Literature DB >> 18826005

A survey on statistical methods for health care fraud detection.

Jing Li1, Kuei-Ying Huang, Jionghua Jin, Jianjun Shi.   

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.

Mesh:

Year:  2008        PMID: 18826005     DOI: 10.1007/s10729-007-9045-4

Source DB:  PubMed          Journal:  Health Care Manag Sci        ISSN: 1386-9620


  3 in total

1.  Using Bayesian networks to analyze expression data.

Authors:  N Friedman; M Linial; I Nachman; D Pe'er
Journal:  J Comput Biol       Date:  2000       Impact factor: 1.479

2.  Data preparation framework for preprocessing clinical data in data mining.

Authors:  Jau-Huei Lin; Peter J Haug
Journal:  AMIA Annu Symp Proc       Date:  2006

3.  Critical pathways: effectiveness in achieving patient outcomes.

Authors:  C L Ireson
Journal:  J Nurs Adm       Date:  1997-06       Impact factor: 1.737

  3 in total
  13 in total

1.  Computer-aided auditing of prescription drug claims.

Authors:  Vijay S Iyengar; Keith B Hermiz; Ramesh Natarajan
Journal:  Health Care Manag Sci       Date:  2013-07-03

2.  Multi-stage methodology to detect health insurance claim fraud.

Authors:  Marina Evrim Johnson; Nagen Nagarur
Journal:  Health Care Manag Sci       Date:  2015-01-20

3.  Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain.

Authors:  Patrick J Tighe; Christopher A Harle; Robert W Hurley; Haldun Aytug; Andre P Boezaart; Roger B Fillingim
Journal:  Pain Med       Date:  2015-05-29       Impact factor: 3.750

4.  Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study.

Authors:  Hossein Joudaki; Arash Rashidian; Behrouz Minaei-Bidgoli; Mahmood Mahmoodi; Bijan Geraili; Mahdi Nasiri; Mohammad Arab
Journal:  Int J Health Policy Manag       Date:  2015-11-10

5.  Healthcare Fraud Data Mining Methods: A Look Back and Look Ahead.

Authors:  Nishamathi Kumaraswamy; Mia K Markey; Tahir Ekin; Jamie C Barner; Karen Rascati
Journal:  Perspect Health Inf Manag       Date:  2022-01-01

6.  The State of Data in Healthcare: Path Towards Standardization.

Authors:  Keith Feldman; Reid A Johnson; Nitesh V Chawla
Journal:  J Healthc Inform Res       Date:  2018-05-22

7.  Multicriteria decision frontiers for prescription anomaly detection over time.

Authors:  Babak Zafari; Tahir Ekin; Fabrizio Ruggeri
Journal:  J Appl Stat       Date:  2021-07-31       Impact factor: 1.416

8.  Detecting fraud, waste, and abuse in substance use disorder treatment.

Authors:  Melissa M Garrido; David K Jones; Alexander Woodruff; Kiersten Strombotne; Sivagaminathan Palani; Sarah Zahakos; Michael Adelberg; Steven D Pizer; Austin B Frakt
Journal:  Health Serv Res       Date:  2022-08-19       Impact factor: 3.734

Review 9.  No evidence of the effect of the interventions to combat health care fraud and abuse: a systematic review of literature.

Authors:  Arash Rashidian; Hossein Joudaki; Taryn Vian
Journal:  PLoS One       Date:  2012-08-24       Impact factor: 3.240

Review 10.  Using data mining to detect health care fraud and abuse: a review of literature.

Authors:  Hossein Joudaki; Arash Rashidian; Behrouz Minaei-Bidgoli; Mahmood Mahmoodi; Bijan Geraili; Mahdi Nasiri; Mohammad Arab
Journal:  Glob J Health Sci       Date:  2014-08-31
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