research-article

Health Cards for Consumer Health Search

Authors:

Gianluca DemartiniAuthors Info & Claims

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 35 - 44

https://doi.org/10.1145/3331184.3331194

Published: 18 July 2019 Publication History

Abstract

This paper investigates the impact of health cards in consumer health search (CHS) - people seeking health advice online. Health cards are a concise presentations of a health concept shown along side search results to specific health queries; they have the potential to convey health information in easily digestible form for the general public. However, little evidence exists on how effective health cards actually are for users when searching health advice online, and whether their effectiveness is limited to specific health search intents. To understand the impact of health cards on CHS, we conducted a laboratory study to observe users completing CHS tasks using two search interface variants: one just with result snippets and one containing both result snippets and health cards. Our study makes the following contributions: (1) it reveals how and when health cards are beneficial to users in completing consumer health search tasks, and (2) it identifies the features of health cards that helped users in completing their tasks. This is the first study that thoroughly investigates the effectiveness of health cards in supporting consumer health search.

Supplementary Material

MP4 File (cite3-11h20-d1.mp4)

Download
406.48 MB

References

[1]

Laurence Alpay, John Verhoef, Bo Xie, Dov Te'eni, and J. H. M. Zwetsloot-Schonk. 2009. Current challenge in consumer health informatics: Bridging the gap between access to information and information understanding. Journal of BII, Vol. 2 (2009), BII--S2223.

[2]

Jaime Arguello, Fernando Diaz, Jamie Callan, and Ben Carterette. 2011. A methodology for evaluating aggregated search results. In ECIR'11. Springer, 141--152.

Digital Library

[3]

Krisztian Balog. 2018. Utilizing Entities for an Enhanced Search Experience. In: Entity-Oriented Search. Vol. 39. Springer.

[4]

Pieter Blignaut. 2009. Fixation identification: The optimum threshold for a dispersion algorithm. Journal of APP, Vol. 71, 4 (2009), 881--895.

[5]

Pia Borlund. 2000. Experimental components for the evaluation of interactive information retrieval systems. Journal of Documentation, Vol. 56, 1 (2000), 71--90.

[6]

Horatiu Bota, Ke Zhou, and Joemon M. Jose. 2016. Playing your cards right: The effect of entity cards on search behaviour and workload. In CHIIR'16. ACM, 131--140.

Digital Library

[7]

Melanie Diez, Deborah A. Boehm-Davis, Robert W. Holt, Mary E. Pinney, Jeffrey T. Hansberger, and Wolfgang Schoppek. 2001. Tracking pilot interactions with flight management systems through eye movements. In ISAP'01, Vol. 6. Citeseer.

[8]

Evgeniy Gabrilovich. 2016. Cura Te Ipsum: answering symptom queries with question intent. In Second WebQA workshop, SIGIR 2016 (invited talk).

[9]

Faegheh Hasibi, Krisztian Balog, and Svein Erik Bratsberg. 2017. Dynamic Factual Summaries for Entity Cards. In SIGIR'17. ACM, 773--782.

Digital Library

[10]

Jimmy, Guido Zuccon, and Gianluca Demartini. 2018. On the Volatility of Commercial Search Engines and its Impact on Information Retrieval Research. In SIGIR'18. ACM, 1105--1108.

Digital Library

[11]

Jimmy, Guido Zuccon, and Bevan Koopman. 2018. Payoffs and pitfalls in using knowledge-bases for consumer health search. IRJ (2018), 1--45.

Digital Library

[12]

Jimmy, Guido Zuccon, Joao Palotti, Lorraine Goeuriot, and Liadh Kelly. 2018. Overview of the CLEF 2018 Consumer Health Search Task. In CLEF 2018 Evaluation Labs and Workshop: Online Working Notes (CEUR Workshop Proceedings).

[13]

Diane Kelly et al. 2009. Methods for evaluating interactive information retrieval systems with users. Foundations and Trends® in Information Retrieval, Vol. 3, 1-2 (2009), 1--224.

Digital Library

[14]

Diane Kelly, Jaime Arguello, Ashlee Edwards, and Wan-ching Wu. 2015. Development and evaluation of search tasks for IIR experiments using a cognitive complexity framework. In ICTIR'15. ACM, 101--110.

Digital Library

[15]

Dmitry Lagun, Chih-Hung Hsieh, Dale Webster, and Vidhya Navalpakkam. 2014. Towards better measurement of attention and satisfaction in mobile search. In SIGIR'14. ACM, 113--122.

Digital Library

[16]

Annie Y. S. Lau and Enrico W. Coiera. 2007. Do people experience cognitive biases while searching for information? JAMIA, Vol. 14, 5 (2007), 599--608.

[17]

Yuelin Li and Nicholas J. Belkin. 2008. A faceted approach to conceptualizing tasks in information seeking. Journal of IPM, Vol. 44, 6 (2008), 1822--1837.

Digital Library

[18]

Yuelin Li and Nicholas J. Belkin. 2010. An exploration of the relationships between work task and interactive information search behavior. JASIST, Vol. 61, 9 (2010), 1771--1789.

Digital Library

[19]

Sunghoon Lim, Conrad S. Tucker, and Soundar Kumara. 2017. An unsupervised machine learning model for discovering latent infectious diseases using social media data. JBI, Vol. 66 (2017), 82--94.

[20]

Carla Teixeira Lopes and Cristina Ribeiro. 2015. Effects of terminology on health queries: An analysis by user's health literacy and topic familiarity. In Current issues in libraries, information science and related fields. 145--184.

[21]

Edward Alan Miller and Antoinette Pole. 2010. Diagnosis blog: checking up on health blogs in the blogosphere. AJPH, Vol. 100, 8 (2010), 1514--1519.

[22]

Sunil Mohan, Nicolas Fiorini, Sun Kim, and Zhiyong Lu. 2018. A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval. In WWW'18. 77--86.

Digital Library

[23]

Patrick Cheong-Iao Pang, Shanton Chang, Karin Verspoor, and Jon Pearce. 2016. Designing Health Websites Based on Users' Web-Based Information-Seeking Behaviors: A Mixed-Method Observational Study. JMIR, Vol. 18, 6 (2016).

[24]

Robert M Plovnick and Qing T Zeng. 2004. Reformulation of consumer health queries with professional terminology: a pilot study. JMIR, Vol. 6, 3 (2004).

[25]

Frances A. Pogacar, Amira Ghenai, Mark D. Smucker, and Charles L. A. Clarke. 2017. The Positive and Negative Influence of Search Results on People's Decisions about the Efficacy of Medical Treatments. In SIGIR'17. ACM, 209--216.

Digital Library

[26]

Filip Radlinski, Nick Craswell, Bodo Billerbeck, Milad Shokouhi, Sanaz Ahari, Nitin Agrawal, Timothy Hoad, Song Zhou, and Muhammad Aatif Awan. 2015. Entity detection and extraction for entity cards. US Patent 9,158,846.

[27]

David Robins, Jason Holmes, and Mary Stansbury. 2010. Consumer health information on the Web: The relationship of visual design and perceptions of credibility. JASIST, Vol. 61, 1 (2010), 13--29.

Digital Library

[28]

Dario D. Salvucci and Joseph H. Goldberg. 2000. Identifying fixations and saccades in eye-tracking protocols. In ETRA'00. ACM, 71--78.

Digital Library

[29]

Milad Shokouhi and Qi Guo. 2015. From queries to cards: Re-ranking proactive card recommendations based on reactive search history. In SIGIR'15. ACM, 695--704.

Digital Library

[30]

Luca Soldaini, Andrew Yates, Elad Yom-Tov, Ophir Frieder, and Nazli Goharian. 2016. Enhancing web search in the medical domain via query clarification. IRJ, Vol. 19, 1-2 (2016), 149--173.

Digital Library

[31]

Luca Soldaini and Elad Yom-Tov. 2017. Inferring Individual Attributes from Search Engine Queries and Auxiliary Information. In WWW'17. 293--301.

Digital Library

[32]

Isabelle Stanton, Samuel Ieong, and Nina Mishra. 2014. Circumlocution in diagnostic medical queries. In SIGIR'14. ACM, 133--142.

Digital Library

[33]

Elaine G. Toms and Celeste Latter. 2007. How consumers search for health information. Health informatics journal, Vol. 13, 3 (2007), 223--235.

[34]

Ryen White. 2013. Beliefs and biases in web search. In SIGIR'13. ACM, 3--12.

Digital Library

[35]

Mark Wilson and Karen Draney. 2004. Some Links Between Large-Scale and Classroom Assessments: The Case of the BEAR Assessment System. Yearbook of the National Society for the Study of Education, Vol. 103, 2 (2004), 132--154.

[36]

Qing Zeng, S. Kogan, N. Ash, R. A. Greenes, A. A. Boxwala, et al. 2002. Characteristics of consumer terminology for health information retrieval. Methods of Information in Medicine-Methodik der Information in der Medizin, Vol. 41, 4 (2002), 289--298.

[37]

Qing T. Zeng, Jonathan Crowell, Robert M. Plovnick, Eunjung Kim, Long Ngo, and Emily Dibble. 2006. Assisting consumer health information retrieval with query recommendations. JAMIA, Vol. 13, 1 (2006), 80--90.

[38]

Bin Zou, Vasileios Lampos, and Ingemar Cox. 2018. Multi-Task Learning Improves Disease Models from Web Search. In WWW'18. 87--96.

Digital Library

[39]

Guido Zuccon and Bevan Koopman. 2018. SIGIR 2018 Tutorial on Health Search (HS2018): A Full-day from Consumers to Clinicians. In SIGIR'18. ACM, 1391--1394.

Digital Library

[40]

Guido Zuccon, Bevan Koopman, and Joao Palotti. 2015. Diagnose this if you can. In ECIR'15. Springer, 562--567.

Cited By

Reimer JSchmidt SFröbe MGienapp LScells HStein BHagen MPotthast MChen HDuh WHuang HKato MMothe JPoblete B(2023)The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web ArchivesProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591890(2848-2860)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591890
Jiang TLiu J(2022)Reflection on future directions: a systematic review of reported limitations and solutions in interactive information retrieval user studiesAslib Journal of Information Management10.1108/AJIM-05-2022-0253Online publication date: 19-Dec-2022
https://doi.org/10.1108/AJIM-05-2022-0253
Bondarenko AShirshakova EDriker MHagen MBraslavski PDemartini GZuccon GCulpepper JHuang ZTong H(2021)Misbeliefs and Biases in Health-Related SearchesProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482141(2894-2899)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482141
Show More Cited By

Index Terms

Health Cards for Consumer Health Search
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies

Recommendations

Health Card Retrieval for Consumer Health Search: An Empirical Investigation of Methods
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

This paper investigates methods to rank health cards, a domain-specific type of entity cards, for consumer health search (CHS) queries. A key challenge in this context is which card(s) should be presented to the user. In particular, little evidence ...
Contemporary Consumer Health Informatics
Contemporary Consumer Health Informatics

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2019

1512 pages

ISBN:9781450361729

DOI:10.1145/3331184

General Chairs:
Benjamin Piwowarski
CNRS - Sorbonne Universite, France
,
Max Chevalier
Universite de Toulouse, CNRS, France
,
Eric Gaussier
Universite Grenoble Alpes, CNRS, France
,
Program Chairs:
Yoelle Maarek
Amazon Research, Israel
,
Jian-Yun Nie
University of Montreal, Canada
,
Falk Scholer
RMIT University, Australia

Copyright © 2019 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Indonesia Endowment Fund for Education (LPDP)
Australian Research Council DECRA Research Fellowship
Google Faculty Award

Conference

SIGIR '19

Sponsor:

SIGIR

SIGIR '19: The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 21 - 25, 2019

Paris, France

Acceptance Rates

SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
434
Total Downloads

Downloads (Last 12 months)13
Downloads (Last 6 weeks)2

Reflects downloads up to 15 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Reimer JSchmidt SFröbe MGienapp LScells HStein BHagen MPotthast MChen HDuh WHuang HKato MMothe JPoblete B(2023)The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web ArchivesProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591890(2848-2860)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591890
Jiang TLiu J(2022)Reflection on future directions: a systematic review of reported limitations and solutions in interactive information retrieval user studiesAslib Journal of Information Management10.1108/AJIM-05-2022-0253Online publication date: 19-Dec-2022
https://doi.org/10.1108/AJIM-05-2022-0253
Bondarenko AShirshakova EDriker MHagen MBraslavski PDemartini GZuccon GCulpepper JHuang ZTong H(2021)Misbeliefs and Biases in Health-Related SearchesProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482141(2894-2899)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482141
Potthast MGünther SBevendorff JBittner JBondarenko AFröbe MKahmann CNiekler AVölske MStein BHagen MDiaz FShah CSuel TCastells PJones RSakai T(2021)The Information Retrieval AnthologyProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462798(2550-2555)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462798
Scells HJimmy Zuccon GDiaz FShah CSuel TCastells PJones RSakai T(2021)Big Brother: A Drop-In Website Interaction Logging ServiceProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462781(2590-2594)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462781
van der Vegt AZuccon GKoopman B(2021)Do better search engines really equate to better clinical decisions? If not, why not?Journal of the Association for Information Science and Technology10.1002/asi.2439872:2(141-155)Online publication date: 18-Jan-2021
https://dl.acm.org/doi/10.1002/asi.24398
Wu ZSanderson MCambazoglu BCroft WScholer Fd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Providing Direct Answers in Search Results: A Study of User BehaviorProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412017(1635-1644)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412017
Shajalal MAono M(2020)Health Information RetrievalSignal Processing Techniques for Computational Health Informatics10.1007/978-3-030-54932-9_8(193-207)Online publication date: 8-Oct-2020
https://doi.org/10.1007/978-3-030-54932-9_8
Hersh WHersh W(2020)EvaluationInformation Retrieval: A Biomedical and Health Perspective10.1007/978-3-030-47686-1_7(289-335)Online publication date: 23-Jul-2020
https://doi.org/10.1007/978-3-030-47686-1_7

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents