Examining Predictive Modeling–Based Approaches to Characterizing Health Care Fraud

Header: FOR CONFERENCE-RELATED PAPERS, REPLACE THIS LINE WITH YOUR SESSION NUMBER, E.G., AB-02 (DOUBLE-CLICK HERE)
Examining Predictive Modeling–Based Approaches
to Characterizing Health Care Fraud
Authors Name/s per 1st Affiliation (Author)
line 1 (of Affiliation): dept. name of organization
line 2: name of organization, acronyms acceptable
line 3: City, Country
line 4: e-mail address if desired
Authors Name/s per 2nd Affiliation (Author)
line 1 (of Affiliation): dept. name of organization
line 2: name of organization, acronyms acceptable
line 3: City, Country
line 4: e-mail address if desired
Abstract— This document presents a literature review on the
application of predictive-model based approaches for
characterization of healthcare frauds. This is a major concern in
the modern healthcare industry, where a group people are using
genuine data of the patients in fraudulent activities to increase
their revenue and profit. Large number of false claims and false
billings as well as various other practices are used in the
fraudulent activities in the health sector. Predictive analysis
under big data science is found to be effective in other sectors like
the insurance sector for detecting frauds. Thus, a literature
review has been conducted to show that implementation of
predictive modelling can be useful to identify the fraud patterns
and take efficient measures.
Keywords-Predictive modeling, big data analytics, healthcare
fraud, insurance fraud
I. INTRODUCTION
ealthcare fraud is a major concern factor in today’s
world. It is happening in almost all over the world and
the healthcare fraud may result in spending of hundreds of
billions of dollar, which could have been better utilized on the
patient care [1]. It is often termed as a type of white-collar
crime involving dishonest health care claims for earning profit
[3]. Health care fraud has negative impact on the society as
well as on the economy, and hence, governments of all
countries across the world are taking rigorous measures to
prevent these fraud activities and mitigate the impact on the
people. In order to implement measures to detect healthcare
fraud and take measures, it is essential to analyze the
characteristics of the crime in a precise manner. This is
assumed to be beneficial to categorize the degree of fraudulent
activities in the health care and plan preventive measures and
penalizations accordingly. Data analytics is an effective way
to capture the incidences of health fraud and analyze those
information in a manner that helps to identify the pattern of
the crime and understand the characteristics [2]. Various
techniques under data analytics can be used for assessing the
characteristics of the health fraud. However, this paper will
discuss the effectiveness of Predictive Modeling–Based
Approaches for analyzing the features of the health care fraud
and taking efficient measures through a literature review. A
H
comprehensive discussion on the given topic is presented
below.
.
II. DISCUSSION
A. Healthcare fraud
Healthcare fraud is a social crime. Majority of this fraud is
usually committed by few dishonest healthcare providers.
Unfortunately, due to the fraudulent activities by a small
number of dishonest people affect the reputation of most
trusted as well as respected healthcare providers in the
community [4]. These people misuse their power as well as
people’s trust on them for committing the fraud on a large
scale. While conceiving the fraud schemes, these people often
bring on creative plans as they have access to a wide range of
variables, such as, the entire population of the patients;
complete range of the potential medical conditions as well as
treatment on the basis of which false claims are made; and the
scope of spreading the false billings among many insurers at
the same time. This includes the government’s medical
insurance or health care programs also [5]. This lowers their
chances of being identified by any single insurer. The most
common types of health care frauds include:
a) Billing for the services that were never provided and they
use genuine patient information, obtained through
identity theft, for fabricating the claims with the services
that did not take place;
b) False billing for more expensive procedures or services
than actually performed, known as ‘upcoding’ using
more critical disease and process code; performing
unnecessary medical services solely for increasing
revenue;
c) False diagnosis for justifying unnecessary tests or
surgeries, unbundling or billing each step of medical
process as those are separate process;
d) Billing a patient more than their co-pay amount which are
already prepaid by the plan and many more [5].
Thus, it can be understood that there are various ways that
healthcare fraud can take place and it is important to identify
0018-9464 © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. (Inserted by IEEE.)