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How should regulatory schemes be optimized to enhance deterrence against medical insurance fraud by enrollees? Evidence from a discrete choice experiment in China

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Listed:
  • Zhang, Jinsui
  • Hu, Min
  • Jia, Yusheng
  • Gu, Yuanyuan
  • Chen, Wen

Abstract

Medical insurance fraud (MIF) poses a substantial global financial challenge, necessitating effective regulatory strategies, especially in China, where such measures are in a critical developmental phase. This study investigates the effectiveness of various regulatory components in deterring MIF among enrollees and explores preference heterogeneity among individuals with different characteristics, utilizing a discrete choice experiment survey. Grounded in deterrence theory, our conceptual framework incorporates five attributes: intensity of economic penalties, restrictions on medical insurance benefits, deterioration of social reputation, and certainty and celerity of penalties. Employing a D-efficiency design, 24 choice sets were generated and blocked into three versions. A multistage stratified sampling method was adopted to collect data from the basic medical insurance enrollees in Shanghai. The survey was conducted from September to October 2022. The sample representativeness was further improved via the entropy balancing approach. Data from the final sample of 1034 respondents were analyzed using mixed logit models (MIXLs), incorporating interactions with individual characteristics to assess preference heterogeneity. Results reveal that escalating economic penalties, suspending insurance benefits, listing individuals as unfaithful parties, ensuring penalty certainty, and expediting enforcement significantly enhance the deterrent effect. We observed preference heterogeneity across different demographics, including age, gender, education, health status, and employment status. The study underscores the pivotal role of economic penalties in deterring MIF, while also acknowledging the significance of non-economic measures such as enforcement efficiency and social sanctions. These findings offer valuable insights for policymakers to tailor and strengthen regulatory schemes against MIF, contributing to the advancement of more effective and precise healthcare policies.

Suggested Citation

  • Zhang, Jinsui & Hu, Min & Jia, Yusheng & Gu, Yuanyuan & Chen, Wen, 2024. "How should regulatory schemes be optimized to enhance deterrence against medical insurance fraud by enrollees? Evidence from a discrete choice experiment in China," Social Science & Medicine, Elsevier, vol. 354(C).
    • Handle: RePEc:eee:socmed:v:354:y:2024:i:c:s0277953624005124
    DOI: 10.1016/j.socscimed.2024.117059
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    1. R. E. Caves & M. E. Porter, 1977. "From Entry Barriers to Mobility Barriers: Conjectural Decisions and Contrived Deterrence to New Competition," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 91(2), pages 241-261.
    2. Chandoevwit, Worawan & Wasi, Nada, 2020. "Incorporating discrete choice experiments into policy decisions: Case of designing public long-term care insurance," Social Science & Medicine, Elsevier, vol. 258(C).
    3. Lance Lochner & Enrico Moretti, 2004. "The Effect of Education on Crime: Evidence from Prison Inmates, Arrests, and Self-Reports," American Economic Review, American Economic Association, vol. 94(1), pages 155-189, March.
    4. Lancsar, Emily & Gu, Yuanyuan & Gyrd-Hansen, Dorte & Butler, Jim & Ratcliffe, Julie & Bulfone, Liliana & Donaldson, Cam, 2020. "The relative value of different QALY types," Journal of Health Economics, Elsevier, vol. 70(C).
    5. Conghai Zhang & Xinyao Xiao & Chao Wu, 2020. "Medical Fraud and Abuse Detection System Based on Machine Learning," IJERPH, MDPI, vol. 17(19), pages 1-11, October.
    6. Hennink, Monique & Kaiser, Bonnie N., 2022. "Sample sizes for saturation in qualitative research: A systematic review of empirical tests," Social Science & Medicine, Elsevier, vol. 292(C).
    7. Wang, Qun & Abiiro, Gilbert Abotisem & Yang, Jin & Li, Peng & De Allegri, Manuela, 2021. "Preferences for long-term care insurance in China: Results from a discrete choice experiment," Social Science & Medicine, Elsevier, vol. 281(C).
    8. Vikas Soekhai & Esther W. Bekker-Grob & Alan R. Ellis & Caroline M. Vass, 2019. "Discrete Choice Experiments in Health Economics: Past, Present and Future," PharmacoEconomics, Springer, vol. 37(2), pages 201-226, February.
    9. Kabindra Regmi & Dinesh Kaphle & Sabina Timilsina & Nik Annie Afiqah Tuha, 2018. "Application of Discrete-Choice Experiment Methods in Tobacco Control: A Systematic Review," PharmacoEconomics - Open, Springer, vol. 2(1), pages 5-17, March.
    10. Esther W. de Bekker‐Grob & Mandy Ryan & Karen Gerard, 2012. "Discrete choice experiments in health economics: a review of the literature," Health Economics, John Wiley & Sons, Ltd., vol. 21(2), pages 145-172, February.
    11. Entorf, Horst, 2012. "Certainty and Severity of Sanctions in Classical and Behavioral Models of Deterrence: A Survey," IZA Discussion Papers 6516, Institute of Labor Economics (IZA).
    12. Entorf, Horst & Spengler, Hannes, 2000. "Socioeconomic and demographic factors of crime in Germany: Evidence from panel data of the German states," International Review of Law and Economics, Elsevier, vol. 20(1), pages 75-106, March.
    13. Maarse, Hans & Paulus, Aggie & Kuiper, Gerard, 2005. "Supervision in social health insurance: a four country study," Health Policy, Elsevier, vol. 71(3), pages 333-346, March.
    14. Matthew S. Johnson, 2020. "Regulation by Shaming: Deterrence Effects of Publicizing Violations of Workplace Safety and Health Laws," American Economic Review, American Economic Association, vol. 110(6), pages 1866-1904, June.
    15. Isaac Akomea-Frimpong & Charles Andoh & Eric Dei Ofosu-Hene, 2016. "Causes, effects and deterrence of insurance fraud: evidence from Ghana," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 23(4), pages 678-699, October.
    16. Michael Clark & Domino Determann & Stavros Petrou & Domenico Moro & Esther Bekker-Grob, 2014. "Discrete Choice Experiments in Health Economics: A Review of the Literature," PharmacoEconomics, Springer, vol. 32(9), pages 883-902, September.
    17. Renee Flasher & Melvin A. Lamboy-Ruiz, 2019. "Impact of Enforcement on Healthcare Billing Fraud: Evidence from the USA," Journal of Business Ethics, Springer, vol. 157(1), pages 217-229, June.
    18. Chen, Gang & Ratcliffe, Julie & Milte, Rachel & Khadka, Jyoti & Kaambwa, Billingsley, 2021. "Quality of care experience in aged care: An Australia-Wide discrete choice experiment to elicit preference weights," Social Science & Medicine, Elsevier, vol. 289(C).
    19. John D'Arcy & Tejaswini Herath, 2011. "A review and analysis of deterrence theory in the IS security literature: making sense of the disparate findings," European Journal of Information Systems, Taylor & Francis Journals, vol. 20(6), pages 643-658, November.
    20. Simon Trang & Benedikt Brendel, 2019. "A Meta-Analysis of Deterrence Theory in Information Security Policy Compliance Research," Information Systems Frontiers, Springer, vol. 21(6), pages 1265-1284, December.
    21. Daniel S. Nagin, 2013. "Deterrence: A Review of the Evidence by a Criminologist for Economists," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 83-105, May.
    22. Liu, Yun & Kong, Qingxia & de Bekker-Grob, Esther W., 2019. "Public preferences for health care facilities in rural China: A discrete choice experiment," Social Science & Medicine, Elsevier, vol. 237(C), pages 1-1.
    23. John D'Arcy & Anat Hovav & Dennis Galletta, 2009. "User Awareness of Security Countermeasures and Its Impact on Information Systems Misuse: A Deterrence Approach," Information Systems Research, INFORMS, vol. 20(1), pages 79-98, March.
    24. Rowen, Donna & Powell, Philip A. & Hole, Arne Risa & Aragon, Maria-Jose & Castelli, Adriana & Jacobs, Rowena, 2022. "Valuing quality in mental healthcare: A discrete choice experiment eliciting preferences from mental healthcare service users, mental healthcare professionals and the general population," Social Science & Medicine, Elsevier, vol. 301(C).
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