Classification in the detection of health care fraud and abuse
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Classification in the Detection of Health Care Fraud and Abuse. 2004/10/21. Outline. NHI program in Taiwan BNHI Health care fraud and abuse problem Detection approach Feature selection Classification Detection result. NHI program in Taiwan. The National Health Insurance (NHI) program:

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Classification in the Detection of Health Care Fraud and Abuse

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Classification in the Detection of Health Care Fraud and Abuse

2004/10/21


Outline

  • NHI program in Taiwan

    • BNHI

    • Health care fraud and abuse problem

  • Detection approach

    • Feature selection

    • Classification

  • Detection result


NHI program in Taiwan

  • The National Health Insurance (NHI) program:

    • Developed in 1995

    • Administered by the Bureau of National Health Insurance (BNHI).

    • Provide the public comprehensive medical care

    • More than 21 million individuals are enrolled in the program (with coverage rate 96%)

    • More than 92% health institutions are contacted in the program


NHI program in Taiwan

  • The Fee-for-Service payment method is mainly used between 1995 to 2001.

  • Fee-application process

  • Hence, reviewing hospitals’ applications becomes an important work for BNHI


Detection approach

  • Common health care fraud and abuse:

    • Billing services that are never rendered,

    • Performing more expensive services,

    • Performing unnecessary medical services,

    • Misrepresenting non-covered treatments as necessary covered treatments,

    • Falsification of patients diagnosis/treatment history.


Detection approach

  • Since health care fraud and abuse often result in unauthorized benefit, indicators derived from expense may have the detection power.


Detection approach

  • Model development

    • Data are collected from a regional-level hospital between July 2001 to June 2002,

    • 906 normal PID instances are collected,

    • 906 fraudulent PID instances are also collected,

    • ID3 (C4.5) algorithm is used to construct decision rules.


Detection approach


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