Reimbursement Fraud di Era Digital: Tinjauan Sistematis Skema, Determinan, dan Kontrol Berbasis Risiko
Abstract
This paper synthesizes contemporary evidence on reimbursement fraud across healthcare and adjacent claim-based systems, addressing four questions on dominant schemes, multi-level drivers, control effectiveness, and the digital evolution of modus operandi. Reimbursement fraud persists amid expanding automation, creating financial leakage, operational inefficiency, and credibility risks for payers and providers. The study’s novelty lies in integrating behavioral, organizational, and institutional lenses with a process-stage mapping that aligns “scheme × claim-stage × control,” while proposing a minimum reporting set for evaluation metrics beyond raw accuracy. A PRISMA-guided systematic review of Scopus-indexed, peer-reviewed articles (2016—2025) identifies thirteen studies and codes schemes, stages, determinants, interventions, and outcomes for narrative synthesis. Findings indicate recurrent upcoding, phantom billing, unbundling, duplicate claims, and cost inflation, concentrated at adjudication and post-payment review when verification is fragmented. Risk-based pre-authorization, targeted verification, and post-payment audits work best within interoperable data governance, complemented by ML/AI analytics, document forensics, and, where appropriate, blockchain for audit integrity. Digitalization scales fraudulent attempts, requiring continuous monitoring, model refresh, and shadow testing to manage drift and adaptive behavior. The main implication is a shift from tool-centric fixes to adaptive, risk-based systems of control that report accuracy, detection latency, false-positive burden, and financial recovery for policy-relevant decisions.
References
Aikins, M., Tabong, P. T.-N., Salari, P., Tediosi, F., Asenso-Boadi, F. M., & Akweongo, P. (2021). Positioning the National Health Insurance for Financial Sustainability and Universal Health Coverage in Ghana: A Qualitative Study Among Key Stakeholders. PLoS ONE, 16(6 June). https://doi.org/10.1371/journal.pone.0253109
Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Amoli, M. M., Cunningham, P. J., Cassel, B., Carroll, N. W., & Dahman, B. (2025). MIPS Under Scrutiny: Exploring the Association Between Providers with Fraudulent Practices and Quality Metrics within MACRA’s Framework. Journal of Evaluation in Clinical Practice, 31(5). https://doi.org/10.1111/jep.70217
Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a Curated, High-Quality Bibliometric Data Source for Academic Research in Quantitative Science Studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019
Bandura, A. (1977). Self-Efficacy: Toward a Unifying Theory of Behavioral Change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
Bandura, A. (1999). Moral Disengagement in the Perpetration of Inhumanities. Personality and Social Psychology Review, 3(3), 193–209. https://doi.org/10.1207/s15327957pspr0303_3
Bandura, A. (2002). Selective Moral Disengagement in the Exercise of Moral Agency. Journal of Moral Education, 31(2), 101–119. https://doi.org/10.1080/0305724022014322
Bandura, A., Barbaranelli, C., Caprara, G. V, & Pastorelli, C. (1996). Mechanisms of Moral Disengagement in the Exercise of Moral Agency. Journal of Personality and Social Psychology, 71(2), 364–374. https://doi.org/10.1037/0022-3514.71.2.364
Bauder, R. A., & Khoshgoftaar, T. M. (2017). Multivariate Outlier Detection in Medicare Claims Payments Applying Probabilistic Programming Methods. Health Services and Outcomes Research Methodology, 17(3–4), 256–289. https://doi.org/10.1007/s10742-017-0172-1
Bazyar, M., Yazdi-Feyzabadi, V., Rahimi, N., & Rashidian, A. (2020). What Are the Potential Advantages and Disadvantages of Merging Health Insurance Funds? A Qualitative Policy Analysis from Iran. BMC Public Health, 20(1). https://doi.org/10.1186/s12889-020-09417-7
Chandawarkar, R., Nadkarni, P., Barmash, E., Thomas, S., Capek, A., Casey, K., & Carradero, F. (2024). Revenue Cycle Management: The Art and the Science. Plastic and Reconstructive Surgery - Global Open, 12(7), e5756. https://doi.org/10.1097/GOX.0000000000005756
Chen, A., Blumenthal, D. M., & Jena, A. B. (2018). Characteristics of Physicians Excluded from US Medicare and State Public Insurance Programs for Fraud, Health Crimes, or Unlawful Prescribing of Controlled Substances. JAMA Network Open, 1(8). https://doi.org/10.1001/jamanetworkopen.2018.5805
Davies, M. R., Monssen, D., Sharpe, H., Allen, K. L., Simms, B., Goldsmith, K. A., Byford, S., Lawrence, V., & Schmidt, U. (2024). Management of Fraudulent Participants in Online Research: Practical Recommendations from a Randomized Controlled Feasibility Trial. International Journal of Eating Disorders, 57(6), 1311–1321. https://doi.org/10.1002/eat.24085
Dorminey, J., Scott Fleming, A., Kranacher, M. J., & Riley, R. A. (2012). The Evolution of Fraud Theory. Issues in Accounting Education, 27(2), 555–579. https://doi.org/10.2308/iace-50131
Drabiak, K., & Wolfson, J. (2020). Policy Forum What Should Health Care Organizations Do to Reduce Billing Fraud and Abuse? AMA Journal of Ethics, 22(3), E221–E231. https://doi.org/10.1001/AMAJETHICS.2020.221
Eddy, C. A., Liberman, A., & Falen, T. (2000). Medical Care Program Compliance—A Year 2000 Recipient Perspective of Medicare Claims. Health Care Manager, 19(2), 22–37. https://doi.org/10.1097/00126450-200019020-00006
Faunce, T. A., Urbas, G., & Skillen, L. (2011). Implementing US-Style Anti-Fraud Laws in the Australian Pharmaceutical and Health Care Industries. Medical Journal of Australia, 194(9), 474–478. https://doi.org/10.5694/j.1326-5377.2011.tb03066.x
Faux, M. A., Wardle, J. L., & Adams, J. (2015). No Payments, Copayments and Faux Payments: Are Medical Practitioners Adequately Equipped to Manage Medicare Claiming and Compliance? Internal Medicine Journal, 45(2), 221–227. https://doi.org/10.1111/imj.12665
Faux, M., Adams, J., Dahiya, S., & Wardle, J. (2022). Wading Through Molasses: A Qualitative Examination of the Experiences, Perceptions, Attitudes, and Knowledge of Australian Medical Practitioners Regarding Medical Billing. PLoS ONE, 17(1 January). https://doi.org/10.1371/journal.pone.0262211
Fei, Y., Fu, Y., Yang, D.-X., & Hu, C.-H. (2022). Research on the Formation Mechanism of Health Insurance Fraud in China: From the Perspective of the Tripartite Evolutionary Game. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.930120
Finlayson, S. G., Bowers, J. D., Ito, J., Zittrain, J. L., Beam, A. L., & Kohane, I. S. (2019). Adversarial Attacks on Medical Machine Learning. Science, 363(6433), 1287–1289. https://doi.org/10.1126/science.aaw4399
Gaitonde, R., Oxman, A. D., Okebukola, P. O., & Rada, G. (2016). Interventions to Reduce Corruption in the Health Sector. Cochrane Database of Systematic Reviews, 2016(8). https://doi.org/10.1002/14651858.CD008856.pub2
Gill, A. S., Menjivar, D., Shipman, P., Sumsion, J., Error, M., & Alt, J. A. (2023). Healthcare Provider Feedback Improves Outpatient E/M Billing and Coding in Otolaryngology Clinics. OTO Open, 7(1). https://doi.org/10.1002/oto2.20
Golden, J. A. (2017). Deep Learning Algorithms for Detection of Lymph Node Metastases from Breast Cancer Helping Artificial Intelligence be Seen. JAMA, 318(22), 2184–2186. https://doi.org/10.1001/jama.2017.14580
Heath, J. (2020). Ethical Issues in Physician Billing under Fee-For-Service Plans. The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine, 45(1), 86–104. https://doi.org/10.1093/jmp/jhz029
Hennig-Schmidt, H., Jürges, H., & Wiesen, D. (2018). Dishonesty in Health Care Practice: A Behavioral Experiment on Upcoding in Neonatology. Health Economics (United Kingdom), 1–20. https://doi.org/10.1002/hec.3842
Hoelscher, J., & Shonhiwa, T. (2023). J&S Publisher Problems: A Diagnostic Analytics Case Exploring Employee Expense Reimbursement. Journal of Emerging Technologies in Accounting, 20(1), 213–221. https://doi.org/10.2308/JETA-2021-011
Kaddi, S. S., & Patil, M. M. (2023). Ensemble Learning Based Health Care Claim Fraud Detection in an Imbalance Data Environment. Indonesian Journal of Electrical Engineering and Computer Science, 32(3), 1686–1694. https://doi.org/10.11591/ijeecs.v32.i3.pp1686-1694
Kronfol, N., Khalife, J., Romanos, J., Makouk, J., Noun, P., & Ammar, W. (2014). Observations on the Ministry of Public Health Program of Support to the Hospitalization of Patients in Lebanon. Journal Medical Libanais, 62(1), 33–39. https://doi.org/10.12816/0002625
Kuunibe, N., Lohmann, J., Schleicher, M., Koulidiati, J.-L., Robyn, P. J., Zigani, Z., Sanon, A., & De Allegri, M. (2019). Factors Associated with Misreporting in Performance-Based Financing in Burkina Faso: Implications for Risk-Based Verification. International Journal of Health Planning and Management, 34(4), 1217–1237. https://doi.org/10.1002/hpm.2786
Lorence, D. P., & Richards, M. (2002). Variation in Coding Influence Across the USA. Risk and Reward in Reimbursement Optimization. Journal of Management in Medicine, 16(6), 422–435. https://doi.org/10.1108/02689230210450981
Lorence, D. P., & Spink, A. (2002). Regional variation In Medical Systems Data: Influences on Upcoding. Journal of Medical Systems, 26(5), 369–381. https://doi.org/10.1023/A:1016405214914
Lyles, M. A. (2013). Teaching Physicians about Fraud and Program Integrity. Academic Medicine, 88(8), 1061–1063. https://doi.org/10.1097/ACM.0b013e318299efc1
Mackey, T. K., Miyachi, K., Fung, D., Qian, S., & Short, J. (2020). Combating Health Care Fraud and Abuse: Conceptualization and Prototyping Study of a Blockchain Antifraud Framework. Journal of Medical Internet Research, 22(9). https://doi.org/10.2196/18623
Matloob, I., Khan, S., Rahman, H. ur, & Hussain, F. (2020). Medical Health Benefit Management System for Real-Time Notification of Fraud Using Historical Medical Records. Applied Sciences (Switzerland), 10(15). https://doi.org/10.3390/app10155144
McGee, J. A., & Byington, J. R. (2008). Beware Expense Reimbursement Schemes! Journal of Corporate Accounting and Finance, 20(1), 9–13. https://doi.org/10.1002/jcaf.20444
Naib, P., Gupta, S. D., & Chandrashekar, S. (2025). Study of Provider Perceptions of Types and Extent of Fraud in Health Insurance and Factors Influencing Ethics in Healthcare Provisioning: Learnings from the World’s Largest Public Health Insurance Scheme in India. Journal of Health Management, 27(1), 86–92. https://doi.org/10.1177/09720634241304983
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. In The BMJ (Vol. 372). BMJ Publishing Group. https://doi.org/10.1136/bmj.n71
Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., Mcdonald, S., … Mckenzie, J. E. (2021). PRISMA 2020 Explanation and Elaboration: Updated Guidance and Exemplars for Reporting Systematic Reviews. In The BMJ (Vol. 372). BMJ Publishing Group. https://doi.org/10.1136/bmj.n160
Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World. In Publications (Vol. 9, Issue 1). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/publications9010012
Roffia, P., & Poffo, M. (2025). Revisiting the Fraud Triangle in Corporate Frauds: Towards a Polygon of Elements. Journal of Risk and Financial Management, 18(3). https://doi.org/10.3390/jrfm18030156
Ruhnka, J. C., Gac, E. J., & Boerstler, H. (2000). Qui Tam Claims: Threat to Voluntary Compliance Programs in Health Care Organizations. Journal of Health Politics, Policy and Law, 25(2), 283–308. https://doi.org/10.1215/03616878-25-2-283
Sanghavi, P., Jena, A. B., Newhouse, J. P., & Zaslavsky, A. M. (2021). Identifying Outlier Patterns of Inconsistent Ambulance Billing in Medicare. Health Services Research, 56(2), 188–192. https://doi.org/10.1111/1475-6773.13622
Satou, G. M., Rheuban, K., Alverson, D., Lewin, M., Mahnke, C., Marcin, J., Martin, G. R., Mazur, L. S., Sahn, D. J., Shah, S., Tuckson, R., Webb, C. L., & Sable, C. A. (2017). Telemedicine in Pediatric Cardiology: A Scientific Statement from the American Heart Association. Circulation, 135(11), e648–e678. https://doi.org/10.1161/CIR.0000000000000478
Schofield, M. (2021). Regulatory and Legislative Issues on Telehealth. Nutrition in Clinical Practice, 36(4), 729–738. https://doi.org/10.1002/ncp.10740
Schonberger, R. B., Dutton, R. P., & Dai, F. (2016). Is There Evidence for Systematic Upcoding of ASA Physical Status Coincident with Payer Incentives? A Regression Discontinuity Analysis of the National Anesthesia Clinical Outcomes Registry. Anesthesia and Analgesia, 122(1), 243–250. https://doi.org/10.1213/ANE.0000000000000917
Sommersguter-Reichmann, M., Wild, C., Stepan, A., Reichmann, G., & Fried, A. (2018). Individual and Institutional Corruption in European and US Healthcare: Overview and Link of Various Corruption Typologies. Applied Health Economics and Health Policy, 16(3), 289–302. https://doi.org/10.1007/s40258-018-0386-6
Soneji, P. T. (2022). The Fraud Theories: Triangle, Diamond, Pentagon. International Journal of Accounting, Auditing and Performance Evaluation, 18(1), 49–60. https://doi.org/10.1504/IJAAPE.2022.123301
Studdert, D. M., Mello, M. M., & Brennan, T. A. (2004). Financial Conflicts of Interest in Physicians’ Relationships with the Pharmaceutical Industry - Self-Regulation in the Shadow of Federal Prosecution. New England Journal of Medicine, 351(18), 1891–1900. https://doi.org/10.1056/NEJMlim042229
Sun, J., Wang, Y., Zhang, Y., Li, L., Li, H., Liu, T., & Zhang, L. (2024). Research on the Risk Governance of Fraudulent Reimbursement of Patient Consultation Fees. Frontiers in Public Health, 12. https://doi.org/10.3389/fpubh.2024.1339177
Tan, K.-L., Hii, I. S. H., Huang, Y., & Yan, Y. (2024). Reducing Dishonest Disclosures during Expense Reimbursement: Investigating the Predictive Power of the Technology Acceptance Model with a Corporate Governance Perspective. Journal of Accounting and Organizational Change, 20(5), 874–895. https://doi.org/10.1108/JAOC-01-2023-0019
Vassiliou, A. G., Georgakopoulou, C., Papageorgiou, A., Georgakopoulos, S., Goulas, S., Paschalis, T., Paterakis, P., Gallos, P., Kyriazis, D., & Plagianakos, V. (2020). Health in All Policy Making Utilizing Big Data. Acta Informatica Medica, 28(1), 65–70. https://doi.org/10.5455/AIM.2020.28.65-70
Villegas-Ortega, J., Bellido-Boza, L., & Mauricio, D. (2021). Fourteen Years of Manifestations and Factors of Health Insurance Fraud, 2006–2020: A Scoping Review. Health and Justice, 9(1). https://doi.org/10.1186/s40352-021-00149-3
Vyas, K. S., Snapp, W. K., Rinker, B. D., & Vásconez, H. C. (2016). A Critical Analysis of the 2012 Centers for Medicare and Medicaid Services Provider Use and Payment Database. Plastic and Reconstructive Surgery, 137(2), 462–473. https://doi.org/10.1097/01.prs.0000475779.30536.1b
Wynia, M. K., Cummins, D. S., VanGeest, J. B., & Wilson, I. B. (2000). Physician Manipulation of Reimbursement Rules for Patients: Between A Rock and a Hard Place. JAMA, 283(14), 1858–1865. https://doi.org/10.1001/jama.283.14.1858
Zerzan, J., Stearns, S., & Hanson, L. (2000). Access to Palliative Care and Hospice in Nursing Homes. JAMA, 284(19), 2489–2494. https://doi.org/10.1001/jama.284.19.2489
Zhang, G., Zhang, X., Bilal, M., Dou, W., Xu, X., & Rodrigues, J. J. P. C. (2022). Identifying Fraud in Medical Insurance Based on Blockchain and Deep Learning. Future Generation Computer Systems, 130, 140–154. https://doi.org/10.1016/j.future.2021.12.006
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