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

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.

Authors

Institutions

Publication Details

Journal
Frontiers in Public Health
Published
2025-08-20
DOI
https://doi.org/10.3389/fpubh.2025.1609503
Primary Topic
Mobile Health and mHealth Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Yinzhi Wang, Yingqiu Zhu, Lei Qin, Ruojia Wang et al.
Frontiers in Public Health
Mobile Health and mHealth Applications
article

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

Yinzhi Wang, Yingqiu Zhu, Lei Qin, Ruojia Wang, Linglong Ye, Yang Chen, Weimin Wang
article en

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.

Frontiers in Public HealthVol. 13
Beijing Institute of Petrochemical Technology (CN), University of International Business and Economics (CN), Guizhou Normal University (CN), Beijing University of Chinese Medicine (CN), Xiamen University (CN), Wuhan University (CN), Wuhan Donghu University (CN), Beijing Hospital of Traditional Chinese Medicine (CN), Shanghai University of International Business and Economics (CN)
National Office for Philosophy and Social Sciences
Openalex Percentile: Top 66%
Mobile Health and mHealth Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.