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为何不揭发医疗保险欺诈行为?来自中国江苏省的证据。

Why Not Blow the Whistle on Health Care Insurance Fraud? Evidence from Jiangsu Province, China.

作者信息

Wang Dandan, Zhan Changchun

机构信息

School of Management, Jiangsu University, Zhenjiang, People's Republic of China.

出版信息

Risk Manag Healthc Policy. 2022 Oct 12;15:1897-1915. doi: 10.2147/RMHP.S379300. eCollection 2022.

DOI:10.2147/RMHP.S379300
PMID:36268183
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9577100/
Abstract

PURPOSE

To identify the factors that influence whistleblowing behavior as it relates to health care insurance fraud in Jiangsu Province, China.

METHODS

To construct a factor model and formulate research hypotheses using the Motivation-Opportunity-Ability framework. We designed a questionnaire containing 24 items and distributed it on-site to 2081 respondents in Jiangsu Province, China. Afterward, we applied structural equation modeling to validate the research hypotheses.

RESULTS

Policy awareness negatively contributes to whistleblowing behavior, risk perception does not reduce the incentive to blow the whistle, and an inability to recognize fraud is another critical barrier to converting whistleblowing intentions into behavior.

CONCLUSION

Practices that are likely to promote citizen whistleblowing on insurance fraud may focus on the constraints identified by the comprehensive Motivation-Opportunity-Ability framework.

摘要

目的

确定在中国江苏省与医疗保险欺诈相关的影响举报行为的因素。

方法

使用动机-机会-能力框架构建因素模型并提出研究假设。我们设计了一份包含24个条目的问卷,并在中国江苏省向2081名受访者进行了现场发放。之后,我们应用结构方程模型来验证研究假设。

结果

政策意识对举报行为有负面作用,风险认知并未降低举报的积极性,而无法识别欺诈是将举报意图转化为行为的另一个关键障碍。

结论

可能促进公民举报保险欺诈行为的做法可侧重于综合动机-机会-能力框架所确定的制约因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc62/9577100/2631d471df5f/RMHP-15-1897-g0007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc62/9577100/f91c86c8fcd8/RMHP-15-1897-g0005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc62/9577100/2631d471df5f/RMHP-15-1897-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc62/9577100/31abe9b39ba8/RMHP-15-1897-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc62/9577100/80dba5811cff/RMHP-15-1897-g0002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc62/9577100/f91c86c8fcd8/RMHP-15-1897-g0005.jpg
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Medical Fraud and Abuse Detection System Based on Machine Learning.
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The Public's Self-Avoidance and Other-Reliance in the Reporting of Medical Insurance Fraud: A Cross-Sectional Survey in China.公众在医疗保险欺诈举报中的自我回避与依赖他人:一项中国的横断面调查
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