Evidence map›Paper›PMID 32297872›Full record

ArticleJournal of medical Internet research2020

Patient Questions and Physician Responses in a Chinese Health Q&A Website: Content Analysis.

Ziying Hong, Zhaohua Deng, Richard Evans, Haiyan Wu

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–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

10 citing papers in PubMed.

  1. Article
  2. Large-Scale Analysis of Online Questions Related to Opioid Use Disorder on Reddit.Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. Self-Adaptive Telemedicine Specialist Recommendation Considering Specialist Activity and Patient Feedback.International journal of environmental research and public health · 2022
    Article
  7. Article
  8. Article
  9. Article
  10. An empirical study of Q&A websites for game developers.Empirical software engineering · 2021
    Article
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

4 authors.

Ziying HongSchool of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID 0000-0003-4160-9739
Zhaohua DengSchool of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID 0000-0002-7744-7818
Richard EvansCollege of Engineering, Design and Physical Sciences, Brunel University, London, United Kingdom.ORCID 0000-0001-6367-0560
Haiyan WuUndergraduate School of Medical Business, Guangdong Pharmaceutical University, Zhongshan, China.ORCID 0000-0002-0945-0062

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince the turn of this century, the internet has become an invaluable resource for people seeking health information and answers to health-related queries. Health question and answer websites have grown in popularity in recent years as a means for patients to obtain health information from medical professionals. For patients suffering from chronic illnesses, it is vital that health care providers become better acquainted with patients' information needs and learn how they express them in text format.

objectiveThe aims of this study were to: (1) explore whether patients can accurately and adequately express their information needs on health question and answer websites, (2) identify what types of problems are of most concern to those suffering from chronic illnesses, and (3) determine the relationship between question characteristics and the number of answers received.

methodsQuestions were collected from a leading Chinese health question and answer website called "All questions will be answered" in January 2018. We focused on questions relating to diabetes and hepatitis, including those that were free and those that were financially rewarded. Content analysis was completed on a total of 7068 (diabetes) and 6685 (hepatitis) textual questions. Correlations between the characteristics of questions (number of words per question, value of reward) and the number of answers received were evaluated using linear regression analysis.

resultsThe majority of patients are able to accurately express their problem in text format, while some patients may require minor social support. The questions posted were related to three main topics: (1) prevention and examination, (2) diagnosis, and (3) treatment. Patients with diabetes were most concerned with the treatment received, whereas patients with hepatitis focused on the diagnosis results. The number of words per question and the value of the reward were negatively correlated with the number of answers. The number of words per question and the value of the reward were negatively correlated with the number of answers.

conclusionsThis study provides valuable insights into the ability of patients suffering from chronic illnesses to make an understandable request on health question and answer websites. Health topics relating to diabetes and hepatitis were classified to address the health information needs of chronically ill patients. Furthermore, identification of the factors affecting the number of answers received per question can help users of these websites to better frame their questions to obtain more valuable answers.

Indexed as

InternetChinaData CollectionHumansPatientsPhysiciansSurveys and Questionnaireshealth information seekingquestion classification

Identifiers

PMID32297872
PMCPMC7193435

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.