Evidence map›Paper›PMID 40520139›Full record

ArticleDigital health

Content analysis and optimization suggestions for China's big data on healthcare policy from the perspective of policy tools.

Quansheng Wang, Guoqing Han, Lansong Huang

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Quansheng WangLaw School, Shandong University, Weihai, China.ORCID https://orcid.org/0000-0002-4676-9478
Guoqing HanLaw School, Shandong University, Weihai, China.ORCID https://orcid.org/0000-0003-4661-9282
Lansong HuangLaw School, Shandong University, Weihai, China.ORCID https://orcid.org/0000-0001-8853-3917

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To understand the internal and external characteristics of the big data policy of healthcare in China. This paper discusses the focus and shortcomings of policy objectives and related policies. This study provides a reference for optimizing China's big data-related healthcare policies. Methods: In this study, NVivo 12 was used for qualitative and quantitative analyses of the combination of the software policy. The policy documents included in the study were coded, and the statistical description method was used to analyze the relevant policy tools. Results: Among the basic types of policy instruments, supply-oriented, environmental, and demand-oriented policy instruments accounted for 58.9%, 24.2%, and 16.9%, respectively. The use of specific tools for different types of policy is also very uneven. Conclusions: The policy is dominated by the government; its use is insufficient; some are neglected, and the system is quite different. It is suggested to improve the precision of policy targeting, optimize the internal structure of policies, actively cultivate market players, and enhance the use of information technology.

Indexed as

Healthcare big data policypolicy goalspolicy textpolicy toolsstatistical description method

Identifiers

PMID40520139
PMCPMC12163259

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

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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.