Research on consumers' health information consultation patterns

Repetitions in online doctor-patient communication: Frequency, functions, and reasons

OBJECTIVES

To attain insights into language repetition during online doctor-patient communication (DPC), understand why doctors and patients use repetition, and improve the current deficiencies in online medical platforms.

METHODS

The study performed a content analysis of 72 sets of textual doctor-patient conversations on the Chinese online medical consultation platform Chunyu Doctor.

RESULTS

Repetitions occurred 1412 times in the 72 sets of online DPC. Patient self-repetitions were the most prevalent (30.7 %), while patient repetitions of doctors were the least common (17.1 %). Doctors used repetitions for explanations and affirmations. Patients used repetition for emphasis, verification, and turn-taking. The repetition frequencies of doctors and patients were primarily influenced by personal factors. However, doctor-dimension factors exerted a greater impact on the frequency of patient repetitions.

CONCLUSIONS

The reasons for repetitions in online DPC differ from those offline. Online DPC increases patient initiative and reduces doctor authority. Nevertheless, it could be affected by gender stereotypes generated during offline consultations. Doctors still dominated the conversations but attended sufficiently to the patient discourse. Online DPC is gradually attempting to fulfill the expectations of a new patient-centered healthcare pattern.

PRACTICAL IMPLICATIONS

The findings yield suggestions for healthcare providers and the designers of online healthcare platforms.

Understanding citizen engagement and concerns about the COVID-19 pandemic in China: a thematic analysis of government social media

Purpose

In crises such as the coronavirus disease 2019 (COVID-19) pandemic, governments need to act in time to lead citizens toward rational reactions and disclose information effectively to the public. This paper aims to understand the content published by the government and identify how citizen engagement relates to content type and emotional valence.

Design/methodology/approach

The grounded theory approach was adopted and nine types of content posted by the government were observed. The data were obtained from “People's Daily”, an official Sina Weibo account representing the voice of the Chinese government, from January 3 to June 22 in 2020.

Findings

The analysis shows that information related to emotional support and social mobilization were the most reposted, while those mentioning immoral and illegal incidents were the most liked and commented. Also, it was found that positive posts tend to attract more likes, yet with fewer reposts than neutral posts.

Originality/value

The authors adopted thematic analysis and focused on the impact of post content and valence on user participation behavior. This study expands the existing literature. The government can improve crises management capability by learning about citizen engagement behaviors on social media.

Trends and characteristics of China's medical informatization policy from 1996 to 2020: a bibliometric analysis

Purpose

Qualitative methods are not suitable to process high volumes of policy texts for exploring policy evolution. Therefore, it is hard to use qualitative methods to systematically analyze the characteristics of complex policy networks. So the authors propose a bibliometric research study for exploring policy evolution from time–agency–theme perspectives to excavate the rules and existing problems of China's medical informatization policy and to provide suggestions for formulating and improving the future medical informatization policies.

Design/methodology/approach

Initially, 615 valid samples are obtained by retrieving related China's medical informatization policy documents, and the joint policy-making agency network and the co-occurrence network models of medical informatization policies are defined, and then the authors research China's medical informatization policies from single-dimension and multi-dimension view.

Findings

The analysis results reveal that China's medical informatization policy process can be divided into four stages; the policy-making agencies are divided into four subgroups by community detection analysis according to the fast unfolding algorithm; the core policy theme keywords are identified based on the eigenvector centrality of the nodes in those networks; the focuses of theme terms are varied in different stages and the correlations between agencies and themes are gradually decentralized.

Practical implications

These findings provide experience and evidence on leveraging informatics in the medical and healthcare field of China. Also, they can help scholars and practitioners better understand the current status and future directions of medical and healthcare informatics development in China and provide a reference to formulate and improve China's future medical informatization policies.

Originality/value

This study proposes a quantitative bibliometric-based research framework to describe transitions and trends of China's medical informatization policy.