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. 2023 Dec 22:16:2869-2881.
doi: 10.2147/RMHP.S438854. eCollection 2023.

The Public's Self-Avoidance and Other-Reliance in the Reporting of Medical Insurance Fraud: A Cross-Sectional Survey in China

Affiliations

The Public's Self-Avoidance and Other-Reliance in the Reporting of Medical Insurance Fraud: A Cross-Sectional Survey in China

Jinpeng Xu et al. Risk Manag Healthc Policy. .

Abstract

Purpose: To understand the public's self-willingness to report medical insurance fraud and their expectations on others, to provide a reference for the government to do a good job in medical insurance anti-fraud.

Methods: Data were obtained from a questionnaire survey of 846 respondents in China. Descriptive statistical analyses and multinomial logistic regression were used to analyze the different subjective attitudes of the public toward different subjects when faced with medical insurance fraud and the influencing factors.

Results: 511 (60.40%) respondents were willing to report medical insurance fraud, while 739 (87.35%) respondents expected others to report it. 485 (57.33%) respondents were willing and expected others to report medical insurance fraud, followed by those who were not willing but expected others to report it (254, 30.02%). Compared to those who were unwilling to report themselves and did not want others to report, those who believe their reporting is useless (OR=3.13, 95% CI=1.15-8.33) and those who fear for their safety after reporting (OR=2.96, 95% CI=1.66-5.26) were more likely to expect others to report. Self-reporting willingness was stronger among the public who were satisfied with the government's protective measures for the safety of whistleblowers (OR=4.43, 95% CI=1.38-14.17). The public who believe that both themselves and others have responsibilities to report medical insurance fraud were willing to report and expect others to do the same.

Conclusion: The public had a "self-avoidance" and "other-reliance" mentality in medical insurance anti-fraud. The free-rider mentality, lack of empathy, concerns about own risk after reporting, and the interference of decentralized responsibility were important factors contributing to this public mentality. At this stage, the government should prevent the public's "collective indifference" in medical insurance anti-fraud efforts. Improving the safety and protection of whistleblowers and making everyone feel more responsible and valued may be effective incentives to enhance the public's willingness to report.

Keywords: anti-fraud; medical insurance; subjective attitudes; whistleblower.

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Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Overview of the KAP-RC framework.
Figure 2
Figure 2
The flowchart of the research sample.
Figure 3
Figure 3
Subjective attitudes of the public toward different reporting subjects.

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References

    1. Kang H, Hong J, Lee K, Kim S. The effects of the fraud and abuse enforcement program under the National Health Insurance program in Korea. Health Policy. 2010;95(1):41–49. doi:10.1016/j.healthpol.2009.10.003 - DOI - PubMed
    1. Shrank WH, Rogstad TL, Parekh N. Waste in the US Health Care System: estimated Costs and Potential for Savings. JAMA J Am Med Assoc. 2019;322(15):1501–1509. doi:10.1001/jama.2019.13978 - DOI - PubMed
    1. Villegas-Ortega J, Bellido-Boza L, Mauricio D. Fourteen years of manifestations and factors of health insurance fraud, 2006–2020: a scoping review. Health Justice. 2021;9(1):26. doi:10.1186/s40352-021-00149-3 - DOI - PMC - PubMed
    1. Kose I, Gokturk M, Kilic K. An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance. Appl. Soft Comput. 2015;36:283–299. doi:10.1016/j.asoc.2015.07.018 - DOI
    1. Perez V, Wing C. Should We Do More To Police Medicaid Fraud? Evidence on the Intended and Unintended Consequences of Expanded Enforcement. Am J Health Economics. 2019;5:1–55. doi:10.1162/ajhe_a_00130 - DOI