A survey on statistical methods for health care fraud detection
Fraud and abuse have led to significant additional expense in the health care system of the United States. This paper aims to provide a comprehensive survey of the statistical methods applied to health care fraud detection, with focuses on classifying fraudulent behaviors, identifying the major sour...
Saved in:
| Published in | Health care management science Vol. 11; no. 3; pp. 275 - 287 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.09.2008
Springer Springer Nature B.V |
| Series | Health Care Management Science |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1386-9620 1572-9389 |
| DOI | 10.1007/s10729-007-9045-4 |
Cover
| Abstract | Fraud and abuse have led to significant additional expense in the health care system of the United States. This paper aims to provide a comprehensive survey of the statistical methods applied to health care fraud detection, with focuses on classifying fraudulent behaviors, identifying the major sources and characteristics of the data based on which fraud detection has been conducted, discussing the key steps in data preprocessing, as well as summarizing, categorizing, and comparing statistical fraud detection methods. Based on this survey, some discussion is provided about what has been lacking or under-addressed in the existing research, with the purpose of pinpointing some future research directions. |
|---|---|
| AbstractList | Fraud and abuse have led to significant additional expense in the health care system of the United States. This paper aims to provide a comprehensive survey of the statistical methods applied to health care fraud detection, with focuses on classifying fraudulent behaviors, identifying the major sources and characteristics of the data based on which fraud detection has been conducted, discussing the key steps in data preprocessing, as well as summarizing, categorizing, and comparing statistical fraud detection methods. Based on this survey, some discussion is provided about what has been lacking or under-addressed in the existing research, with the purpose of pinpointing some future research directions. Fraud and abuse have led to significant additional expense in the health care system of the United States. This paper aims to provide a comprehensive survey of the statistical methods applied to health care fraud detection, with focuses on classifying fraudulent behaviors, identifying the major sources and characteristics of the data based on which fraud detection has been conducted, discussing the key steps in data preprocessing, as well as summarizing, categorizing, and comparing statistical fraud detection methods. Based on this survey, some discussion is provided about what has been lacking or under-addressed in the existing research, with the purpose of pinpointing some future research directions.Fraud and abuse have led to significant additional expense in the health care system of the United States. This paper aims to provide a comprehensive survey of the statistical methods applied to health care fraud detection, with focuses on classifying fraudulent behaviors, identifying the major sources and characteristics of the data based on which fraud detection has been conducted, discussing the key steps in data preprocessing, as well as summarizing, categorizing, and comparing statistical fraud detection methods. Based on this survey, some discussion is provided about what has been lacking or under-addressed in the existing research, with the purpose of pinpointing some future research directions. Fraud and abuse have led to significant additional expense in the health care system of the United States. This paper aims to provide a comprehensive survey of the statistical methods applied to health care fraud detection, with focuses on classifying fraudulent behaviors, identifying the major sources and characteristics of the data based on which fraud detection has been conducted, discussing the key steps in data preprocessing, as well as summarizing, categorizing, and comparing statistical fraud detection methods. Based on this survey, some discussion is provided about what has been lacking or under-addressed in the existing research, with the purpose of pinpointing some future research directions. [PUBLICATION ABSTRACT] |
| Author | Huang, Kuei-Ying Jin, Jionghua Shi, Jianjun Li, Jing |
| Author_xml | – sequence: 1 givenname: Jing surname: Li fullname: Li, Jing email: jinglz@asu.edu organization: Department of Industrial Engineering, Arizona State University – sequence: 2 givenname: Kuei-Ying surname: Huang fullname: Huang, Kuei-Ying organization: Department of Industrial and Operations Engineering, University of Michigan – sequence: 3 givenname: Jionghua surname: Jin fullname: Jin, Jionghua organization: Department of Industrial and Operations Engineering, University of Michigan – sequence: 4 givenname: Jianjun surname: Shi fullname: Shi, Jianjun organization: Department of Industrial and Operations Engineering, University of Michigan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/18826005$$D View this record in MEDLINE/PubMed http://econpapers.repec.org/article/kaphcarem/v_3a11_3ay_3a2008_3ai_3a3_3ap_3a275-287.htm$$DView record in RePEc |
| BookMark | eNp9kctu1jAQhS1URC_wAGxQxIJdwLf4skJVVW6qxKZdW44zJilJHGyn0v_2dZRSpEp0MfbY-s7xeOYUHc1hBoTeEvyRYCw_JYIl1XVJa415U_MX6IQ0ktaaKX1UcqZErQXFx-g0pVuMcYMFeYWOiVJUlNMJ-nxepTXewaEKc5WyzUPKg7NjNUHuQ5cqH2LVgx1zXzkbofLRrl3VQQaXhzC_Ri-9HRO8edjP0M2Xy-uLb_XVz6_fL86vase1yrVijAnLuRWqFNrhxkvpZNs6xxrQlrcWbNty6i2lQjrKcSM197S1Hpzwmp2hD7vvEsOfFVI205AcjKOdIazJCC2IZrQp4Psn4G1Y41xqM5RKTRXHtEA_dijCAs4scZhsPJjfdum3T07mzjBLSFkOJSjGqmxDCVZi2a5kY6iSps9TMXv38OLaTtA9uv1tcgHkDrgYUorgjRu2Toc5RzuMhmCzjdPs4zRbuo3T8KIkT5SP5s9o6K5JhZ1_QfzXgP-L7gGRt6_G |
| CitedBy_id | crossref_primary_10_1016_j_jhealeco_2020_102405 crossref_primary_10_1051_matecconf_201818903002 crossref_primary_10_1186_s40352_021_00149_3 crossref_primary_10_2308_JFAR_2021_015 crossref_primary_10_1016_j_protcy_2013_12_140 crossref_primary_10_1109_ACCESS_2024_3425892 crossref_primary_10_1016_j_cosrev_2021_100402 crossref_primary_10_1002_asmb_2633 crossref_primary_10_3390_healthcare9101274 crossref_primary_10_1109_TSC_2021_3051165 crossref_primary_10_1016_j_habitatint_2017_05_007 crossref_primary_10_1186_s40537_018_0138_3 crossref_primary_10_1007_s10479_019_03360_6 crossref_primary_10_1016_j_heliyon_2024_e30045 crossref_primary_10_2139_ssrn_2459343 crossref_primary_10_1016_j_asoc_2015_07_018 crossref_primary_10_1016_j_cmpb_2011_09_003 crossref_primary_10_1109_ACCESS_2022_3170888 crossref_primary_10_1007_s40200_023_01228_y crossref_primary_10_1016_j_eswa_2019_113128 crossref_primary_10_2139_ssrn_1965735 crossref_primary_10_1002_sam_11408 crossref_primary_10_3390_healthcare11131972 crossref_primary_10_1016_j_eswa_2012_01_105 crossref_primary_10_7232_JKIIE_2013_39_4_313 crossref_primary_10_1177_1471082X16685020 crossref_primary_10_1007_s10742_017_0172_1 crossref_primary_10_1016_j_accinf_2016_04_001 crossref_primary_10_1080_02664763_2015_1034659 crossref_primary_10_1007_s41666_018_0019_8 crossref_primary_10_1016_j_dss_2010_08_006 crossref_primary_10_1080_00031305_2017_1292955 crossref_primary_10_1007_s10489_015_0685_7 crossref_primary_10_1016_j_insmatheco_2016_09_013 crossref_primary_10_1155_2019_1432597 crossref_primary_10_1007_s10729_015_9317_3 crossref_primary_10_1016_j_ress_2015_05_025 crossref_primary_10_1080_10864415_2022_2076199 crossref_primary_10_1007_s00287_021_01398_0 crossref_primary_10_1111_pme_12713 crossref_primary_10_1111_jori_12359 crossref_primary_10_15171_ijhpm_2015_196 crossref_primary_10_1007_s12652_021_03481_6 crossref_primary_10_1186_s40537_019_0225_0 crossref_primary_10_1080_10920277_2021_1895843 crossref_primary_10_1111_insr_12269 crossref_primary_10_4018_JOEUC_301271 crossref_primary_10_2174_1874944502013010718 crossref_primary_10_1016_j_jnca_2016_04_007 crossref_primary_10_3414_ME15_01_0076 crossref_primary_10_1002_mde_3117 crossref_primary_10_1080_02664763_2021_1959528 crossref_primary_10_7595_management_fon_2018_0015 crossref_primary_10_1016_j_jocs_2017_02_007 crossref_primary_10_3390_app10155144 crossref_primary_10_1016_j_jeconom_2020_05_021 crossref_primary_10_1177_2168479015620248 crossref_primary_10_3390_healthcare6020054 crossref_primary_10_1093_comjnl_bxab038 crossref_primary_10_1365_s40702_016_0278_x crossref_primary_10_1007_s10729_013_9247_x crossref_primary_10_1057_s41270_019_00055_6 crossref_primary_10_1186_s12874_022_01768_6 crossref_primary_10_1109_TKDE_2019_2924431 crossref_primary_10_1007_s00500_023_08296_5 crossref_primary_10_1016_j_artmed_2024_103061 crossref_primary_10_1186_s12913_017_2054_1 crossref_primary_10_1109_TVCG_2023_3261910 crossref_primary_10_37920_sasj_2020_54_2_2 |
| Cites_doi | 10.1111/1539-6975.00025 10.1016/j.compind.2003.10.006 10.1016/S0957-4174(97)00045-6 10.1007/3-540-63797-4_87 10.1109/TKDE.2004.1277822 10.1002/9781119013563 10.1080/00224065.2008.11917712 10.1016/S1088-467X(97)00008-5 10.1089/106652700750050961 10.1023/B:DAMI.0000023676.72185.7c 10.1016/j.ecolmodel.2003.08.020 10.1145/765891.765910 10.1016/j.eswa.2005.09.003 10.1109/ICSMC.1998.725092 10.20965/jaciii.2000.p0130 10.1145/279943.279962 10.1109/HICSS.2001.926570 10.1023/A:1007692713085 10.1080/07408170600899532 10.1007/978-1-4612-2748-9 10.1145/312129.312195 10.1111/j.1467-8640.1994.tb00166.x 10.1007/3-540-48912-6_26 10.1145/1007730.1007738 10.1007/978-94-011-5014-9_11 10.1097/00005110-199706000-00008 |
| ContentType | Journal Article |
| Copyright | Springer Science+Business Media, LLC 2007 Springer Science+Business Media, LLC 2008 |
| Copyright_xml | – notice: Springer Science+Business Media, LLC 2007 – notice: Springer Science+Business Media, LLC 2008 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM DKI X2L 3V. 7WY 7WZ 7X7 7XB 87Z 88C 88E 8AO 8FI 8FJ 8FK 8FL ABUWG AFKRA BENPR BEZIV CCPQU DWQXO FRNLG FYUFA F~G GHDGH K60 K6~ K9. L.- L.0 M0C M0S M0T M1P PHGZM PHGZT PJZUB PKEHL PPXIY PQBIZ PQBZA PQEST PQQKQ PQUKI Q9U 7X8 |
| DOI | 10.1007/s10729-007-9045-4 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed RePEc IDEAS RePEc ProQuest Central (Corporate) ABI/INFORM Collection ABI/INFORM Global (PDF only) Health & Medical Collection ProQuest Central (purchase pre-March 2016) ABI/INFORM Global (Alumni Edition) Healthcare Administration Database (Alumni) Medical Database (Alumni Edition) ProQuest Pharma Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni Edition) ProQuest Central (Alumni Edition) ProQuest Central ProQuest Central Business Premium Collection ProQuest One Community College ProQuest Central Business Premium Collection (Alumni) Health Research Premium Collection ABI/INFORM Global (Corporate) Health Research Premium Collection (Alumni) ProQuest Business Collection (Alumni Edition) ProQuest Business Collection ProQuest Health & Medical Complete (Alumni) ABI/INFORM Professional Advanced ABI/INFORM Professional Standard ABI/INFORM Global Health & Medical Collection (Alumni Edition) Healthcare Administration Database Medical Database ProQuest Central Premium ProQuest One Academic ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central Basic MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Pharma Collection ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ABI/INFORM Professional Standard ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ABI/INFORM Complete (Alumni Edition) Business Premium Collection ABI/INFORM Global ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Health Management ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Business Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest Health Management (Alumni Edition) ProQuest One Business (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) Business Premium Collection (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic ABI/INFORM Global (Corporate) |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: DKI name: RePEc IDEAS url: http://ideas.repec.org/ sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central url: http://www.proquest.com/pqcentral?accountid=15518 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Public Health Business |
| EISSN | 1572-9389 |
| EndPage | 287 |
| ExternalDocumentID | 1531672441 kaphcarem_v_3a11_3ay_3a2008_3ai_3a3_3ap_3a275_287_htm 18826005 10_1007_s10729_007_9045_4 |
| Genre | Journal Article |
| GroupedDBID | --- -57 -5G -BR -EM -Y2 -~C .86 .VR 06D 07C 0R~ 0VY 199 1N0 1SB 203 29I 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 36B 3V. 4.4 406 408 409 40D 40E 44B 53G 5GY 5VS 67Z 6NX 78A 7WY 7X7 88E 8AO 8FI 8FJ 8FL 8FW 8TC 8UJ 8VB 95- 95. 95~ 96X AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAWTL AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABIPD ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABPLI ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHQT ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADBBV ADHHG ADHIR ADINQ ADJJI ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHIZS AHMBA AHQJS AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ AKVCP ALIPV ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG AQUVI ARMRJ ASPBG AVWKF AXYYD AYQZM AZFZN B-. BA0 BAPOH BDATZ BENPR BEZIV BGNMA BPHCQ BSONS BVXVI CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBD EBLON EBO EBS EBU EHE EIOEI EJD EMB EMOBN EOH ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC FYUFA GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GROUPED_ABI_INFORM_RESEARCH GXS H13 HF~ HG5 HG6 HMCUK HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ IMOTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K1G K60 K6~ KDC KOV LAK LLZTM M0C M0T M1P M4Y MA- N2Q NB0 NPVJJ NQJWS NU0 O9- O93 O9G O9J OAM OVD P2P P9M PF0 PQBIZ PQBZA PQQKQ PROAC PSQYO PT4 PT5 Q2X QOS QWB R89 R9I RNI ROL RPX RSV RZC RZD RZK S16 S1Z S27 S3B SAP SBE SDH SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW SSXJD STPWE SV3 SZN T13 TEORI TH9 TSG TSK TSV TUC U2A U9L UG4 UKHRP UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z81 Z83 ZL0 ZMTXR ~8M AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PJZUB PPXIY PUEGO CGR CUY CVF ECM EIF NPM - 0R 57 5G 86 95 AAEIZ AAFGU AAGJQ AAPBV ABFGW ABKAS ACBMV ACBRV ACBYP ACDSR ACIGE ACIPQ ACTTH ACVWB ACWMK ADMDM ADTIX AEFTE AESTI AEVTX AGGBP AIMYW AJDOV AKQUC BBAFP BR C DKI EM HF HZ IPNFZ K6 KSO PQEST PQUKI PRINS RIG UNUBA VR X2L Y2 7XB 8FK K9. L.- L.0 PKEHL Q9U 7X8 |
| ID | FETCH-LOGICAL-c498t-83336a44a68045d05f77c7bbcc35e9a4baeabb42fa2267c2405794f2bafec6f93 |
| IEDL.DBID | AGYKE |
| ISSN | 1386-9620 |
| IngestDate | Fri Sep 05 13:00:14 EDT 2025 Mon Oct 06 16:58:13 EDT 2025 Sun Jul 17 10:24:02 EDT 2022 Thu Apr 03 07:07:16 EDT 2025 Wed Oct 01 03:46:43 EDT 2025 Thu Apr 24 22:53:11 EDT 2025 Fri Feb 21 02:36:00 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | Health care Fraud detection Statistical methods |
| Language | English |
| License | http://www.springer.com/tdm |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c498t-83336a44a68045d05f77c7bbcc35e9a4baeabb42fa2267c2405794f2bafec6f93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PMID | 18826005 |
| PQID | 227928402 |
| PQPubID | 25887 |
| PageCount | 13 |
| ParticipantIDs | proquest_miscellaneous_69619325 proquest_journals_227928402 repec_primary_kaphcarem_v_3a11_3ay_3a2008_3ai_3a3_3ap_3a275_287_htm pubmed_primary_18826005 crossref_citationtrail_10_1007_s10729_007_9045_4 crossref_primary_10_1007_s10729_007_9045_4 springer_journals_10_1007_s10729_007_9045_4 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2008-09-01 |
| PublicationDateYYYYMMDD | 2008-09-01 |
| PublicationDate_xml | – month: 09 year: 2008 text: 2008-09-01 day: 01 |
| PublicationDecade | 2000 |
| PublicationPlace | Boston |
| PublicationPlace_xml | – name: Boston – name: Netherlands – name: New york |
| PublicationSeriesTitle | Health Care Management Science |
| PublicationTitle | Health care management science |
| PublicationTitleAbbrev | Health Care Manage Sci |
| PublicationTitleAlternate | Health Care Manag Sci |
| PublicationYear | 2008 |
| Publisher | Springer US Springer Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer – name: Springer Nature B.V |
| References | BennettKDemirizASemi-supervised support vector machinesAdv Neural Inf Process Syst199812368374 MajorJARiedingerDREFD: A hybrid knowledge/statistical-based system for the detection of fraudThe Journal of Risk and Insurance200269330932410.1111/1539-6975.00025 Cooper C (2003) Turning information into action. Computer Associates: The Software That Manages eBusiness, Report, available at http://www.ca.com Li J, Jin J, Shi J (2008) Causation-based T2 Decomposition for Multivariate Process Monitoring and Diagnosis. Journal of Quality Technology, to appear in January 2008. LamWBacchusFLearning Bayesian belief networks: an approach based on the MDL principleComput Intell19931026929310.1111/j.1467-8640.1994.tb00166.x Yang WS (2003) A Process Pattern Mining Framework for the Detection of Health Care Fraud and Abuse, Ph.D. thesis, National Sun Yat-Sen University, Taiwan WilliamsGHuangZMining the knowledge mine: The Hot Spots methodology for mining large real world databasesLect Notes Comput Sci1997134234034810.1007/3-540-63797-4_87 Ormerod T, Morley N, Ball L, Langley C, Spenser C (2003) Using ethnography to design a Mass Detection Tool (MDT) for the early discovery of insurance fraud. In Proceedings of the ACM CHI Conference HeHHawkinsSGracoWYaoXApplication of Genetic Algorithms and k-Nearest Neighbour method in real world medical fraud detection problemJournal of Advanced Computational Intelligence and Intelligent Informatics200042130137 NigamKMcCalumAThrunSMitchellTText classification from labeled and unlabeled documents using EMMachine Learning20003910313410.1023/A:1007692713085 YamanishiKTakeuchiJWilliamsGMilnePOn-line unsupervised outlier detection using finite mixtures with discounting learning algorithmsData Mining and Knowledge Discovery2004827530010.1023/B:DAMI.0000023676.72185.7c Chan CL, Lan CH (2001) A data mining technique combining fuzzy sets theory and Bayesian classifier—an application of auditing the health insurance fee. In Proceedings of the International Conference on Artificial Intelligence, 402–408 WilliamsGEvolutionary Hot Spots data mining: an architecture for exploring for interesting discoveriesLect Notes Comput Sci19991574184193 PhuaCAlahakoonDLeeVMinority report in fraud detection: classification of skewed dataSIGKEE Explorations200461505910.1145/1007730.1007738 GAO (1996) Health Care Fraud: Information-Sharing Proposals to Improve Enforcement Effects. Report of United States General Accounting Office HubickKTArtificial neural networks in Australia1992CanberraDepartment of Industry, Technology and Commerce, CPN Publications LiJShiJKnowledge Discovery from Observational Data for Process Control using Causal Bayesian NetworksIIE Transactions200739668169010.1080/07408170600899532 Viveros MS, Nearhos JP, Rothman MJ (1996) Applying data mining techniques to a health insurance information system. In Proceedings of the 22nd VLDB Conference, Mumbai, India, 286–294 FriedmanNLinialMNachmanIPe’erDUsing Bayesian networks to analyze expression dataJ Comput Biol2000760162010.1089/106652700750050961 YangWSHwangSYA process-mining framework for the detection of healthcare fraud and abuseExpert Syst Appl200631566810.1016/j.eswa.2005.09.003 NHCAA (2005) The Problem of Health Care Fraud: A serious and costly reality for all Americans, report of National Health Care Anti-Fraud Association (NHCAA) BorsukMEStowCAReckhowKHA Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysisEcol Model200417321923910.1016/j.ecolmodel.2003.08.020 HwangSYWeiCPYangWSDiscovery of temporal patterns from process instancesComp Ind20035334536410.1016/j.compind.2003.10.006 DashMLiuHFeature selection for classificationIDA19971131156 ShapiroAFThe merging of neural networks, fuzzy logic, and genetic algorithmsInsurance: Mathematics and Economics20023111513110.1016/S0167-6687(02)00124-5 Yang WS (2002) Process analyzer and its application on medical care. In Proceedings of 23rd International Conference on Information Systems (ICIS02), Spain HallCIntelligent data mining at IBM: new products and applicationsIntell Softw Strateg199675111 ViaeneSDerrigRDedeneGA case study of applying boosting Naive Bayes to claim fraud diagnosisIEEE Trans Knowl Data Eng200416561262010.1109/TKDE.2004.1277822 Wei CP, Hwang SY, Yang WS (2000) Mining frequent temporal patterns in process databases. Proceedings of international workshop on information technologies and systems, Australia, 175–180 GhoshSReillyDCredit card fraud detection with a neural networkProceedings of 27th Hawaii International Conference on Systems Science19943621630 Sokol L, Garcia B, West M, Rodriguez J, Johnson K (2001) Precursory steps to mining HCFA health care claims. In Proceedings of the 34th Hawaii International Conference on System Sciences Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In Proceedings of the 11th Annual Conference on Computational Learning Theory HeHWangJGracoWHawkinsSApplication of neural networks to detection of medical fraudExpert Syst Appl19971332933610.1016/S0957-4174(97)00045-6 HeckermanDA tutorial on learning with Bayesian networks. In Learning in Graphical Models1998BostonKluwer Academic301354 Lin J-H, Haug PJ (2006) Data preparation framework for preprocessing clinical data in data mining, AMIA Symposium Proceedings 489–493 AbbottDWMatkovskyIPElderJFAn evaluation of high-end data mining tools for fraud detection1998Man, and Cybernetics, San Diego, CAIn Proceedings of IEEE International Conference on Systems Ortega PA, Figueroa CJ, Ruz GA (2006) A medical claim fraud/abuse detection system based on data mining: a case study in Chile. In Proceedings of International Conference on Data Mining, Las Vegas, Nevada, USA IresonCLCritical pathways: effectiveness in achieving patient outcomesJ Nurs Adm1997276162310.1097/00005110-199706000-00008 Dai H, Korb KB, Wallace CS, Wu X (1997) A study of casual discovery with weak links and small samples. In Proceeding of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI), San Francisco, CA, pp 1304–1309 Fawcett T, Provost F (1999) Activity monitoring: noticing interesting changes in behavior. In Proceedings of SIGKDD99, 53–62 Bonchi F, Giannotti F, Mainetto G, Pedreschi D (1999) A classification-based methodology for planning auditing strategies in fraud detection. In Proceedings of SIGKDD99, 175–184 HerbWTomMA scientific approach for detecting fraudBest’s Review19959547881 CoxEGoonatilakeSTreleavenPA fuzzy system for detecting anomalous behaviors in healthcare provider claimsIntelligent systems for finance and business1995New YorkWiley111134 LittleRJARubinDBStatistical analysis with missing data2002New YorkWiley SpirtesPGlymourCScheinesRCausation, Prediction and Search1993New YorkSpringer N Friedman (9045_CR12) 2000; 7 S Viaene (9045_CR37) 2004; 16 RJA Little (9045_CR26) 2002 ME Borsuk (9045_CR5) 2004; 173 K Nigam (9045_CR29) 2000; 39 C Hall (9045_CR15) 1996; 7 H He (9045_CR17) 1997; 13 K Bennett (9045_CR2) 1998; 12 H He (9045_CR16) 2000; 4 WS Yang (9045_CR43) 2006; 31 M Dash (9045_CR10) 1997; 1 9045_CR31 9045_CR11 G Williams (9045_CR41) 1997; 1342 S Ghosh (9045_CR14) 1994; 3 DW Abbott (9045_CR1) 1998 9045_CR30 9045_CR39 KT Hubick (9045_CR20) 1992 9045_CR3 9045_CR13 9045_CR35 J Li (9045_CR24) 2007; 39 9045_CR4 9045_CR38 9045_CR7 9045_CR6 9045_CR9 SY Hwang (9045_CR21) 2003; 53 K Yamanishi (9045_CR42) 2004; 8 CL Ireson (9045_CR33) 1997; 27 P Spirtes (9045_CR36) 1993 JA Major (9045_CR27) 2002; 69 D Heckerman (9045_CR18) 1998 W Herb (9045_CR19) 1995; 95 C Phua (9045_CR32) 2004; 6 9045_CR44 9045_CR23 9045_CR45 9045_CR28 W Lam (9045_CR22) 1993; 10 G Williams (9045_CR40) 1999; 1574 E Cox (9045_CR8) 1995 9045_CR25 AF Shapiro (9045_CR34) 2002; 31 9204043 - J Nurs Adm. 1997 Jun;27(6):16-23 17238389 - AMIA Annu Symp Proc. 2006;:489-93 11108481 - J Comput Biol. 2000;7(3-4):601-20 |
| References_xml | – reference: Cooper C (2003) Turning information into action. Computer Associates: The Software That Manages eBusiness, Report, available at http://www.ca.com – reference: Lin J-H, Haug PJ (2006) Data preparation framework for preprocessing clinical data in data mining, AMIA Symposium Proceedings 489–493 – reference: Wei CP, Hwang SY, Yang WS (2000) Mining frequent temporal patterns in process databases. Proceedings of international workshop on information technologies and systems, Australia, 175–180 – reference: HeckermanDA tutorial on learning with Bayesian networks. In Learning in Graphical Models1998BostonKluwer Academic301354 – reference: NHCAA (2005) The Problem of Health Care Fraud: A serious and costly reality for all Americans, report of National Health Care Anti-Fraud Association (NHCAA) – reference: Yang WS (2002) Process analyzer and its application on medical care. In Proceedings of 23rd International Conference on Information Systems (ICIS02), Spain – reference: Sokol L, Garcia B, West M, Rodriguez J, Johnson K (2001) Precursory steps to mining HCFA health care claims. In Proceedings of the 34th Hawaii International Conference on System Sciences – reference: YangWSHwangSYA process-mining framework for the detection of healthcare fraud and abuseExpert Syst Appl200631566810.1016/j.eswa.2005.09.003 – reference: Yang WS (2003) A Process Pattern Mining Framework for the Detection of Health Care Fraud and Abuse, Ph.D. thesis, National Sun Yat-Sen University, Taiwan – reference: BorsukMEStowCAReckhowKHA Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysisEcol Model200417321923910.1016/j.ecolmodel.2003.08.020 – reference: LittleRJARubinDBStatistical analysis with missing data2002New YorkWiley – reference: Bonchi F, Giannotti F, Mainetto G, Pedreschi D (1999) A classification-based methodology for planning auditing strategies in fraud detection. In Proceedings of SIGKDD99, 175–184 – reference: Chan CL, Lan CH (2001) A data mining technique combining fuzzy sets theory and Bayesian classifier—an application of auditing the health insurance fee. In Proceedings of the International Conference on Artificial Intelligence, 402–408 – reference: LamWBacchusFLearning Bayesian belief networks: an approach based on the MDL principleComput Intell19931026929310.1111/j.1467-8640.1994.tb00166.x – reference: HwangSYWeiCPYangWSDiscovery of temporal patterns from process instancesComp Ind20035334536410.1016/j.compind.2003.10.006 – reference: Dai H, Korb KB, Wallace CS, Wu X (1997) A study of casual discovery with weak links and small samples. In Proceeding of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI), San Francisco, CA, pp 1304–1309 – reference: IresonCLCritical pathways: effectiveness in achieving patient outcomesJ Nurs Adm1997276162310.1097/00005110-199706000-00008 – reference: WilliamsGEvolutionary Hot Spots data mining: an architecture for exploring for interesting discoveriesLect Notes Comput Sci19991574184193 – reference: HerbWTomMA scientific approach for detecting fraudBest’s Review19959547881 – reference: ShapiroAFThe merging of neural networks, fuzzy logic, and genetic algorithmsInsurance: Mathematics and Economics20023111513110.1016/S0167-6687(02)00124-5 – reference: Li J, Jin J, Shi J (2008) Causation-based T2 Decomposition for Multivariate Process Monitoring and Diagnosis. Journal of Quality Technology, to appear in January 2008. – reference: SpirtesPGlymourCScheinesRCausation, Prediction and Search1993New YorkSpringer – reference: GhoshSReillyDCredit card fraud detection with a neural networkProceedings of 27th Hawaii International Conference on Systems Science19943621630 – reference: FriedmanNLinialMNachmanIPe’erDUsing Bayesian networks to analyze expression dataJ Comput Biol2000760162010.1089/106652700750050961 – reference: WilliamsGHuangZMining the knowledge mine: The Hot Spots methodology for mining large real world databasesLect Notes Comput Sci1997134234034810.1007/3-540-63797-4_87 – reference: HubickKTArtificial neural networks in Australia1992CanberraDepartment of Industry, Technology and Commerce, CPN Publications – reference: Ormerod T, Morley N, Ball L, Langley C, Spenser C (2003) Using ethnography to design a Mass Detection Tool (MDT) for the early discovery of insurance fraud. In Proceedings of the ACM CHI Conference – reference: CoxEGoonatilakeSTreleavenPA fuzzy system for detecting anomalous behaviors in healthcare provider claimsIntelligent systems for finance and business1995New YorkWiley111134 – reference: PhuaCAlahakoonDLeeVMinority report in fraud detection: classification of skewed dataSIGKEE Explorations200461505910.1145/1007730.1007738 – reference: ViaeneSDerrigRDedeneGA case study of applying boosting Naive Bayes to claim fraud diagnosisIEEE Trans Knowl Data Eng200416561262010.1109/TKDE.2004.1277822 – reference: YamanishiKTakeuchiJWilliamsGMilnePOn-line unsupervised outlier detection using finite mixtures with discounting learning algorithmsData Mining and Knowledge Discovery2004827530010.1023/B:DAMI.0000023676.72185.7c – reference: HeHWangJGracoWHawkinsSApplication of neural networks to detection of medical fraudExpert Syst Appl19971332933610.1016/S0957-4174(97)00045-6 – reference: Ortega PA, Figueroa CJ, Ruz GA (2006) A medical claim fraud/abuse detection system based on data mining: a case study in Chile. In Proceedings of International Conference on Data Mining, Las Vegas, Nevada, USA – reference: Viveros MS, Nearhos JP, Rothman MJ (1996) Applying data mining techniques to a health insurance information system. In Proceedings of the 22nd VLDB Conference, Mumbai, India, 286–294 – reference: LiJShiJKnowledge Discovery from Observational Data for Process Control using Causal Bayesian NetworksIIE Transactions200739668169010.1080/07408170600899532 – reference: Fawcett T, Provost F (1999) Activity monitoring: noticing interesting changes in behavior. In Proceedings of SIGKDD99, 53–62 – reference: BennettKDemirizASemi-supervised support vector machinesAdv Neural Inf Process Syst199812368374 – reference: NigamKMcCalumAThrunSMitchellTText classification from labeled and unlabeled documents using EMMachine Learning20003910313410.1023/A:1007692713085 – reference: HallCIntelligent data mining at IBM: new products and applicationsIntell Softw Strateg199675111 – reference: Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In Proceedings of the 11th Annual Conference on Computational Learning Theory – reference: HeHHawkinsSGracoWYaoXApplication of Genetic Algorithms and k-Nearest Neighbour method in real world medical fraud detection problemJournal of Advanced Computational Intelligence and Intelligent Informatics200042130137 – reference: AbbottDWMatkovskyIPElderJFAn evaluation of high-end data mining tools for fraud detection1998Man, and Cybernetics, San Diego, CAIn Proceedings of IEEE International Conference on Systems – reference: DashMLiuHFeature selection for classificationIDA19971131156 – reference: MajorJARiedingerDREFD: A hybrid knowledge/statistical-based system for the detection of fraudThe Journal of Risk and Insurance200269330932410.1111/1539-6975.00025 – reference: GAO (1996) Health Care Fraud: Information-Sharing Proposals to Improve Enforcement Effects. Report of United States General Accounting Office – ident: 9045_CR44 – volume: 69 start-page: 309 issue: 3 year: 2002 ident: 9045_CR27 publication-title: The Journal of Risk and Insurance doi: 10.1111/1539-6975.00025 – volume: 53 start-page: 345 year: 2003 ident: 9045_CR21 publication-title: Comp Ind doi: 10.1016/j.compind.2003.10.006 – start-page: 111 volume-title: Intelligent systems for finance and business year: 1995 ident: 9045_CR8 – volume: 95 start-page: 78 issue: 4 year: 1995 ident: 9045_CR19 publication-title: Best’s Review – volume: 13 start-page: 329 year: 1997 ident: 9045_CR17 publication-title: Expert Syst Appl doi: 10.1016/S0957-4174(97)00045-6 – volume: 1342 start-page: 340 year: 1997 ident: 9045_CR41 publication-title: Lect Notes Comput Sci doi: 10.1007/3-540-63797-4_87 – volume: 16 start-page: 612 issue: 5 year: 2004 ident: 9045_CR37 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2004.1277822 – volume-title: Statistical analysis with missing data year: 2002 ident: 9045_CR26 doi: 10.1002/9781119013563 – ident: 9045_CR23 doi: 10.1080/00224065.2008.11917712 – ident: 9045_CR38 – ident: 9045_CR6 – volume: 1 start-page: 131 year: 1997 ident: 9045_CR10 publication-title: IDA doi: 10.1016/S1088-467X(97)00008-5 – ident: 9045_CR13 – volume: 12 start-page: 368 year: 1998 ident: 9045_CR2 publication-title: Adv Neural Inf Process Syst – ident: 9045_CR4 – ident: 9045_CR31 – volume: 7 start-page: 601 year: 2000 ident: 9045_CR12 publication-title: J Comput Biol doi: 10.1089/106652700750050961 – volume: 8 start-page: 275 year: 2004 ident: 9045_CR42 publication-title: Data Mining and Knowledge Discovery doi: 10.1023/B:DAMI.0000023676.72185.7c – volume: 173 start-page: 219 year: 2004 ident: 9045_CR5 publication-title: Ecol Model doi: 10.1016/j.ecolmodel.2003.08.020 – ident: 9045_CR30 doi: 10.1145/765891.765910 – volume: 7 start-page: 1 issue: 5 year: 1996 ident: 9045_CR15 publication-title: Intell Softw Strateg – volume: 31 start-page: 115 year: 2002 ident: 9045_CR34 publication-title: Insurance: Mathematics and Economics – ident: 9045_CR28 – volume: 31 start-page: 56 year: 2006 ident: 9045_CR43 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2005.09.003 – volume-title: An evaluation of high-end data mining tools for fraud detection year: 1998 ident: 9045_CR1 doi: 10.1109/ICSMC.1998.725092 – volume: 4 start-page: 130 issue: 2 year: 2000 ident: 9045_CR16 publication-title: Journal of Advanced Computational Intelligence and Intelligent Informatics doi: 10.20965/jaciii.2000.p0130 – ident: 9045_CR45 – ident: 9045_CR3 doi: 10.1145/279943.279962 – ident: 9045_CR35 doi: 10.1109/HICSS.2001.926570 – volume: 39 start-page: 103 year: 2000 ident: 9045_CR29 publication-title: Machine Learning doi: 10.1023/A:1007692713085 – volume: 39 start-page: 681 issue: 6 year: 2007 ident: 9045_CR24 publication-title: IIE Transactions doi: 10.1080/07408170600899532 – ident: 9045_CR9 – volume-title: Causation, Prediction and Search year: 1993 ident: 9045_CR36 doi: 10.1007/978-1-4612-2748-9 – ident: 9045_CR39 – volume: 3 start-page: 621 year: 1994 ident: 9045_CR14 publication-title: Proceedings of 27th Hawaii International Conference on Systems Science – ident: 9045_CR7 – ident: 9045_CR11 doi: 10.1145/312129.312195 – volume: 10 start-page: 269 year: 1993 ident: 9045_CR22 publication-title: Comput Intell doi: 10.1111/j.1467-8640.1994.tb00166.x – volume: 1574 start-page: 184 year: 1999 ident: 9045_CR40 publication-title: Lect Notes Comput Sci doi: 10.1007/3-540-48912-6_26 – volume: 6 start-page: 50 issue: 1 year: 2004 ident: 9045_CR32 publication-title: SIGKEE Explorations doi: 10.1145/1007730.1007738 – start-page: 301 volume-title: A tutorial on learning with Bayesian networks. In Learning in Graphical Models year: 1998 ident: 9045_CR18 doi: 10.1007/978-94-011-5014-9_11 – volume-title: Artificial neural networks in Australia year: 1992 ident: 9045_CR20 – volume: 27 start-page: 16 issue: 6 year: 1997 ident: 9045_CR33 publication-title: J Nurs Adm doi: 10.1097/00005110-199706000-00008 – ident: 9045_CR25 – reference: 9204043 - J Nurs Adm. 1997 Jun;27(6):16-23 – reference: 17238389 - AMIA Annu Symp Proc. 2006;:489-93 – reference: 11108481 - J Comput Biol. 2000;7(3-4):601-20 |
| SSID | ssj0005061 |
| Score | 2.2116394 |
| SecondaryResourceType | review_article |
| Snippet | Fraud and abuse have led to significant additional expense in the health care system of the United States. This paper aims to provide a comprehensive survey of... |
| SourceID | proquest repec pubmed crossref springer |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 275 |
| SubjectTerms | Business and Management Departments Econometrics Fraud Fraud - statistics & numerical data Fraud detection Fraud prevention Health Administration Health care Health care expenditures Health care industry Health care policy Health Care Sector - statistics & numerical data Health Informatics Health insurance Humans Insurance Claim Reporting - statistics & numerical data Insurance Claim Review - statistics & numerical data Insurance companies Management Medicaid fraud Models, Statistical Operations Research/Decision Theory Statistical methods Studies Subject specialists |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1baxUxEB7qKYggovW2ttU8-KQEt5vLJg-l1NJShBYRC30LSTZLRbtnPWdPof_eSfZykNI-BJbN3pLMJt9kZr4B-KgKLpQTmnpWWVRQKkWdrHPq1V6ucUasWOLZPjuXpxf826W43ICzMRYmulWOc2KaqKu5j3vkXxLTHWojxUH7l8akUdG4OmbQsENmhWo_MYw9gs0iEmPNYPPr8fn3H2ufj7wnUGVKUi2LyczZx9IhzqRx504jzKH8_4XqDvpEMLsIbfB3LKhpYTp5Ds8GREkOexF4ARuh2YLHo0P7Fjztd-ZIH3D0Eg4OyXK1uAm3ZN6QGE-UqJrxCX0y6SVBGEv68EgS_cJIvbCrilShS15bzSu4ODn-eXRKhzQK1HOtOqoYY9JybqXChlW5qMvSl855z0TQljsbrHO8qC1CsdIXEcJpXhfO1sHLWrPXMGvmTXgLRHjmeNB1wRmqdYncTSHgkD6vCiZsnkE-9pnxA8d4THXxx6zZkWM3m3gYu9nwDD5Nt7Q9wcZDF2-PA2GGf21pJsnI4MNUiz9JtHzYJsxXSyO1jEBVZPCmH731q1DDQMyHNUdpOKeK37ZNznfX5sYwi1oSs7dYUtZOZn9hYVjaeKoUBvVOc9VdZ_B5FIb1993bmncPtmYbnoy-KfneDsy6xSrsIgDq3PtBrP8BGDX9iQ priority: 102 providerName: ProQuest |
| Title | A survey on statistical methods for health care fraud detection |
| URI | https://link.springer.com/article/10.1007/s10729-007-9045-4 https://www.ncbi.nlm.nih.gov/pubmed/18826005 http://econpapers.repec.org/article/kaphcarem/v_3a11_3ay_3a2008_3ai_3a3_3ap_3a275-287.htm https://www.proquest.com/docview/227928402 https://www.proquest.com/docview/69619325 |
| Volume | 11 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVLSH databaseName: SpringerLink Journals customDbUrl: mediaType: online eissn: 1572-9389 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005061 issn: 1386-9620 databaseCode: AFBBN dateStart: 19980901 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1572-9389 dateEnd: 20171231 omitProxy: true ssIdentifier: ssj0005061 issn: 1386-9620 databaseCode: 7X7 dateStart: 19990101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1572-9389 dateEnd: 20171231 omitProxy: true ssIdentifier: ssj0005061 issn: 1386-9620 databaseCode: BENPR dateStart: 19990101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 1572-9389 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005061 issn: 1386-9620 databaseCode: AGYKE dateStart: 19980101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 1572-9389 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0005061 issn: 1386-9620 databaseCode: U2A dateStart: 19980901 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB7RrYSQEI9CIRSKD5xArtLYceITWsqWQkWFUFdaTpbtOAKVZlebpFL59YydZBcoIPXgJLITx29_Y898BniRJzzNTSqpZYVGAaXIqRFlTG2-H0scEQsWeLY_noijKf8wS2e9HXc9aLsPW5JhpP7F2A2BIPVLaxJxCOUbsBnotkawOX735Xiy1uyIO5pUlgsqRbLazPxbJL9PR1cwJkLWpVs4e2WfNEw_h3fhdEh4p3Vyttc2Zs_--IPT8Zo5uwd3ejhKxl37uQ83XLUFNwdt-C243S3rkc5a6QG8HpO6XV64SzKviDdGCjzPGEN3EnVNEAOTzraSeKUyUi51W5DCNUHlq3oI08PJ6cER7c9goJbLvKE5Y0xozrXIMWlFnJZZZjNjrGWpk5ob7bQxPCk14rjMJh7_SV4mRpfOilKybRhV88o9BpJaZriTZcIZyoSBGS5HtCJsXCQs1XEE8VAVyvYE5f6cjO9qTa3sC0r5R19QikfwcvXJomPn-N_LO0P9qr6j1ioQKKKQm0TwfBWKPcxvm-jKzdtaCSk8yk0jeNQ1ivWvUDxBwIghB6GVrALO9CJo7p2rC8U0ilhMX6ILR34y_Q0dQ7fwXlmqUGhVX5vzCF4NzWOdvn_m5sm13t6BW4OiS7z_FEbNsnXPEE01Zhc2slm22_chvL-ZnHz6jL5vj9_jdZqMfwL_hxVF |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3da9UwFA9jAxVEdOqsU5cHfVGCXZKmycMYc27cue0issHeYpKmKLreem_v5P5x_m-epB8XGe5tD4HStE1z8vU7yTm_g9BrSXkmbaaIY4UBBaWQxIoyJU5upwpmxIJFnu3TsRid808X2cUK-tP7wgSzyn5OjBN1MXFhj_x9ZLoDbYTu1r9ICBoVDlf7CBqmi6xQ7ESGsc6v49gvfoMGN9s5-gjN_YbSw4Oz_RHpggwQx5VsiGSMCcO5ERLQTZFmZZ673FrnWOaV4dZ4Yy2npQGgkjsaAI7iJbWm9E6UgYsJVoA1zrgC3W_tw8H485eljUnaErYyKYgSdDhWbX33ANeSsFOooGDC_10Yr6FdAM9TX3t37cQ2LoSHD9GDDsHivbbLPUIrvlpHd3oD-nV0v90JxK2D02O0u4dn8-mVX-BJhYP_UqSGhi-0watnGGAzbt0xcbBDw-XUzAtc-CZaiVVP0PmtSPQpWq0mlX-GcOaY5V6VFMTLZSSTkwBwhEsLyjKTJijtZaZdx2keQmv81Es25iBmHS6DmDVP0Nvhlbol9Ljp4c2-IXQ3tmd66IkJ2hpyYVCGkxZT-cl8poUSARhnCdpoW29ZFGg0gDEhZz8255Dxw9TR2O9SX2lmQCtjZgEpRgll5jskBqkOt_JMg56rvzWXCXrXd4bl__23Ns9vrM0Wujs6Oz3RJ0fj4010r7eLSbdfoNVmOvcvAXw19lXXxTH6etuj6i_aBTt8 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Nb9QwELWqIlVIqILyFQrUB7iArKa248QHVFUtq5ZCxYFKezO246gImk032aL9afw7xnaSFarorQdLq3g32dhj-4395g1CbwrKs8JkklhWanBQyoIYUaXEFnuphBmxZEFn-8uZOD7nn6bZdA39GWJhPK1ymBPDRF3OrN8j3w1Kd-CN0N2qZ0V8PZrsN1fEJ5DyB61DNo1oIadu-Ru8t_bDyRF09VtKJx-_HR6TPsEAsVwWHSkYY0JzrkUByKZMsyrPbW6MtSxzUnOjnTaG00oDSMkt9eBG8ooaXTkrKq_DBLP_vZwx6dmE-TRfsUvSKNXKCkGkoOOBaozaA0RL_B6hhMcS_u-SeAPnAmyeu8bZG2e1YQmcPESbPXbFB9HYHqE1V2-hjYE6v4UexD1AHEObHqP9A9wu5tduiWc19pFLQRQa7hDTVrcYADOOgZjYM9BwNdeLEpeuC_yw-gk6v5P2fIrW61ntniOcWWa4kxXlDBzIICNXALQRNi0py3SaoHRoM2V7NXOfVOOXWukw-2ZW_qNvZsUT9G78SROlPG778vbQEaof1a0abTBBO2MtDEd_xqJrN1u0SkjhIXGWoGex91aPAl8G0CXUHIbuHCt-6ibQ_C7VtWIa_DGml1BCflCmf0BhUBp_Kc8UeLjqortM0PvBGFb_779v8-LWt9lBGzCW1OeTs9NtdH8gxKR7L9F6N1-4V4C6OvM62DdG3-96QP0FIi05Fg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+survey+on+statistical+methods+for+health+care+fraud+detection&rft.jtitle=Health+care+management+science&rft.au=Li%2C+Jing&rft.au=Huang%2C+Kuei-Ying&rft.au=Jin%2C+Jionghua&rft.au=Shi%2C+Jianjun&rft.date=2008-09-01&rft.issn=1386-9620&rft.eissn=1572-9389&rft.volume=11&rft.issue=3&rft.spage=275&rft.epage=287&rft_id=info:doi/10.1007%2Fs10729-007-9045-4&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s10729_007_9045_4 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1386-9620&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1386-9620&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1386-9620&client=summon |