Literature DB >> 11604772

Patient and clinician vocabulary: how different are they?

Q Zeng1, S Kogan, N Ash, R A Greenes.   

Abstract

Consumers and patients are confronted with a plethora of health care information, especially through the proliferation of web content resources. Democratization of the web is an important milestone for patients and consumers since it helps to empower them, make them better advocates on their own behalf and foster better, more-informed decisions about their health. Yet lack of familiarity with medical vocabulary is a major problem for patients in accessing the available information. As a first step to providing better vocabulary support for patients, this study collected and analyzed patient and clinician terms to confirm and quantitatively assess their differences. We also analyzed the information retrieval (IR) performance resulting from these terms. The results showed that patient terminology does differ from clinician terminology in many respects including misspelling rate, mapping rate and semantic type distribution, and patient terms lead to poorer results in information retrieval.

Entities:  

Mesh:

Year:  2001        PMID: 11604772

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  14 in total

1.  Exploring medical expressions used by consumers and the media: an emerging view of consumer health vocabularies.

Authors:  Tony Tse; Dagobert Soergel
Journal:  AMIA Annu Symp Proc       Date:  2003

2.  Exploring and developing consumer health vocabularies.

Authors:  Qing T Zeng; Tony Tse
Journal:  J Am Med Inform Assoc       Date:  2005-10-12       Impact factor: 4.497

3.  Dynamic generation of a table of contents with consumer-friendly labels.

Authors:  Trudi Miller; Gondy Leroy; Elizabeth Wood
Journal:  AMIA Annu Symp Proc       Date:  2006

4.  Consumer health concepts that do not map to the UMLS: where do they fit?

Authors:  Alla Keselman; Catherine Arnott Smith; Guy Divita; Hyeoneui Kim; Allen C Browne; Gondy Leroy; Qing Zeng-Treitler
Journal:  J Am Med Inform Assoc       Date:  2008-04-24       Impact factor: 4.497

5.  Perils of providing visual health information overviews for consumers with low health literacy or high stress.

Authors:  Gondy Leroy; Trudi Miller
Journal:  J Am Med Inform Assoc       Date:  2010 Mar-Apr       Impact factor: 4.497

6.  An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource.

Authors:  Mohammed Ibrahim; Susan Gauch; Omar Salman; Mohammed Alqahtani
Journal:  PeerJ Comput Sci       Date:  2021-08-09

7.  Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients.

Authors:  Jinying Chen; Hong Yu
Journal:  J Biomed Inform       Date:  2017-03-04       Impact factor: 6.317

8.  Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text.

Authors:  Albert Park; Andrea L Hartzler; Jina Huh; David W McDonald; Wanda Pratt
Journal:  J Med Internet Res       Date:  2015-08-31       Impact factor: 5.428

9.  Consumers' Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites.

Authors:  Min Sook Park; Zhe He; Zhiwei Chen; Sanghee Oh; Jiang Bian
Journal:  JMIR Med Inform       Date:  2016-11-24

10.  Context-Sensitive Spelling Correction of Consumer-Generated Content on Health Care.

Authors:  Xiaofang Zhou; An Zheng; Jiaheng Yin; Rudan Chen; Xianyang Zhao; Wei Xu; Wenqing Cheng; Tian Xia; Simon Lin
Journal:  JMIR Med Inform       Date:  2015-07-31
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