Evidence map›Paper›PMID 40910059›Full record

ArticleFrontiers in public health2025

Frequent use of online medical records: analysis of influence factors based on structural equation modeling.

Wei Wang, Lei Qin, Yang Chen, Yinzhi Wang, Linglong Ye, Ruojia Wang, Yingqiu Zhu

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

7 authors.

Wei WangSchool of Economics and Management, Guizhou Normal University, Guiyang, China.
Lei QinSchool of Statistics, University of International Business and Economics, Beijing, China.
Yang ChenPetrochina (Beijing) Digital Intelligence Research Institute CO., LTD, Beijing, China.
Yinzhi WangSchool of Finance, Shanghai University of International Business and Economics, Shanghai, China.
Linglong YeSchool of Public Affairs, Xiamen University, Xiamen, China.
Ruojia WangSchool of Management, Beijing University of Traditional Chinese Medicine, Beijing, China.
Yingqiu ZhuSchool of Statistics, University of International Business and Economics, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advent of electronic storage of medical records and the internet has led to an increase in the use of online medical records, thereby enhancing doctor-patient communication and facilitating medical treatment. Based on demographic and personal behavioral characteristics from the National Cancer Institute's 2019-2020 National Trends in Health Information Survey data, this study explored the characteristics and factors influencing the frequent use of online medical records and compared them with those that do not. By combining traditional statistical tests and two machine learning algorithms, eight variables were identified as key variables in the frequent use of online medical records. These variables were then divided into three influencing factors (latent variables). The structural equation model was used to conduct impact path analysis of the three influencing factors and target variables. The three impact factors were (1) Whether to provide online medical records, (2) Degree of concern for health, and (3) Whether to use internet. This paper proposes recommendations based on the three impact factors, thereby promoting the usefulness of medical records in a larger group of people.

Indexed as

Electronic Health RecordsInternetAdultAgedFemaleHumansLatent Class AnalysisMaleMiddle AgedSurveys and QuestionnairesHINTSinfluence factorsonline medical recordspublic healthstructural equation modeling

Identifiers

PMID40910059
PMCPMC12405286

What Socratic holds

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LicenceCC BY
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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 Socratic graph.