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美国波士顿代写Assignment 医疗领域的犯罪
2020-04-05 00:17
近年来,有组织犯罪在医疗领域有所增加。有组织犯罪涉及三人或三人以上。例如,Mirzoyan-Terdjanian组织窃取了几名医生的身份,并利用这些医生的信息提交虚假声明。结果,超过1亿美元的医疗保险索赔被支付给该组织。从2006年开始,美国25个州利用被盗的医生身份建立了医疗保险提供者账户,随后建立了假诊所。欺诈活动是通过识别不寻常的计费模式来识别的。该组织向联邦医疗保险(Medicare)提出的索赔中,包括了与医生的提供者类型不匹配的程序。索赔也因不寻常和昂贵的程序而提交。据联邦调查局统计,欺诈、浪费和滥用每年占美国医疗支出的3-10%。医疗保险占美国医疗支出的20%。大多数医疗欺诈是通过手动审计索赔和记录来识别的,以发现可疑的账单模式和虚假索赔。随着索赔的数量,医疗保险收到的手动审计是低效的。利用大量数据来识别模式和错误的过程称为数据挖掘,这与当前的技术进步更相关。在过去的几年里,医疗保险每年都提供“大数据”数据集,包括提供者信息、程序类型、支付金额、账单数量和其他各种关键数据。这些数据集的数据挖掘研究正在进行中。这些研究的结果将是打击欺诈的关键。
美国波士顿代写Assignment 医疗领域的犯罪
In recent years organized crime has increased in healthcare. Organized crime involves three or more people. For example, the Mirzoyan-Terdjanian Organization stole the identities of several physicians and filed false claims using those physician’s information. As a result, over $100 million dollars in Medicare claims were paid to the organization. Fake clinics were established after setting up Medicare provider accounts with the stolen physician identities starting in 2006 in 25 states across America. The fraudulent activity was identified by the recognition of unusual billing patterns. Claims the organization sent to Medicare included procedures that did not match the provider type for the physician. Claims were also filed for unusual and expensive procedures.Fraud, waste, and abuse, according to the FBI, account for 3-10% of U.S. healthcare spending annually. Medicare accounts for 20% of U.S. healthcare spending. The majority of healthcare fraud is identified by manually auditing claims and records to find suspicious billing patterns and false claims. With the volume of claims Medicare receives manually auditing is inefficient. Utilizing massive amounts of data to identify patterns and errors with a process called data mining is much more relevant with the current advances in technology. Medicare, over the last several years has provided “Big Data” datasets each year that include provider information, procedure types, payment amounts, quantities billed and various other key data. Data mining studies are ongoing of these datasets. The results of these studies will be key in the battle against fraud.
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