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在线病历的频繁使用:基于结构方程模型的影响因素分析

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

作者信息

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

机构信息

School of Economics and Management, Guizhou Normal University, Guiyang, China.

School of Statistics, University of International Business and Economics, Beijing, China.

出版信息

Front Public Health. 2025 Aug 20;13:1609503. doi: 10.3389/fpubh.2025.1609503. eCollection 2025.

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.

摘要

电子病历存储和互联网的出现使得在线病历的使用增加,从而加强了医患沟通并促进了医疗治疗。基于美国国家癌症研究所2019 - 2020年健康信息调查数据中的人口统计学和个人行为特征,本研究探讨了影响在线病历频繁使用的特征和因素,并将其与不频繁使用的情况进行比较。通过结合传统统计测试和两种机器学习算法,确定了八个变量为在线病历频繁使用的关键变量。这些变量随后被分为三个影响因素(潜在变量)。使用结构方程模型对这三个影响因素和目标变量进行影响路径分析。这三个影响因素分别为:(1)是否提供在线病历,(2)对健康的关注程度,以及(3)是否使用互联网。本文基于这三个影响因素提出建议,从而在更大人群中提升病历的实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d632/12405286/c2ec05679406/fpubh-13-1609503-g001.jpg

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