참고문헌
- Ministry of Helath and Welfare. Korean national health accounts and total health expenditure in 2013. Sejong: Ministry of Helath and Welfare; 2015.
- Hyun KR, Kang S, Lee S. Population aging and healthcare expenditure in Korea. Health Econ 2016;25(10):1239-1251. DOI: https://doi.org/10.1002/hec.3209.
- Chandra A, Skinner JS. Technology growth and expenditure growth in health care. Cambridge (MA): National Bureau of Economic Research; 2011. DOI: https://doi.org/10.3386/w16953.
- Kwon HY, Hong JM, Godman B, Yang BM. Price cuts and drug spending in South Korea: the case of antihyperlipidemic agents. Health Policy 2013;112(3):217-226. DOI: https://doi.org/10.1016/j.healthpol.2013.08.011.
- Morris L. Combating fraud in health care: an essential component of any cost containment strategy. Health Aff (Millwood) 2009;28(5):1351-1356. DOI: https://doi.org/10.1377/hlthaff.28.5.1351.
- Hyman DA. Health care fraud and abuse: market change, social norms, and the trust "reposed on the workmen". J Legal Stud 2001;30(2):531-567. DOI: https://doi.org/10.1086/324674.
- Yang WS, Hwang SY. A process-mining framework for the detection of healthcare fraud and abuse. Expert Syst Appl 2006;31(1):56-68. DOI: https://doi.org/10.1016/j.eswa.2005.09.003.
- Schiller J. The impact of insurance fraud detection systems. J Risk Insur 2006;73(3):421-438. DOI: https://doi.org/10.1111/j.1539-6975.2006.00182.x.
- Kim H, Jean Kwon W. A multi-line insurance fraud recognition system: a government-led approach in Korea. Risk Manag Insur Rev 2006;9(2):131-147. DOI: https://doi.org/10.1111/j.1540-6296.2006.00090.x.
- Chan PK, Stolfo SJ. Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection [Internet]. Palo Alto (CA): Association for the Advancement of Artificial Intelligence; 1998 [cited 2017 Dec 20]. Available from: http://www.aaai.org/Papers/KDD/1998/KDD98-026.pdf.
- Rezaee Z. Financial statement fraud: prevention and detection. New York (NY): John Wiley & Sons; 2002.
- Bierstaker JL, Brody RG, Pacini C. Accountants' perceptions regarding fraud detection and prevention methods. Manag Audit J 2006;21(5):520-535. DOI: https://doi.org/10.1108/02686900610667283.
- Shin YS, Min IS, Nam KJ, Jeong JY. Performance evaluation and improvement of fraud investigation system operation. Sejong: Korea Institute for Health and Social Affairs; 2011.
- Fawcett T, Provost F. Adaptive fraud detection. Data Min Knowl Discov 1997;1(3):291-316. https://doi.org/10.1023/A:1009700419189
- Bolton RJ, Hand DJ. Statistical fraud detection: a review. Stat Sci 2002;17(3):235-249. https://doi.org/10.1214/ss/1042727940
- Provost F, Fawcett T. Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions [Internet]. Palo Alto (CA): Association for the Advancement of Artificial Intelligence; 1997 [cited 2017 Dec 20]. Available from: http://www.aaai.org/Papers/KDD/1997/KDD97-007.pdf.
- Kim KJ, Burns LR. Success factors in hospital network performance: evidence from Korea. Health Serv Manage Res 2007;20(3):141-152. DOI: https://doi.org/10.1258/095148407781395946.
- Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics 1988;44(4):1049-1060. DOI: https://doi.org/10.2307/2531734.
- Hanley JA, Negassa A, Edwardes MD, Forrester JE. Statistical analysis of correlated data using generalized estimating equations: an orientation. Am J Epidemiol 2003;157(4):364-375. DOI: https://doi.org/10.1093/aje/kwf215.
- Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther 2002;27(4):299-309. DOI: https://doi.org/10.1046/j.1365-2710.2002.00430.x.
- Zhang F, Wagner AK, Soumerai SB, Ross-Degnan D. Methods for estimating confidence intervals in interrupted time series analyses of health interventions. J Clin Epidemiol 2009;62(2):143-148. DOI: https://doi.org/10.1016/j.jclinepi.2008.08.007.
- Feng C, Wang H, Lu N, Tu XM. Log transformation: application and interpretation in biomedical research. Stat Med 2013;32(2):230-239. DOI: https://doi.org/10.1002/sim.5486.
- Basu A, Manning WG, Mullahy J. Comparing alternative models: log vs Cox proportional hazard? Health Econ 2004;13(8):749-765. DOI: https://doi.org/10.1002/hec.852.
- Faddy M, Graves N, Pettitt A. Modeling length of stay in hospital and other right skewed data: comparison of phase-type, gamma and log-normal distributions. Value Health 2009;12(2):309-314. DOI: https://doi.org/10.1111/j.1524-4733.2008.00421.x.
- Malehi AS, Pourmotahari F, Angali KA. Statistical models for the analysis of skewed healthcare cost data: a simulation study. Health Econ Rev 2015;5:11. DOI: https://doi.org/10.1186/s13561-015-0045-7.
- You BN, Park JM. The processes and factors of innovation stretegy for Korean hospitals. J Korean Acad Assoc Bus Adm 2007;20(1):373-395.
- Knapp CA, Knapp MC. The effects of experience and explicit fraud risk assessment in detecting fraud with analytical procedures. Account Organ Soc 2001;26(1):25-37. DOI: https://doi.org/10.1016/s0361-3682(00)00005-2.
- Ngai EW, Hu Y, Wong YH, Chen Y, Sun X. The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis Support Syst 2011;50(3):559-569. DOI: https://doi.org/10.1016/j.dss.2010.08.006.
- Ravisankar P, Ravi V, Rao GR, Bose I. Detection of financial statement fraud and feature selection using data mining techniques. Decis Support Syst 2011;50(2):491-500. DOI: https://doi.org/10.1016/j.dss.2010.11.006.
- Thorpe KE. Why are urban hospital costs so high?: the relative importance of patient source of admission, teaching, competition, and case mix. Health Serv Res 1988;22(6):821-836.
- Steinwald B, Dummit LA. Hospital case-mix change: sicker patients or DRG creep? Health Aff (Millwood) 1989;8(2):35-47. DOI: https://doi.org/10.1377/hlthaff.8.2.35.
- Kuster SP, Ruef C, Bollinger AK, Ledergerber B, Hintermann A, Deplazes C, et al. Correlation between case mix index and antibiotic use in hospitals. J Antimicrob Chemother 2008;62(4):837-842. DOI: https://doi.org/10.1093/jac/dkn275.