Developing Intelligent Auditing Methods to Deal with Up-coding under DRG payment system

China Health Insurance ›› 2022, Vol. 0 ›› Issue (6) : 44-47.

China Health Insurance ›› 2022, Vol. 0 ›› Issue (6) : 44-47. DOI: 10.19546/j.issn.1674-3830.2022.6.009
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Developing Intelligent Auditing Methods to Deal with Up-coding under DRG payment system

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Abstract

“Up-coding” under the diagnosis-related group payment refers to the behavior that providers deliberately and systematically change the reported case mix in order to obtain more medical insurance payment, which brings risks to the security of medical insurance funds. In 2021, the National Healthcare Security Administration proposed to achieve full coverage of regions, medical institutions, disease groups and medical insurance funds based on DRG/DIP payments in the next three years. As the most destructive behavior, up-coding should be the focus of supervision. The traditional manual auditing method is inefficient, and international experience prefers the comprehensive supervision mode of computer-aided intelligent auditing. This paper systematically summarizes the methods and development of dealing with DRG up-coding, and proposes paths and suggestions to develop intelligent audit means in dealing with DRG up-coding in China.

Key words

diagnosis-related group / up-coding / supervision / intelligent audit

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Developing Intelligent Auditing Methods to Deal with Up-coding under DRG payment system[J]. China Health Insurance. 2022, 0(6): 44-47 https://doi.org/10.19546/j.issn.1674-3830.2022.6.009

References

[1] Hsia D C, Krushat W M, Fagan A B, et al.Accuracy of diagnostic coding for Medicare patients under the prospective-payment system[J]. N Engl J Med, 1988, 318(6): 352-5.
[2] Xourafas D, Merath K, Spolverato G, et al.Specific Medicare Severity-Diagnosis Related Group Codes Increase the Predictability of 30-Day Unplanned Hospital Readmission After Pancreaticoduodenectomy[J]. J Gastrointest Surg, 2018, 22(11): 1920-7.
[3] Liang F-W, Wang L-Y, Liu L-Y, et al.Physician code creep after the initiation of outpatient volume control program and implications for appropriate ICD-10-CM coding[J]. BMC Health Services Research, 2020, 20(1): 127.
[4] Steinbusch P J M, Oostenbrink J B, Zuurbier J J, et al. The risk of upcoding in casemix systems: A comparative study[J]. Health Policy, 2007, 81(2): 289-99.
[5] Groß M, Jürges H, Wiesen D.The effects of audits and fines on upcoding in neonatology[J]. Health Econ, 2021
[6] Burger F, Walgenbach M, Gobel P, et al.Is DRG Coding too Important to be Left to Physicians? - Evaluation of Economic Efficiency by Health Economists in a University Medical Centre[J]. Z Orthop Unfall, 2017, 155(2): 177-83.
[7] Hsia D C, Ahern C A, Ritchie B P, et al.Medicare reimbursement accuracy under the prospective payment system, 1985 to 1988[J]. Jama, 1992, 268(7): 896-9.
[8] O'malley K J, Cook K F, Price M D, et al. Measuring Diagnoses: ICD Code Accuracy[J]. Health Services Research, 2005, 40(5p2): 1620-39.
[9] Zhan C, Elixhauser A, Friedman B, et al.Modifying DRG-PPS to Include Only Diagnoses Present on Admission: Financial Implications and Challenges[J]. Medical Care, 2007, 45(4):
[10] Spika S B, Zweifel P.Buying efficiency: optimal hospital payment in the presence of double upcoding[J]. Health Econ Rev, 2019, 9(1): 38.
[11] Hsia D C.Accuracy of Medicare reimbursement for cardiac arrest[J]. Jama, 1990, 264(1): 59-62.
[12] Perez V, Wing C.Should We Do More to Police Medicaid Fraud? Evidence on the Intended and Unintended Consequences of Expanded Enforcement[J]. American Journal of Health Economics, 2019, 5(4): 481-508.
[13] Milcent C.From downcoding to upcoding: DRG based payment in hospitals[J]. International Journal of Health Economics and Management, 2021, 21(1): 1-26.
[14] Cook A, Averett S. Do hospitals respond to changing incentive structures? Evidence from Medicare's2007 DRG restructuring[J]. J Health Econ, 2020, 73(102319.
[15] CMS. The Health Care Fraud and Abuse Control Program Protects Consumers and Taxpayers by Combating Health Care Fraud [EB/OL].(2016-2-26) [2022-05-13] https://www.cms.gov/newsroom/fact-sheets/health-care-fraud-and-abuse-control-program-protects-consumers-and-taxpayers-combating-health-care
[16] Harrington K. Clamping down on upcoding: Government efforts to curb a Medicare billing fraud and abuse [EB/OL].(2003) [2022-05-13] https://digitalcommons.unl.edu/dissertations/AAI3085736
[17] Dua P, Bais S.Supervised Learning Methods for Fraud Detection in Healthcare Insurance[M] DUA S, ACHARYA U R, DUA P. Machine Learning in Healthcare Informatics. Berlin, Heidelberg; Springer Berlin Heidelberg. 2014: 261-85.
[18] Ormerod T, Morley N, Ball L, et al.Using ethnography to design a mass detection tool (MDT) for the early discovery of insurance fraud[M]. CHI '03 Extended Abstracts on Human Factors in Computing Systems. Ft. Lauderdale, Florida, USA; Association for Computing Machinery. 2003: 650-1.
[19] Liou F M, Tang Y C, Chen J Y.Detecting hospital fraud and claim abuse through diabetic outpatient services[J]. Health Care Manag Sci, 2008, 11(4): 353-8.
[20] Kirlidog M, Asuk C. A Fraud Detection Approach with Data Mining in Health Insurance [J]. Procedia - Social and Behavioral Sciences, 2012, 62(989-94.
[21] Hillerman T, Souza J C F, Reis A C B, et al. Applying clustering and AHP methods for evaluating suspect healthcare claims [J]. Journal of Computational Science, 2017, 19(97-111.
[22] Bolton R J, Hand D J.Statistical Fraud Detection: A Review[J]. Statistical Science, 2002, 17(3): 235-55, 21.
[23] Bayerstadler A, Van Dijk L, Winter F. Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance [J]. Insurance: Mathematics and Economics, 2016, 71(244-52.
[24] Rosenberg M A, Fryback D G, Katz D A.A Statistical Model to Detect DRG Upcoding[J]. Health Services and Outcomes Research Methodology, 2000, 1(3): 233-52.
[25] Massi M C, Ieva F, Lettieri E.Data mining application to healthcare fraud detection: a two-step unsupervised clustering method for outlier detection with administrative databases[J]. BMC Med Inform Decis Mak, 2020, 20(1): 160.
[26] Feng Y, Lin S, Lin E-J, et al.Identifying Candidates for Medical Coding Audits: Demonstration of a Data Driven Approach to Improve Medicare Severity Diagnosis-Related Group Coding Compliance; proceedings of the Health Information Science, Cham, F 2019, 2019 [C]. Springer International Publishing.

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