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Assisting Consumer Health Information Retrieval with Query Recommendations

Affiliations of the authors: Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (QTZ, JC, RMP, EK); the Department of Biostatistics, Harvard School of Public Health, Boston, MA (LN); Bunker Hill Community College, Boston, MA (ED).

Correspondence and reprints: Qing T. Zeng, PhD, Department of Radiology, Decision Systems Group, Thorn 309, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115; e-mail: <qzeng{at}dsg.bwh.harvard.edu>.

Received for publication: 03/04/05; accepted for publication: 08/27/05.

Objective: 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.

Design: 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.

Measurements: 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.

Results: 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.

Conclusion: Providing semantic-distance-based query recommendations can help consumers with query formation during HIR.

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