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Global Journal of Health Science; Vol. 7, No. 1; 2015
ISSN 1916-9736 E-ISSN 1916-9744
Published by Canadian Center of Science and Education
194
Using Data Mining to Detect Health Care Fraud and Abuse: A Review
of Literature
Hossein Joudaki1, Arash Rashidian1, Behrouz Minaei-Bidgoli2, Mahmood Mahmoodi3, Bijan Geraili4, Mahdi
Nasiri2 & Mohammad Arab1
1 Department of Health Management and Economics, School of Public Health, Tehran University of Medical
Sciences, Tehran, Iran
2 School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
3 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences,
Tehran, Iran
4 Mazandaran University of Medical Sciences, Mazandaran, Iran
Correspondence: Arash Rashidian, Department of Health Management and Economics, School of Public Health,
Tehran University of Medical Sciences, Poursina Ave, Tehran 1417613191, Islamic Republic of Iran. E-mail:
arashidian@tums.ac.ir
Received: June 16, 2014 Accepted: August 16, 2014 Online Published: August 31, 2014
doi:10.5539/gjhs.v7n1p194 URL: http://dx.doi.org/10.5539/gjhs.v7n1p194
Abstract
Inappropriate payments by insurance organizations or third party payers occur because of errors, abuse and fraud.
The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of
detecting health care fraud and abuse are time-consuming and inefficient. Combining automated methods and
statistical knowledge lead to the emergence of a new interdisciplinary branch of science that is named
Knowledge Discovery from Databases (KDD). Data mining is a core of the KDD process. Data mining can help
third-party payers such as health insurance organizations to extract useful information from thousands of claims
and identify a smaller subset of the claims or claimants for further assessment. We reviewed studies that
performed data mining techniques for detecting health care fraud and abuse, using supervised and unsupervised
data mining approaches. Most available studies have focused on algorithmic data mining without an emphasis on
or application to fraud detection efforts in the context of health service provision or health insurance policy.
More studies are needed to connect sound and evidence-based diagnosis and treatment approaches toward
fraudulent or abusive behaviors. Ultimately, based on available studies, we recommend seven general steps to
data mining of health care claims.
Keywords: health care, data mining, KDD, Business Intelligence, insurance claim, fraud
1. Introduction
1.1 Defining Fraud and Abuse
Inappropriate payments by insurance organizations or third party payers occur as a result of error, abuse or fraud.
Abuse describes provider’s practices that, either directly or indirectly, result in unnecessary costs to the payer.
Abuse includes any practice that is not consistent with the goals of providing patients with services that are
medically necessary, meet professionally recognized standards, and are fairly priced (Centers for Medicare and
Medicaid Services, 2012).
Health care fraud is an intentional deception used in order to obtain unauthorized benefits (Busch, 2007). Unlike
error and abuse, fraudulent behaviors are usually defined as a crime in law. However, there is no global
consensus on the definition of fraud and abuse in health care services or health insurance setting. For more
details and examples of fraud and abuse, please see Rashidian, Joudaki, and Vian (2012).
It is estimated that about 10 per cent of health care system expenditure is wasted due to fraud and abuse (Gee,
Button, Brooks, & Vincke, 2010). Therefore, the scale of health care fraud and abuse is large enough to make it a
priority issue for health systems.
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1.2 Emerging Data Mining for Better Detection of Health Care Fraud and Abuse
In traditional methods of health care fraud and abuse detection, a few auditors handle thousands of paper health
care claims. In reality, they have little time for each claim, focusing on certain characteristics of a claim without
paying attention to the comprehensive picture of a provider’s behavior (Rashidian et al., 2012). This method is
time-consuming and inefficient. It is still the dominant picture in many low-income and middle-income countries
(Copeland, Edberg, Panorska, & Wendel, 2013; Aral, Güvenir, Sabuncuoğlu, & Akar, 2012; Ortega, Figueroa, &
Ruz, 2006).
Electronic health records and growing use of computerized systems has led to newly emerging opportunities for
better detection of fraud and abuse. Innovations in machine learning and artificial intelligence bring attention to
automated methods of fraud detection. Combining automated methods and statistical knowledge led to a newly
emerging interdisciplinary branch of science that is named Knowledge Discovery from Databases (KDD). Data
mining is the core of the KDD process.
Data mining can help third-party payers such as health insurance organizations to extract useful knowledge from
thousands of claims and identify a smaller subset of the claims or claimants for further assessment and scrutiny
for fraud and abuse (Rashidian et al., 2012). In this way, the data mining approach is part of a more efficient and
effective IT-based auditing system.
2. Scope and Objectives of Our Study
We reviewed studies that achieved better detection of health care fraud and abuse by using data mining
techniques. We aimed to identify different approaches of data mining and applied data mining algorithms for
health care fraud detection. Our study does not cover financial fraud, which is not specific to the health care
providers. In addition, our study does not cover fraud detection in other fields such as credit card fraud, money
laundering, telecommunication fraud, computer intrusion and scientific fraud.
3. Related Works
Travaille, Müller, Thornton and Hillegersberg (2011) created an overview on fraud detection within other
industries, and how they can be applied within the healthcare industry. They mentioned 14 review studies that
have reviewed data mining methods in all fraud detection fields (Travaille, Müller, Thornton, & Hillegersberg,
2011). Also, we found two studies that have reviewed Knowledge Discovery from Databases (KDD) and data
mining in health care (Esfandiary, Babavalian, Moghadam, & Tabar, 2013; Yoo et al., 2012). Ultimately, we
found three studies that reviewed data mining methods in health care fraud detection (Liu & Vasarhelyi, 2013; Li,
Huang, Jin, & Shi, 2008; Furlan & Bajec, 2008).
Our study focused on primary researches that applied data mining methods in health care setting and health
insurance. We excluded studies that did not have original data (e.g. Thornton, Mueller, Schoutsen, & van
Hillegersberg, 2013; Ogwueleka, 2012; Ormerod, Morley, Ball, Langley, & Spenser, 2003).
4. Data Mining (DM), Knowledge Discovery from Databases (KDD) and Business Intelligence (BI)
Nowadays, data mining methods are the core part of the integrated Information Technology (IT) software
packages that are sometimes called “Business Intelligence” (BI) (Please see Chee et al. (2009) for a summary of
varied BI definitions and approaches to the definition of BI). Usually these IT-based systems have three layers,
starting with data warehousing, followed by On Line Analytical Process (OLAP) and ending with data mining
methods (that are the most advanced) (Fisher, Lauria, & Chengalur-Smith, 2012; Maimon & Rokach, 2010; Zeng,
Xu, Shi, Wang, & Wu, 2006).
In the first layer of analysis, the physician’s claims are compared with pre-computed aggregates along data
dimensions (predefined rules) and the system detects certain errors and inconsistency in claims. For example, the
price of a drug is defined 10 dollars and the system identifies all of the claims that containthis drug and also
break this rule. Reports that are generated by this layer of analysis can help to identify erroneous or incomplete
data input, duplicate claims, and services with no medical coverage (Li et al., 2008). Despite of the fact that
repeated or frequent errors are susceptible for abuse or fraud, the capability of this analysis layer for detection of
fraud and abuse is usually limited (Li et al., 2008).
In the second layer OLAP multi-level is performed (for example presenting the five physicians with the highest
rate of prescription of injectable antibiotics compared with the month before). However, providing solutions
when the user is unable to describe goals in terms of a specific query is impossible. These two layers of analysis
are often unsuccessful in detecting well-documented fraudulent claims and new patterns of fraud and abuse.
The third layer of analysis uses data mining techniques that are more sophisticated compare to the two previous
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layers. Data mining involves the use of methods that explore the data, develop relevant models and discover
previously unknown patterns in the data (Maimon & Rokach, 2010). For example, by using association rules and
induction methods one could understand physicians’ prescription behavior (or pattern) and then find which one
or group of physicians differ abnormally from the other physicians.
Some researchers have defined data mining as a key part of a broader term of Knowledge Discovery from
Databases (KDD) (Maimon & Rokach, 2010; Fayyad, Piatetsky-Shapiro, & Smyth, 1996). Maimon and Rokach
(2010) have defined KDD as an organized process of identifying valid, novel, useful, and understandable
patterns from large and complex datasets. They have defined Data Mining (DM) as a core of the KDD process,
involving the inferring of algorithms that explore the data, develop the model and discover previously unknown
patterns (Maimon & Rokach, 2010).
KDD involves several steps, starting from understanding the organization environment, determining obvious
objectives, understanding the data, cleaning, preparation and transformation of the data, selecting the appropriate
data mining approach, applying data mining algorithms, and evaluation and interpretation of the findings
(Rashidian et al., 2012; Maimon & Rokach, 2010). Some researchers have described similar steps as a data
mining process (Li et al., 2008). Others have described similar steps for BI (Zeng et al., 2006). Despite of the
fact that data warehousing experts, data mining experts, machine learning experts and other experts may view
these steps from their own viewpoints or emphasize on some steps as opposed to other steps, the logic and
essence of all of these terms is the same. They are all about learning. How a health care organization or insurance
organization learns about thousands of claims and makes informed and intelligent decisions. How an
organization develop a brain for itself to gather big and different data, analyze the data and respond timely and
accurately. We go forward with the data mining as a part of KDD process and KDD as a part of a border term of
BI. In our view, data mining is embedded in vertical solutions for KDD, BI and Decision Support Systems
(DHS).
5. Finding
5.1 Classification of Data Mining Methods
There are different classifications of data mining. It depends on the kinds of data being mined, the kinds of
knowledge being discovered and the kinds of techniques (algorithms) utilized. The most common and
well-accepted categorization that is used by machine learning experts divides data mining methods into
'supervised' and 'unsupervised' methods (Phua, Lee, Smith, & Gayler, 2010; Li et al., 2008; Bolton & Hand,
2002). Supervised methods attempt to discover the relationship between input variables (attributes or features)
and an output (dependent) variable (or target attribute). Unsupervised learning methods are applied when no
prior information of the dependent variable is available for use.
Supervised methods are usually used for classification and prediction objectives including traditional statistical
methods such as regression analysis, discriminant analysis, neural networks, Bayesian networks and Support
Vector Machine (SVM). Unsupervised methods are usually used for description including association rules
extraction such as Apriori algorithm and segmentation methods such as clustering and anomaly detection.
5.2 Supervised Data Mining Methods for Detecting Health Care Fraud and Abuse
In the domain of health care fraud and abuse detection, supervised data mining involves methods that use
samples of previously known fraudulent and non-fraudulent records. These two groups of records are used to
construct models, which allow us to assign new observations to one of the two groups of records. Supervised
methods require confidence in the correct categorization of the records. Furthermore, they are useful in detecting
previously known patterns of fraud and abuse. Hence, the models should be regularly updated to reflect new
types of fraudulent behaviors and changes in the regulations and settings (Rashidian et al., 2012). Examples of
the supervised methods that have been applied to health care fraud and abuse detection include decision tree
(Shin, Park, Lee, & Jhee, 2012; Liou, Tang, & Chen, 2008; William & Huang, 1997), neural networks (Liou et
al., 2008; Ortega et al., 2006; He, Graco, & Yao, 1997), genetic algorithms (He et al., 1999) and Support Vector
Machine (SVM) (Kirlidog & Asuk, 2012; Kumar, Ghani, & Mei, 2010) (Please see Table 1).
5.3 Unsupervised Data Mining Methods for Health Care Abuse and Fraud Detection
When fraudsters become aware of a particular detection method, they will adapt their strategies to avoid
detection (Sparrow, 1996). As we noted above, supervised methods are useful in detecting previously known
patterns of fraud and abuse. In theory, we can apply unsupervised approaches to identify new types of fraud or
abuse.
Unsupervised methods typically assess one claim’s attributes in relation to other claims and determine how they
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are related to or different from each other. Therefore, it can clear sequence and association rules between records,
distinguish anomaly record (s) or group similar records.
Examples of the unsupervised methods that have been applied to health care fraud and abuse are clustering (Liu
& Vasarhelyi, 2013; Ekina, Leva, Ruggeri, & Soyer, 2013; Tang, Mendis, Murray, Hu, & Sutinen, 2011; Musal,
2010; C. Lin, C.M Lin, Li, & Kuo, 2008; William & Huang, 1997), outlier detection (Capelleveen, 2013; Tang et
al., 2011; Shan, Murray, & Sutinen, 2009) and association rules (Shan, Jeacocke, Murray, & Sutinen, 2008)
(Please see Table 1).
5.4 Brief Review of Available Studies
We briefly explain some of the studies mentioned in section 5.2 and 5.3. Liou et al. (2008) used supervised
methods to review claims submitted to Taiwan’s National Health Insurance for diabetic outpatient services (Liou
et al., 2008). They selected nine expense-related variables and compared them in two groups of fraudulent and
non-fraudulent claims for building the detection models. The input variables were average drug cost, average
diagnosis fee, average amount claimed, average days of drug dispense, average medical expenditure per day,
average consultation and treatment fees, average drug cost per day, average dispensing service fees and average
drug cost per day. They compared three data mining methods including logistic regressions, neural networks and
classification trees for the detection of fraudulent or abusive behavior (Liou et al., 2008). They concluded that
while all three methods were accurate, the classification tree model performs the best with an overall correct
identification rate of 99% (Liou et al., 2008). Research by Yang and Hwang (2006) used supervised data mining
approach to assess whether the providers followed defined clinical pathways. They assumed that deviations from
clinical pathways could be an indication of fraudulent or abusive provision of care (Yang & Hwang, 2006).
Lin et al. (2008) applied unsupervised clustering methods on general physicians’ practice data of the National
Health Insurance in Taiwan (Lin et al., 2008). They used ten indicators (features or attributes) to cluster
physicians' practice data. The indicators were amount of fee, number of cases, amount of prescription days,
amount of visits per case, average consultation fee per case, average treatment fee per case, average drug fee per
case, average fee per case, percentage of antibiotic prescriptions, and percentage of injection prescriptions. They
identified and ranked critical clusters using expert opinions about the importance of clusters in affecting health
expenditures. Finally, they illustrated managerial guidance based on expert opinions about the characteristics of
each critical cluster (Lin et al., 2008). A Korean study aimed to identify abuse in 3705 internal medicine
outpatient clinics' claims (Shin, Park, Lee, & Jhee, 2012). This study gathered data from practitioner outpatient
care claims submitted to a health insurance organization. They calculated a risk score for indicating the degree of
likelihood of abuse by the providers; and then classified providers using a decision tree (Shin et al., 2012). As
advantages, Shin et al used a simple definition of anomaly score and extracted 38 features for detecting abuse.
They also provided a detailed explanation of the data mining process. Shan et al. (2009) used an outlier detection
approach to assess optometrists' claims to Medicare Australia based on methods introduced by Breunig et al.
(2000) (Shan et al., 2009). They calculated one single measure, the Local Outlier Factor (LOF), indicating the
degree of outlier-ness of each record. The complete definition and explanation of LOF can be found in the
Breunig et al. (2000). They used the optometrists' compliance history and feedback from experts to validate the
findings (Shan et al., 2009). In another study, association rules mining were applied to examine claims of
specialist physicians (Shan et al., 2008). The data was organized in transactions which were defined as all the
items claimed or billed for one patient on one day by one specialist. Association rules are statements of the form
if antecedent (s) then consequent (s). For example, if a physician prescribed drug A and drug B then he will
prescribe drug C with a likelihood of 98%. They identified 215 association rules. They considered the specialists
whose claims frequently broke the extracted rules as those with a higher risk of fraudulent behavior (Shan et al.,
2008). The Australia's Health Insurance Commission used an online-unsupervised learning algorithm
(SmartSifter) to detect outliers in the utilization of pathology services in Medicare Australia (Yamanishi,
Takeuchi, Williams, & Milne, 2004). Ekina et al. (2013) applied Bayesian co-clustering methods to identify
potentially fraudulent providers and beneficiaries who might have perpetrated a “conspiracy fraud” (Ekina et al.,
2013).
A study by Sokol, Garcia, Rodriguez, West, and Johnson (2001) explains the introductory steps of preparing and
visualizing the data. These steps should be followed in any data mining approach. Usually these precursory steps
need a large amount of work prior to the actual data mining. They used Health Care Financing Administration
claims related to preventative services of mammography, bone density assessment and diabetic counseling
(Sokol et al., 2001). Musal (2010) used Geo-location information and abnormally high utilization rates of
services as indicators of fraudulent behavior.
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5.5 Hybrid Supervised and Unsupervised Data Mining Methods
Hybrid methods of combining supervised and unsupervised methods also have been applied by some studies
(Please see Table 1). Major and Riedinger (2002) tested an electronic fraud detection program that compared
individual provider characteristics to their peers in identifying unusual provider behavior. Unsupervised learning
is used to develop new rules and improve the identification process (Major & Riedinger, 2002). One study
conducted a three step methodology for insurance fraud detection. They applied unsupervised clustering methods
on insurance claims and developed a variety of (labeled) clusters. Then they used an algorithm based on a
supervised classification tree and generated rules for the allocation of each record to clusters. They identified the
most effective 'rules' for future identification of abusive behaviors (Williams & Huang, 1997).
Table 1. Primary studies that used data mining for detecting health care fraud and abuse
Study Topic (Country) The first
author(year) Data mining approach Type of detected fraud Applied data mining technique
(s)
Healthcare fraud detection: A survey and a clustering
model incorporating Geo-location information (US) Liu (2013) Unsupervised Insurance subscribers’
fraud Clustering
Application of Bayesian Methods in Detection o
f
Healthcare Fraud (-) Ekina (2013)
Unsupervised
Conspiracy fraud which
involves more than one
party
Bayesian co-clustering
Unsupervised labeling of data for supervised learning
and its application to medical claims prediction (US) Ngufor (2013) Hybrid supervised and
unsupervised
Provider fraud (Obstetrics
claims)
Unsupervised data labeling and
outlier detection, classification
and regression
Outlier based predictors for health insurance fraud
detection within U.S. Medicaid (US) Capelleveen (2013) Unsupervised Provider fraud (Dental
claim data) Outlier detection
A scoring model to detect abusive billing patterns in
health insurance claims (Korea) Shin (2012) Supervised Provider fraud
(Outpatient clinics)
Six statistical techniques —
correlation analysis, logistic
regression and classification
tree
A fraud detection approach with data mining in health
insurance (Turkey) Kirlidog (2012) Supervised Provider fraud Support vector machine (SVM)
Applying Business Intelligence Concepts to Medicaid
Claim Fraud Detection (US) Copeland, (2012) Unsupervised Provider fraud Visualization by histogram
A prescription fraud detection model (Turkey) Aral (2012) Hybrid supervised and
unsupervised Prescription fraud Distance based correlation and
risked matrices
Unsupervised fraud detection in Medicare Australia
(Australia) Tang (2011) Unsupervised Insurance subscribers’
fraud
Clustering, feature selection and
outlier detection
Two models to investigate Medicare fraud within
unsupervised databases (US) Musal (2010) Unsupervised Provider fraud
Clustering algorithms,
regression analysis, and various
descriptive statistics
Data mining to predict and prevent errors in health
insurance claims processing (US) Kumar (2010) Supervised Error in providers claims Support vector machine (SVM)
Discovering inappropriate billings with local density
based outlier detection method (Australia) Shan (2009) Unsupervised Provider fraud
(Optometrists Billing)
Local density based outlier
detection
Mining medical specialist billing patterns
for health service management (Australia) Shan (2008) Unsupervised Provider fraud (Specialist
billing) Association rules
Detecting hospital fraud and claim abuse through
diabetic outpatient services (Taiwan) Liou (2008) Supervised Provider fraud (Diabetic
outpatient services)
Logistic regression, neural
network, and classification trees
A process-mining framework for the detection o
f
healthcare fraud and abuse (Taiwan) Yang (2006) Supervised Provider fraud
(Gynecology services)
Classification based on
associations algorithm, feature
selection by Markov blanket
filter
A medical claim fraud/abuse detection system based on
data mining: a case study in Chile (Chile) Ortega (2006) Supervised Provider fraud Neural network
EFD: A Hybrid Knowledge/Statistical-Based System for
the Detection of Fraud (US) Major (2002) Hybrid supervised and
unsupervised Provider fraud Outlier detection and rule
extraction
Application of Genetic Algorithms and k-Nearest
Neighbour method in real world medical fraud detection
problem (Australia)
He (1999) Unsupervised Provider fraud (General
practitioners)
Genetic algorithm and
K-Nearest Neighbor clustering
Evolutionary Hot Spots data mining: architecture for
exploring for interesting Discoveries (Australia). Williams (1999) Hybrid supervised and
unsupervised
Insurance subscribers’
fraud Clustering and rule induction
Mining the knowledge mine: The Hot Spots
methodology for mining large real world databases
(Australia)
William (1997) Hybrid supervised and
unsupervised
Insurance subscribers’
fraud
Clustering and C5.0
classification algorithm
Application of neural networks to detection of medical
fraud (Australia) He (1997) Supervised Provider fraud (General
practitioners)
Neural network
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6. Conclusion
Our review demonstrates that the terms KDD and data mining are interpreted differently in different studies.
These approaches contain an array of different methods and can be applied to different sets of problems
(Maimon & Rokach, 2010). Development of practical guides may improve the uptake and usage of the methods
and prevent errors and misuses of the techniques. Despite this limitation, the studies demonstrate that both
supervised and unsupervised techniques have important merits in discovering different fraud strategies and
schemes (Capelleveen, 2012).
Most of the identified literature focused more on the technical methods used in KDD and data mining, and paid
little attention to the practical implications of their findings for health care managers and decision makers. An
exception to this finding is the study by Lin et al. (2008) which provides a good example of a study that provides
managerial implications of their findings for dealing with health care fraud. To improve the uptake of KDD and
data mining methods, future studies should pay more attention to the policy implications of their findings.
It should be noted that fraud detection is only one part of a bigger program of combating health care fraud, abuse
and waste (Rashidian et al., 2012). Fraud detection should note the pitfalls that health care delivery policies can
create that might increase the possibility of fraud and abuse (Capelleveen, 2012). For example, fee for service
payments can increase the quantity of delivered services (Chaix-Couturier, Durand-Zaleski, Jolly, & Durieux,
2000). This may act as a risk factor for abuse, and perhaps fraud in health care.
While fraud and abuse detection in health care is not merely an issue related to payments, most of the attention is
towards frauds that result in unduly increasing costs and payments by the insurers. Further to this, any type of
care not based on evidence is potentially susceptible for abuse or waste. We found one study that applied this
logic to fraud detection (Yang & Hwang, 2006). More KDD research focusing on abuse resulting from
non-evidence based provision of care is needed.
Interestingly, we found no studies that applied data mining methods on health care data for detecting insurer or
payer fraud. Studies are needed to assess the potentials of these methods in detecting payer or insurer fraud.
We need more research on applying data mining methods in the context of low and middle-income countries.
Many such countries have weak IT-based auditing systems, making data mining more difficult, and are probably
more vulnerable to fraud and abuse. Where data is available, however, we think that low- and middle-income
countries can use data mining techniques as an instrument for evaluating provider’s behavior. Applying
unsupervised methods such as association rules induction and clustering are promising. These methods help
compare each provider with peer-groups. For example, applying Apriori (rules induction) algorithms in
prescription drugs of general physicians could result in a rule such as if a physician prescribed drug A and drug B
then he will prescribe drug C with likelihood of 98%. This rule has originated from the behavior of all physicians.
Hence, two per cent of physicians that break this rule should be investigated for the reasons behind this different
behavior of prescription.
In conclusion, we recommend seven general steps to mining health care claims (or insurance claim) to detecting
fraud and abuse (after preprocessing of data): 1). Identifying the most important attributes of data by expert
domains (Sokol et al., 2001; Li et al., 2008) 2). Defining new features that are indicators of fraudulent or abusive
behavior by expert domains or automated algorithms such as association rules induction (Li et al., 2008; Shan et
al., 2008) 3). Identifying unusual records by outlier detection methods for detailed investigation (Shan et al.,
2009) 4). Excluding outliers from the data and clustering (or re-clustering) records based on extracted features
(Lin et al., 2008) 5). Identifying outlier cluster (s) and investigating records in those clusters in more detail and
determining fraudulent or abusive records (e.g. by inspection) (Lin et al., 2008) 6). Designing supervised models
based on labeled records of previous step and selecting the most discriminative features (Liou et al., 2008) 7).
Applying supervised methods as a routine online processing task and applying unsupervised methods (outlier
detection and clustering) in specific time periods for refining the previous steps and detecting new cases of fraud.
Our recommended approach makes it possible to focus on a subset of claims instead of all claims, and is more
likely to be useful in low resources setting where computerized data may have severe limitations.
Acknowledgement
We thank the Tehran University of Medical Sciences for funding the study coded 17311.
Competing Interests
Not declared.
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