Assisting Consumer Health Information Retrieval with Query Recommendations
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 Assis...
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| Published in | Journal of the American Medical Informatics Association : JAMIA Vol. 13; no. 1; pp. 80 - 90 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Elsevier Inc
01.01.2006
Oxford University Press American Medical Informatics Association |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1067-5027 1527-974X 1527-974X |
| DOI | 10.1197/jamia.M1820 |
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| Abstract | 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. |
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| AbstractList | 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. 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. 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.OBJECTIVEHealth 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.DESIGNWe 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.MEASUREMENTSAn 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.RESULTSThe 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.CONCLUSIONProviding semantic-distance-based query recommendations can help consumers with query formation during HIR. |
| Author | Zeng, Qing T. Dibble, Emily Kim, Eunjung Ngo, Long Crowell, Jonathan Plovnick, Robert M. |
| AuthorAffiliation | 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) |
| AuthorAffiliation_xml | – name: 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) |
| Author_xml | – sequence: 1 givenname: Qing T. surname: Zeng fullname: Zeng, Qing T. email: qzeng@dsg.bwh.harvard.edu – sequence: 2 givenname: Jonathan surname: Crowell fullname: Crowell, Jonathan – sequence: 3 givenname: Robert M. surname: Plovnick fullname: Plovnick, Robert M. – sequence: 4 givenname: Eunjung surname: Kim fullname: Kim, Eunjung – sequence: 5 givenname: Long surname: Ngo fullname: Ngo, Long – sequence: 6 givenname: Emily surname: Dibble fullname: Dibble, Emily |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16221944$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adult Consumer Behavior Health Humans Information Storage and Retrieval - methods Internet Original Investigation Patients Semantics Terminology as Topic User-Computer Interface Vocabulary, Controlled |
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| Title | Assisting Consumer Health Information Retrieval with Query Recommendations |
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