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01.01.2006; Oxford University Press; American Medical Informatics Association
• Subjects: Adult; Consumer Behavior; Health; Humans; Information Storage and Retrieval - methods; Internet; Original Investigation; Patients; Semantics; Terminology as Topic; User-Computer Interface; Vocabulary, Controlled
• ISSN: 1067-5027; 1527-974X; 1527-974X
• DOI: 10.1197/jamia.M1820

Health information retrieval (HIR) on the Internet has become an important practice for millions of people, many of whom have problems forming effective queries. We have developed and evaluated a tool to assist people in health-related query formation. We developed the Health Information Query Assistant (HIQuA) system. The system suggests alternative/additional query terms related to the user's initial query that can be used as building blocks to construct a better, more specific query. The recommended terms are selected according to their semantic distance from the original query, which is calculated on the basis of concept co-occurrences in medical literature and log data as well as semantic relations in medical vocabularies. An evaluation of the HIQuA system was conducted and a total of 213 subjects participated in the study. The subjects were randomized into 2 groups. One group was given query recommendations and the other was not. Each subject performed HIR for both a predefined and a self-defined task. The study showed that providing HIQuA recommendations resulted in statistically significantly higher rates of successful queries (odds ratio = 1.66, 95% confidence interval = 1.16–2.38), although no statistically significant impact on user satisfaction or the users' ability to accomplish the predefined retrieval task was found. Providing semantic-distance-based query recommendations can help consumers with query formation during HIR.