Two models to investigate Medicare fraud within unsupervised databases

Musal, R. Muzaffer

doi:10.1016/j.eswa.2010.06.095

Cited by 44 publications

(40 citation statements)

References 9 publications

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“…In most industry practices, data pre‐processing efforts, such as data cleaning and transformation, take most of the time of overall fraud detection procedure (see Lin & Haug, , and Sokol et al ., , for relevant discussions). For instance, being able to submit the same claim under the name of the hospital or provider may require the investigators to define new unique identifiers to analyse the medical data (Musal, ). Another crucial medical data issue is the abundance of missing values.…”

Section: Medical Claims Datamentioning

confidence: 99%

Statistical Medical Fraud Assessment: Exposition to an Emerging Field

Ekin

Ieva

Ruggeri

et al. 2018

Int Statistical Rev

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Health care expenditures constitute a significant portion of governmental budgets. The percentage of fraud, waste and abuse within that spending has increased over years. This paper introduces the emerging area of statistical medical fraud assessment, which becomes crucial to handle the increasing size and complexity of the medical programmes. An overview of fraud types and detection is followed by the description of medical claims data. The utilisation of sampling, overpayment estimation and data mining methods in medical fraud assessment are presented. Recent unsupervised methods are illustrated with real world data. Finally, the paper introduces potential future research areas such as integrated decision making approaches and Bayesian methods and concludes with an overall discussion. The main goal of this exposition is to increase awareness about this important area among a broader audience of statisticians.

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Section: Medical Claims Datamentioning

confidence: 99%

Statistical Medical Fraud Assessment: Exposition to an Emerging Field

Ekin

Ieva

Ruggeri

et al. 2018

Int Statistical Rev

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“…Second type of algorithms include clustering methods that are used to group providers or patients. For instance, Musal uses a hard‐clustering method of geographical regions as input to his regression model. While Ekin et al describe a Bayesian co‐clustering model that captures the dyadic dynamic that connects the providers and beneficiaries.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised Bayesian hierarchical method for medical fraud assessment

Ekin

LaKomski

Musal

2019

Statistical Analysis

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The increasing size and complexity of health care industry makes it attractive for fraudsters, therefore medical fraud assessment has gained more importance. Statistical methods can help medical auditors reveal fraud patterns within medical claims data. This paper proposes an unsupervised Bayesian hierarchical method as a prescreening tool to aid in medical fraud assessment. The proposed hierarchical model helps the investigators group medical procedures and identifies the hidden patterns among providers and medical procedures. Outlier detection and similarity assessment are conducted to analyze the billing differences among providers. We illustrate the utilization of the proposed method using U.S. Medicare Part B data and discuss the potential insights for medical audit decision‐making.

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“…Although a case study may be an instrument that helps to create a set of metrics, evaluation of the metrics by means of experts and flagging results is an absolute necessity. The set of metrics chosen in this paper consists of metrics based on analyzed cases from the FBI (U.S. Federal Bureau of Investigation 2013), metrics developed through discussions with healthcare fraud experts, and metrics found in existing literature (Musal 2010;Ng et al 2010;Shin et al 2012;Tang et al 2011; U.S. Government Accountability Office 2012). To understand the process of fraud metric extraction we illustrate two examples of fraud cases that helped to design identifying metrics.…”

Section: Compose Metric Sets For Domainsmentioning

confidence: 99%

“…There is a large base of statistical methods that are also used in other industries and could potentially be applied within the health care industry (Travaille et al 2011). Some research reported specific fraud scheme detection using data mining approaches (Forgionne et al 2000;Major & Riedinger 2002;Musal 2010;Ng et al 2010;Shin et al 2012), however an outstanding challenge is to explore other healthcare fields for potential data mining possibilities and develop a more applied approach to this problem.…”

Section: Introductionmentioning

confidence: 99%

“…The innovative aspects of the application concerned a method that could process the claims on a real time basis. Other examples are the application of association rule mining to examine billing patterns within a particular specialist group to detect these suspicious claims and potential fraudulent individuals (Shan et al 2008) or the use of clustering procedures as well as regression models for geographical analysis of possible fraud (Musal 2010). Ng et al (2010) experimented on detecting noncompliant consumers (prescription shoppers) in spatio-temporal health data of Medicare Australia using multiple metrics that flagged providers.…”

Section: Introductionmentioning

confidence: 99%

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Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data

Thornton

Capelleveen²,

Poel

et al. 2014

Proceedings of the 16th International Conference on Enterprise Information Systems

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Abstract:Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.

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Two models to investigate Medicare fraud within unsupervised databases

Cited by 44 publications

References 9 publications

Statistical Medical Fraud Assessment: Exposition to an Emerging Field

Statistical Medical Fraud Assessment: Exposition to an Emerging Field

An unsupervised Bayesian hierarchical method for medical fraud assessment

Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data

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