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. 2024 Feb 12:12:1339177.
doi: 10.3389/fpubh.2024.1339177. eCollection 2024.

Research on the risk governance of fraudulent reimbursement of patient consultation fees

Jiangjie Sun #  1   2   3 , Yue Wang #  2 , Yuqing Zhang  3 , Limin Li  3 , Hui Li  3 , Tong Liu  3 , Liping Zhang  4
Affiliations

Research on the risk governance of fraudulent reimbursement of patient consultation fees

Jiangjie Sun et al. Front Public Health. .

Abstract

Background: The fundamental medical insurance fund, often referred to as the public's "life-saving fund," plays a crucial role in both individual well-being and the pursuit of social justice. Medicare fraudulent claims reduce "life-saving money" to "Tang's monk meat", undermining social justice and affecting social stability.

Methods: We utilized crawler technology to gather textual data from 215 cases involving fraudulent health insurance claims. Simultaneously, statistical data spanning 2018 to 2021 was collected from the official websites of the China Medical Insurance Bureau and Anhui Medical Insurance Bureau. The collected data underwent comprehensive analysis through Excel, SPSS 26.0 and R4.2.1. Differential Auto-Regressive Moving Average Model (ARIMA (p, d, q)) was used to fit the fund safety forecast model, and test the predictive validity of the forecast model on the fund security data from July 2021 to October 2023 (the fund security data of Anhui Province from September 2021 to October 2023).

Results: The outcomes revealed that fraudulent claims by health insurance stakeholders adversely impact the equity of health insurance funds. Furthermore, the risk management practices of Medicare fund administrators influence the publication of fraudulent claims cases. Notably, differences among Medicare stakeholders were observed in the prevalence of fraudulent claims. Additionally, effective governance of fraudulent claims risks was found to have a positive impact on the overall health of healthcare funds. Moreover, the predictive validity of the forecast model on the national and Anhui province's fund security data was 92.86% and 100% respectively.

Conclusion: We propose four recommendations for the governance of health insurance fraudulent claims risk behaviors. These recommendations include strategies such as "combatting health insurance fraudulent claims to preserve the fairness of health insurance funds", "introducing initiatives for fraud risk governance and strengthening awareness of the rule of law", "focusing on designated medical institutions and establishing a robust long-term regulatory system", and "adapting to contemporary needs while maintaining a focus on long-term regulation".

Keywords: auto-regressive moving average model; behavior; fraudulent claims; health insurance; risk governance.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Concept model. SHIB: Stakeholders of health insurance benefits.
Figure 2
Figure 2
Trends in the number of people purchasing health insurance (Ten thousand).
Figure 3
Figure 3
State spending on maternity insurance benefits (¥:billion).
Figure 4
Figure 4
status of insurance fraud. DMI, Designated medical institutions; DP, Designated pharmacies; MIGA, Medical insurance government authorities; MICP, Medical Insurance Covered Persons.
Figure 5
Figure 5
Current status of national basic medical insurance income and expenditure.
Figure 6
Figure 6
Current status of income and expenditure of basic medical insurance in Anhui Province.
Figure 7
Figure 7
Effectiveness of official fraud and fraudulent insurance regulation. NFIC, Number of fraudulent insurance cases; ABMIF, The amount of the balance of the Medical Insurance Fund (Ten billion).
Figure 8
Figure 8
Forecasts from ARIMA (1, 1, 0) of China. The light blue area is the 95% confidence interval of the safety forecast line, the gray area is the 85% confidence interval of the safety forecast line.
Figure 9
Figure 9
Forecasts from ARIMA (1, 1, 0) of Anhui. The light blue area is the 95% confidence interval of the safety forecast line, the gray area is the 85% confidence interval of the safety forecast line.
Figure 10
Figure 10
Verification of model predictive validity of China.
Figure 11
Figure 11
Verification of model predictive validity of Anhui.
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Publication types

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported in part by the National Natural Science Foundation of China (No.72374005), the NSF Center for Basic Science Project (No.72188101), the Natural Science Foundation for the Higher Education Institutions of Anhui Province of China (No. 2023AH050561 and No.2022AH051143), Cultivation Programme for Young and Middle-aged Excellent Teachers in Anhui Province (YQZD2023021), School-level offline courses (No.2021xjkc13), and the “Double Ten Thousand Plan” construction project (Research on the teaching methods and cross topic mining of Medical Advanced Mathematics & Health Care Management).