國立虎尾科技大學 |

Fraud Detection in Healthcare.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Fraud Detection in Healthcare./
作者:	Chen, Song.
面頁冊數:	1 online resource (281 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:	Dissertation Abstracts International79-03B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355542769

Fraud Detection in Healthcare.
Chen, Song.

Fraud Detection in Healthcare. - 1 online resource (281 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Healthcare Fraud is an important problem that needs more attention from both the federal government and the health services communities. The United States loses at least $60 billion in healthcare fraud every year, and some organizations put the cost as high as 10% of the nation's total healthcare spending, which exceeds $2 trillion in 2014. The federal government relies on its contractors to combat health fraud and requires all insurance companies to have fraud units dedicated to detecting and investigating fraud.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355542769Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Fraud Detection in Healthcare.
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300 $a 1 online resource (281 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
500 $a Adviser: Aryya Gangopadhyay.
502 $a Thesis (Ph.D.) $c University of Maryland, Baltimore County $d 2017.
504 $a Includes bibliographical references
520 $a Healthcare Fraud is an important problem that needs more attention from both the federal government and the health services communities. The United States loses at least $60 billion in healthcare fraud every year, and some organizations put the cost as high as 10% of the nation's total healthcare spending, which exceeds $2 trillion in 2014. The federal government relies on its contractors to combat health fraud and requires all insurance companies to have fraud units dedicated to detecting and investigating fraud.
520 $a In this dissertation paper, we will discuss this research in analytical techniques of healthcare fraud detection. We propose a fraud detection framework which starts with understanding the fraud schemes and their feature sets. This framework is able to detect both known and unknown types of fraud. We explore physician relationships and convert them into a network structure, with which we build a few algorithms to detect relationship-based healthcare fraud. We explore and compare the Social Network Analysis and Predictive Modeling along with the benefits and challenges.
520 $a In this dissertation paper, we discuss 11 different healthcare fraud schemes, case examples and methods to detect them. We develop innovative new algorithms in Pattern Analysis (Chapter 3.4) to detect potential Hit and Run schemes and other suspicious billing behaviors. The community detection algorithms are discussed in Chapter Community Detections (Chapter 3.5) and Chapter Preliminary Work (Chapter 2.4 and 2.5).
520 $a To evaluate these algorithms, we develop a method to create a synthesized dataset of any number of providers, and test its similarities with real world healthcare claims (Chapter 4.1). We test the Pattern Analysis and show results in Chapter 4.2. Lastly, these community detection algorithms prove to be very effective by comparing running time and accuracy with other community detection algorithms (Chapter 4.3). My algorithms can be extended to detect communities of any sizes, and the success rate is better than other algorithms in overlapping communities.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Health care management. $3 1148454
650 4 $a Information science. $3 561178
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773 0 $t Dissertation Abstracts International $g 79-03B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10624524 $z click for full text (PQDT)