Fraudulent billing

fraudulent billing
Fraudulent billing

Fraudulent billing

According to the Institute of Medicine 2012 report, the U.S. healthcare system loses 10% of the health expenditure annually due to fraudulent billing. This includes unnecessary reimbursements, inefficient delivered healthcare costs, administrative costs, preventable opportunities that are missed and egregious pricing of services. Healthcare fraud is categorized into provider fraud, consumer fraud and payer fraud (Raghupathi & Raghupathi, 2014).

An example of fraudulent billing is Medicare Fraud. This includes submitting knowingly or forcing submission of false claims or misrepresentation of facts so as to obtain healthcare payment which otherwise one would not be entitled to. Other fraudulent behaviors include soliciting, offering or receiving bribes to induce or to reward referrals for services reimbursed by the federal healthcare systems.

These kinds of frauds range from individual to broad based operations by organizations. Other kinds of fraudulent billing includes charging for bounced appointments, billing services at higher level of complexity than what was actually provided for or billing for services that were not furnished or paying for deliveries that were never done (Fraud abuse, 2016).

The following article was chosen for this analysis is :-  Joudaki, H., Rashidian, A., Minaei-Bidgoli, B., Mahmoodi, M., Geraili, B., Nasiri, M., & Arab, M. (2016). Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study. International Journal of Health Policy and Management, 5(3), 165–172. http://doi.org/10.15171/ijhpm.2015.196

  According to this article, the  healthcare fraud  account for 10% of the healthcare expenditure. Despite the stringent laws established by the Congress, addressing the healthcare care fraud has become a challenge especially in light of these major economic changes in healthcare market place. According to this article, traditional methods of detecting healthcare fraudulent billing in healthcare are inefficient and time consuming. The article suggests that using automated methods as well as the statistical knowledge has led to the emergence of new branch of science known as Knowledge Discovery from Databases (KDD). One core processes is the Data mining (Joudaki et al., 2016).

 Through the data mining strategies, the third party payers such as insurance companies are able to obtain useful information from many claims and are also able to identify the smaller subsets of claims that need further assessment. The article explores the various theoretical and practical challenges associated with the healthcare fraud, as well as the merits of addressing healthcare fraudulent activities.

In addition, the article reviews studies conducted  through data  mining  to detect frauds in health care, where three quarters of the study indicates that data mining technique is an effective strategy in identifying, predicting and preventing fraudulent activities  in healthcare. The study concludes that data mining is an effective technique to streamline auditing processes towards the suspected group (Raghupathi & Raghupathi, 2014).

Defrauding the federal government and the medical cover programs is illegal. This could lead to criminal and civil liability and could lead to fines, hefty penalties or imprisonment.  The federal fraud and abuse laws that is used against the physicians includes; United States Criminal Code, False Claims Act (FCA), Physician Self-Referral law (Stark law), and the Social Security Act. 

These laws describe the administrative, civil and criminal remedies imposed by the government on entities that commit fraudulent billing or activities in Medicare program. Violations of these laws can also lead into exclusion in all federal healthcare programs, non payments of reimbursements, or exclusion from all federal healthcare programs (Fraud abuse, 2016).

References

Fraud abuse (2016). Medicare Fraud & abuse: prevention, detection and reporting. Retrieved from https://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts/downloads/Fraud_and_Abuse.pdf

Joudaki, H., Rashidian, A., Minaei-Bidgoli, B., Mahmoodi, M., Geraili, B., Nasiri, M., & Arab, M. (2016). Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study. International Journal of Health Policy and Management, 5(3), 165–172. http://doi.org/10.15171/ijhpm.2015.196

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2, 3. http://doi.org/10.1186/2047-2501-2-3

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