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Data-Driven Implementation to Filter...

Suleiman, Muhammad Nader.

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Data-Driven Implementation to Filter Fraudulent Medicaid Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-Driven Implementation to Filter Fraudulent Medicaid Applications./
作者:	Suleiman, Muhammad Nader.
面頁冊數:	86 p.
附註:	Source: Masters Abstracts International, Volume: 53-01.
Contained By:	Masters Abstracts International53-01(E).
標題:	Information Technology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1557900
ISBN:	9781303963650

Data-Driven Implementation to Filter Fraudulent Medicaid Applications.
Suleiman, Muhammad Nader.

Data-Driven Implementation to Filter Fraudulent Medicaid Applications. - 86 p.

Source: Masters Abstracts International, Volume: 53-01.

Thesis (M.S.)--North Carolina Agricultural and Technical State University, 2014.

This item must not be sold to any third party vendors.

There has been much work to improve IT systems for managing and maintaining health records. The U.S government is trying to integrate different types of health care data for providers and patients. Health care fraud detection research has focused on claims by providers, physicians, hospitals, and other medical service providers to detect fraudulent billing, abuse, and waste. Data-mining techniques have been used to detect patterns in health care fraud and reduce the amount of waste and abuse in the health care system. However, less attention has been paid to implementing a system to detect fraudulent applications, specifically for Medicaid. In this study, a data-driven system using layered architecture to filter fraudulent applications for Medicaid was proposed. The Medicaid Eligibility Application System utilizes a set of public and private databases that contain individual asset records. These asset records are used to determine the Medicaid eligibility of applicants using a scoring model integrated with a threshold algorithm. The findings indicated that by using the proposed data-driven approach, the state Medicaid agency could filter fraudulent Medicaid applications and save over $4 million in Medicaid expenditures.

ISBN: 9781303963650Subjects--Topical Terms:

1030799
Information Technology.

Data-Driven Implementation to Filter Fraudulent Medicaid Applications.
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