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 4 March 2005
- Accepted 27 August 2005
Abstract
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.
Footnotes
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Supported by National Institutes of Health grant R01 LM07222.
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The authors thank Lowell Ling and Chantal Friedman for assisting with the evaluation study.
