Evidence map›Paper›PMID 16221948›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA

Exploring and developing consumer health vocabularies.

Qing T Zeng, Tony Tse

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
88citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

88 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024
    Article
  3. Observational
  4. Article
  5. Article
  6. MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2022
    Article
  7. A survey of automated methods for biomedical text simplification.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  8. Article
  9. Article
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  11. Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs.Computational and mathematical methods in medicine · 2022
    Article
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  16. Development of a plain-language guide for discussing breast cancer genetic counseling and testing with patients with limited health literacy.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2021
    Article
  17. Towards more patient friendly clinical notes through language models and ontologies.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021
    Article
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  19. Article
  20. Article

28 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Qing T ZengDepartment of Radiology, Decision Systems Group, Thorn 309, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. qzeng@dsg.bwh.harvard.edu
Tony Tse

Funding

Vocabulary Support for Consumer Health InformaticsR01LM007222 · NLM · UNIVERSITY OF UTAH · PI ZENG, QING · 2002 to 2010
$1.9M
Intramural NIH HHSNLM NIH HHS R01 LM007222NLM NIH HHS R01 LM07222
6 · The paper itself

Abstract

Laypersons ("consumers") often have difficulty finding, understanding, and acting on health information due to gaps in their domain knowledge. Ideally, consumer health vocabularies (CHVs) would reflect the different ways consumers express and think about health topics, helping to bridge this vocabulary gap. However, despite the recent research on mismatches between consumer and professional language (e.g., lexical, semantic, and explanatory), there have been few systematic efforts to develop and evaluate CHVs. This paper presents the point of view that CHV development is practical and necessary for extending research on informatics-based tools to facilitate consumer health information seeking, retrieval, and understanding. In support of the view, we briefly describe a distributed, bottom-up approach for (1) exploring the relationship between common consumer health expressions and professional concepts and (2) developing an open-access, preliminary (draft) "first-generation" CHV. While recognizing the limitations of the approach (e.g., not addressing psychosocial and cultural factors), we suggest that such exploratory research and development will yield insights into the nature of consumer health expressions and assist developers in creating tools and applications to support consumer health information seeking.

Indexed as

PatientsVocabularyVocabulary, ControlledHealthInformaticsTerminology as Topic

Identifiers

PMID16221948
PMCPMC1380193

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.