MEDICARE FRAUD DETECTION




To analyze Medicare fraud, we will need the following datasets: The PartD 2013 Medicare data set available here. It contains all 2013 prescriptions aggregated at the physician and drug level, as well as information on the physician (National Physician ID (NPI), name, city, postal code,

Medicare fraud occurs when someone knowingly deceives Medicare to receive payment when they should not, or to receive higher payment than they should. Committing fraud is illegal and should be reported

Defrauding the Federal Government and its programs is illegal. Committing Medicare fraud exposes individuals or entities to potential criminal, civil, and administrative liability, and may lead to imprisonment, fines, and penalties.

Medicare beneficiaries may be denied Medicare benefits due to fraud. … If Medicare thinks such services were already provided to the beneficiary, Medicare may deny payment. 5. Medicare fraud, errors, and abuse can result in higher out-of-pocket costs for beneficiaries.

Unsupervised fraud detection in Medicare Australia
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Fraud detection is a fundamental data mining task with a wide range of practical applications. Finding rare and evolving fraudulent claimant behaviour in millions of electronic Medicare records poses unique challenges due to the unsupervised nature of the

A comprehensive study of healthcare fraud detection based on machine learning
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involved in data mining , . Hence, types of fraud healthcare data and methods for detecting frauds has to be The Centers for Medicare and Medicaid Services (CMS) releases healthcare data which is used by most of the researchers for healthcare fraud detection

Application of bayesian methods in detection of healthcare fraud
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co-clustering model is helpful to flag potential fraudulent activities by detecting unusual co Jin J., Shi, J., 200 A survey on statistical methods for health care fraud detection Health Care 1 275-287 Musal R., 20 Two models to investigate Medicare fraud within unsupervised

Healthcare fraud detection : A survey and a clustering model incorporating Geo-location information
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According to the Office of Management and Budget, in 20 about 9%, or around $47.9 billion of the USS Medicare expenditure was lost due to fraud1. Therefore, effective fraud detection is important for reducing the cost of health care system. Detecting health care fraud and For example, it is estimated that with Medicare alone, recovery for 10 or 15 % of expenses through fraud detection is possible (Munro 2014). Moreover, given that the traditional methods for detecting healthcare fraud are inefficient, the motivation for detecting and effectively

Outlier-based Health Insurance Fraud Detection for US Medicaid Data.
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Ng, KS et al.. Detecting Non-compliant Consumers in Spatio-Temporal Health Data: A Case Study from Medicare Australia A Medical Claim Fraud /Abuse Detection System based on Data Mining: A Case Study in Chile

Big Data Analytics and Engineering for Medicare Fraud Detection .
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Detection Institution: Florida Atlantic University Dissertation Advisor: Dr. Taghi M. Khoshgoftaar Degree: Doctor of Philosophy Year: The United States (US) healthcare system produces an enormous volume of data with a vast number of financial transactionsOverall, the Combined and Part B datasets produced the best fraud detection performance with no statistical difference between these our results and the assumption that there is no way to know within which part of Medicare a physician will commit fraud we suggest

Assessment of predictive modeling for identifying fraud within the Medicare program
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fraud detection algorithm for Medicare Part B services is $18.1 billion dollars for providers worthy of investigation that do not require accessing detailed medical records §§§ using a fraud detection algorithm for Medicare Part A services. Our annual estimate from this

MEDICARE FRAUD DETECTION USING SUPERVISED LEARNING
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Health care has grown to be a main expenditure in the US due to the fact 1980. Both the dimensions of the health care zone and the widespread volume of cash involved make it an attractive fraud goal. Therefore, powerful fraud detection is critical for decreasing the value of

Data mining techniques for auditing attest function and fraud detection
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Therefore, the need for efficient fraud detection solutions resides high on the to do list of every company payments resulting from outright fraud and abuse by program participants and/or management, health care programs, such as Medicare and Medicaid, financial assistance

A Medical Claim Fraud /Abuse Detection System based on Data Mining: A Case Study in Chile.
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intensity and health outcomes in the Medicare and Medicaid programs [29]. Cox [30] applied a fraud detection system based on fuzzy logic for analyzing health care provider claims. This fuzzy system uses rules derived from human experts for detecting anomalous behavior

Cascaded propensity matched fraud miner: Detecting anomalies in medicare big data
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Healthcare fraud is a serious cause of concern due to its unrestrained growth in funded medical aid plans around the globe. Apart from the monetary deficiencies caused by fraudulent practices, a greater challenge is the shortage of leftover funding that translates

Computational intelligence models for insurance fraud detection : a review of a decade of research
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425-440. [44] HI Weisberg and RA Derrig, Quantitative methods for detecting fraudulent automobile 116-120. [55] S. Zhu, Y. Wang, and Y. Wu, Health care fraud detection using nonnegative [56] RM Musal, Two models to investigate Medicare fraud within unsupervised

A Framework for Comprehensive Fraud Management using Actuarial Techniques
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5.2 Stage 2: Fraud Detection The important step in fraud detection is the identification of suspicious activities that have a higher probability of being fraudulent. Detecting an insurance fraud and abuse requires an in-depth knowledge of the insurance industry

An Evaluation of Unsupervised Machine Learning Algorithms for Detecting Fraud and Abuse in the US Medicare Insurance Program
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sion [64]. Our group at Florida Atlantic University was, again, the first and only to apply autoencoders with the US Medicare data provided by the Centers for Medi- care and Medicaid Service . As mentioned on previous LOF section, Bauder and Khoshgoftaar [28] compared

An improved approch for fraud detection in health insurance using data mining techniques
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Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient on up coding, treatment intensity and health outcomes in the Medicare and Medicaid Cox applied a fraud detection system based on fuzzy logic for analyzing health care provider

Validating a Novel Health Insurance Fraud Detection Method
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Detecting health care fraud has the potential to improve value in health care, especially in social insurance programs such as Medicaid and Medicare . The savings from health care fraud detection can be redirected for expansion of other types of care

Medicaid and Medicare Fraud Waste and Abuse
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Federal and state governments are also detecting and reclaiming money back on a larger investigative and program integrity funding, and technological improvements to fraud detection programs, databases bodies, particularly in regard to the Medicaid and Medicare programsCombined, this suggests that these three datasets actually be sub-domains requir- ing unique models within the Medicare fraud detection domain We adopt an existing model for detecting Medicare fraud as our frame of refer- ence . This model was selected out of several

Electronic fraud detection in the US Medicaid Health Care Program
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Although the differences between the programs Medicaid and Medicare are significant, the detected fraud schemes appear in both programs (Sparrow). The fundamental reason is that the industry‟s standard detection and control systems are not aimed at criminal fraud

Mining medical specialist billing patterns for health service management
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that have applied a range of data mining techniques to the Medicare Australia data Pearson, Murray and Mettenmeyer 2005), K-nearest neighbour method for fraud detection (He, Graco There is very limited research on association rule mining in detecting anomalous patterns

System For Detection Of National Healthcare Insurance Fraud Based On Computer Application
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prevention teams competence on fraud prevention and detection should also be improved (Hartati, 2016; Jasri several data as follows: 1) Readmission was the most frequent event in potential fraud such as upcoding (Ardyanto); 2) Medicare penalizes hospitals

Data Mining Application to Healthcare Fraud Detection : A Two-Step Unsupervised Clustering Model for Outlier Detection with Administrative Databases
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Therefore, in order to design a model capable of detecting suspicious yet cautious providers, there was the need to obtain from a source of HDC data, complete information about (i) the behavior of the hospital, (ii) the characteristics of Data Mining for Healthcare Fraud Detection

Healthcare data sources and fraud research opportunities
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the Patient Protection and Accountable Care Act (ACA) of in combating Medicare fraud (2015) reviewed studies that performed data-mining techniques for detecting healthcare fraud algorithmic data mining without an emphasis on or application to fraud detection efforts inThis paper expands on the existing work on detecting non- compliant consumers within the health data held by Medi- care Australia [Ng et al.] Therefore, in our ap- proach we use a combination of different classifiers to model the Medicare consumer fraud detection

Application of Bayesian Co-clustering Methods in Healthcare Fraud Detection
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However, audits which are generally done by domain experts have not been very useful in detecting not obvious Overall, the use of Bayesian co-clustering methods is promising in the area of healthcare fraud detection Center for Medicaid and Medicare Services

Detecting hospital fraud and claim abuse through diabetic outpatient services
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Lett 22(1):55 61 3. Bolton R, Hand D (2002) Statistical fraud detection : a review 6. Guisseppi F, Gangopadhyay A, Adya M (2000) Intelligent data mining system to detect healthcare fraud 8. Bureau of National Health Insurance (2004) Report on quality of medicare for diabetes

Ontology-guided Policy Information Extraction for Healthcare Fraud Detection
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The Centers for Medicare Medicaid Services have developed a fraud detection system that analyses claims to identify providers with suspicious billing patterns . This and similar approaches rely on a combination of applying data analytics techniques to patterns in

Fraud in health care and organized crime
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Billions of dollars are being lost in Medicare and Medicaid programs to waste, fraud and abuse. Fraud has been characterized by the FBI as intentional deception or misrepresentation of facts to move from state to state and business to business in an attempt to avoid detection

Preserving life and health by preventing fraud in healthcare
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justice. gov/opa/pr/three-individuals-charged-1- billion- medicare fraudand-money-laundering- scheme Outlier detection in healthcare fraud : A case study in the Medicaid dental domain. International Journal of Accounting Information Systems, 2 18-31

Medicare Fraud Strike Force Teams Turn Up The HEAT
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The Seniors and Taxpayers Obligation Protection Act (STOP) would prohibit the use of Social Security numbers as the beneficiary identifier on Medicare cards and seek to improve HHSs fraud detection methods and scrutinize billing statements. 7 Senators MelHealthcare-related programs such as Medicaid and Medicare are apparently the most significant we can re- duce the false positive rate, reduce cost of detecting fraud and increase to con- tinue our collaboration with fraud investigators to develop new fraud detection capabilities

Feds Recovered $2.5 Billion From Medicare Fraud
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The law increases penalties for fraud and puts more emphasis on real-time detection of fraud and abuse as For years, weve tolerated health care fraud she said With penalties and settlements, $2.5 bil- lion was returned to the Medicare Trust Fund and $441 million to Medicaid

Detection of anomalous activity in diabetic patients using graph-based approach
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in the state of Tennessee who were enrolled in Medicare in as a starting point for demon- strating the application of graph-based anomaly detection to the problem of health care fraud . The next section of this paper presents existing research on detecting health care fraud

Eradicating Fraud in Healthcare: Possibly a Matter of Life or Death
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Retrieved from https://www.justice.gov/opa/pr/three-individuals-charged-1-billion- medicare fraudand-money-laundering-scheme Department of Justice [DoJ]. (2016b) Outlier detection in healthcare fraud : A case study in the Medicaid dental domain

Healthcare Fraud and Abuse: An Investigation of the Nature and Most Common Schemes
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governments enforcement efforts in this area. To date the governments efforts have been primarily directed towards the prevention and detection of fraud against the Medicare /Medicaid system. For example, in June 201 the

Medicare program integrity: Activities to protect Medicare from payment errors, fraud and abuse
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C Binder Congressional Research Service greenbook-waysandmeans.house Detection Heat mapping is a process used to identify geographic areas with increased potential of fraud activity. CMS contractors analyze complaints to Medicare toll free numbers and tips from providers and beneficiaries to target certain areas and potential fraud schemes

Large Scale Medical Database Mining: Abuse and Fraud Detection
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The Improper Medicare FFS Payments Report 2008. CMS Bonchi F, Giannotti F, Mainetto G, Pedreschi D. A classification-based methodology for planning auditing strategies in fraud detection 175-84. Cox E. A fuzzy system for detecting anomalous behaviors in

Health care fraud control: understanding the challenge
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The Special Investigative Units at Medicare contractors all save more than they cost, sev- eral are much better suited to detecting waste and abuse which does not amount to criminal fraud outliers within each provider category, leaving the bulk quite safe from detection even if

An Enhanced Fraud Detection Model using Neural Networks for Telecommunications and Smart Cards in Nigeria
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Johnson proposed a Medicare fraud detection system 3.3 Analysis of the Proposed System The proposed system is an enhanced fraud detection model using neural by Amanze . the proposed system is a reliable smartcard and telecommunication fraud detector due to

Using imbalanced triangle synthetic data for machine learning anomaly detection
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The third approach is the detecting model based on machine learning [Kulkarni, Pino, French et al. (2016); Bosman, Liotta, Iacca et al Zhang, W.; He, X. (2017): An anomaly detection method for medicare fraud detection . IEEE International Conference on Big Knowledge, pp

Medical fraud detection system in health insurance schemes using link and basket analysis algorithm
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optimized using a genetic algorithm was applied in a real world fraud detection problems faced Becker et al (2002) identified the effects of fraud control expenditures and hospital characteristics on upcoding, treatment intensity and health outcomes in the Medicare and Medicaid

Healthcare fraud management using big data analytics
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$4.1 billion stolen or otherwise improperly obtained from federal healthcare programs was recovered and returned to the Medicare Trust Funds Historical fraud detection methods only uncover about 10 percent of losses, and because of the post-payment nature of such methods

Medicare in the USA: a review of 45 years of health provision
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Under the recent healthcare reform, Medicare is introducing more fraud detection measures, including reminding recipients to review their quarterly service statements to see that they reflect the services they received. Additional

Fraud detection in the banking sector: a multi-agent approach
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success and failure. The authors claim that errors constitute an important issue in detecting fraud . They point out that errors are not necessarily the result of ignorance or lack of accuracy in a detection process. Errors of interpretation

A Comparative Study of Using Various Machine Learning and Deep Learning-Based Fraud Detection Models For Universal Health Coverage Schemes
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23] R. Bauder, R. da Rosa, and T. Khoshgoftaar, Identifying Medicare Provider Fraud 24] JE Lu Fletcher; Boritz, F. Lu, and J. Efrim Boritz, Detecting fraud in health S. Sai Mudigonda, PK Kandala, and PK Baruah, Implementation of a Predictive Model for Fraud Detection in Motor

Can Whistleblowers Root Out Public Expenditure Fraud Evidence from Medicare
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Privatization is a potentially effective way to both elicit private information useful for the detection of fraud and also to provide incentives to catch fraudsters Medicare provides an excellent setting to understand the effects of private enforcement on fraud more generally, as

Subject: Medicare Fraud and Abuse: Sununarv and Analvsis of Reforms in the Health Insurance Portabilitv and Accountabitv Act of 1996 and the Balanced
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Table Ll: Fraud and Abuse Funding and Contracting Table 1.2: Fraud Prevention and Detection Table r-3: Criminal Penalties Table L4: Civil Penalties Table 1.5: Sanctions for Managed Care and Medicare +Choice Organizations GAO/HEHS-9%18R HIPAA and BBA Reform

Closing the gates on Medicare billing fraud
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Page 12. 8 fraud is detected and prosecuted. Many government agencies are involved in the detection and prosecution of Medicare fraud while CMS also contracts out to various Medicare Program Safeguard Contractors, such as Trust Solutions, LLC. Page 13. 9

Alleviating Confusion and Preventing Fraud in the Medicare Part D Program
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Accessed October 2005. 9. Centers for Medicare Medicaid Services (CMS). Medicare fraud : Detection and prevention tips. Available at: www. medicare . gov/fraudabuse/tips.aspprinterfriendly =true. Accessed Octo- ber 2 2005. ■ Confusion and Fraud in Medicare Part D

Fraud Detection : Using Artificial Intelligence to Identify Suspicious Persons Over the Phone
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This method is not very reliable for detecting fraud as phone operators often make mistakes a psychology expert for further questioning if the operator suspects a threat of fraud . A more reliable system for detection would decrease unnecessary caller questioning, saving the time

An analytical approach to detecting insurance fraud using logistic regression
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An Analytical Approach To Detecting Page 3 related to loss-With the risk of generalization, this type of fraud fall among the older generation that can receive better medical benefits from an insurance company than they can from MEDICARE A Model For Fraud Detection

A Supervised Approach for Detection of Outliers in Healthcare Claims Data.
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The paper conducted experiments using a Python environment using pandas, numpy, matplot and scikit packages. The experiment uses two baseline algorithms for detecting the outliers Proc. Int. Conf IEEE, An Anomaly Detection Method for Medicare Fraud Detection

Use of Predictive Models to Mitigate Healthcare Fraud and Abuse
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detection methods for the current pay and chase strategy, or a denominator result. Regardless of the answer, the positive news is that members of CMS and legislators are willing to try new methods to mitigate healthcare fraud and abuse going forward. Year Total Medicare FFS

Rooting Out Fraud
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Medicare Contractors And Programs Technically speaking, PSCs and ZPICs are the MIP CMS strategies in meeting this goal include preventing fraud through effective enrollment and through education of providers and beneficiaries; early detection through medicalThe Certified Fraud Examiner The Certified Fraud Examiner (CFE) credential denotes proven expertise in fraud prevention, detection and deterrence Service, US Postal Inspectors, US Secret Service, federal and state Inspectors General, along with Medicare Fraud Control Units

MOX Report No. 28/2013
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card and telecommunications fraud where statistical methods have been commonly used (see ) for fraud detection there is lim- ited research in medical fraud assessment as a The Research Data Assistance Center [37] is a CMS (Center for Medicare Medicaid Services

The Institute for Integrating Statistics in Decision Sciences
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card and telecommunications fraud where statistical methods have been commonly used (see ) for fraud detection there is limited re- search in medical fraud assessment as a The Research Data Assistance Center [37] is a CMS (Center for Medicare Medicaid Services

HHS Pursues Mandate to Clamp Down on Fraud
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Potential Medicare or Medicaid providers will be categorized as at high, medium, or low risk of fraud at the time of enrollment The law in- creases penalties for fraud and puts more emphasis on real-time detection of fraud and abuse, she noted, as opposed to the current