Healthcare Fraud Data Mining Methods: A Look Back and Look Ahead.
Healthcare fraud is an expensive, white-collar crime in the United States, and it is not a victimless crime. Costs associated with fraud are passed on to the population in the form of increased premiums or serious harm to beneficiaries. There is an intense need for digital healthcare fraud detection...
| Publicado en: | Perspectives in Health Information Management Vol. 19; no. 1; pp. 1 - 19 |
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| Autores principales: | , , , , |
| Formato: | review Journal Article |
| Publicado: |
American Health Information Management Association
Winter2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157531941&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157531941 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15594122 2QEL jtl: Perspectives in Health Information Management issn: 15594122 maglogo: N pubinfo: dt: Winter2022 vid: 19 iid: 1 pid: 6825 pub: American Health Information Management Association place: Chicago, Illinois artinfo: ui: 157531941 157531941 157531941 157531941 ppf: 1 ppct: 18 formats: fmt: @attributes: type: P tig: atl: Healthcare Fraud Data Mining Methods: A Look Back and Look Ahead. aug: au: Kumaraswamy, Nishamathi Markey, Mia K. Ekin, Tahir Barner, Jamie C. Rascati, Karen affil: Graduate student at the University of Texas at Austin College of Pharmacy sug: subj: Fraud Prevention and Control Data Mining Methods Health Services Health Care Costs Health Care Industry United States Billing and Claims Insurance, Health Medicaid Punishment Negligence United States Insurance, Health, Reimbursement ab: Healthcare fraud is an expensive, white-collar crime in the United States, and it is not a victimless crime. Costs associated with fraud are passed on to the population in the form of increased premiums or serious harm to beneficiaries. There is an intense need for digital healthcare fraud detection systems to evolve in combating this societal threat. Due to the complex, heterogenic data systems and varied health models across the US, implementing digital advancements in healthcare is difficult. The end goal of healthcare fraud detection is to provide leads to the investigators that can then be inspected more closely with the possibility of recoupments, recoveries, or referrals to the appropriate authorities or agencies. In this article, healthcare fraud detection systems and methods found in the literature are described and summarized. A tabulated list of peer-reviewed articles in this research domain listing the main objectives, conclusions, and data characteristics is provided. The potential gaps identified in the implementation of such systems to real-world healthcare data will be discussed. The authors propose several research topics to fill these gaps for future researchers in this domain. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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