Perspect Health Inf Manag. 2022 Jan 1;19(1):1i. eCollection 2022 Winter.
Healthcare fraud is an expensive, white-collar crime in the United States, and it is not a victimless crime. Costs associated with fraud are passed on to the population in the form of increased premiums or serious harm to beneficiaries. There is an intense need for digital healthcare fraud detection systems to evolve in combating this societal threat. Due to the complex, heterogenic data systems and varied health models across the US, implementing digital advancements in healthcare is difficult. The end goal of healthcare fraud detection is to provide leads to the investigators that can then be inspected more closely with the possibility of recoupments, recoveries, or referrals to the appropriate authorities or agencies. In this article, healthcare fraud detection systems and methods found in the literature are described and summarized. A tabulated list of peer-reviewed articles in this research domain listing the main objectives, conclusions, and data characteristics is provided. The potential gaps identified in the implementation of such systems to real-world healthcare data will be discussed. The authors propose several research topics to fill these gaps for future researchers in this domain.
医疗保健欺诈在美国是一种代价高昂的白领犯罪，而且并非没有受害者。欺诈相关的成本以保费增加或受益人严重受损的形式转嫁给民众。因此，迫切需要开发数字医疗保健欺诈检测系统来应对这一社会威胁。由于美国各地的数据系统复杂且异构，以及医疗模式多样，因此在医疗保健中实施数字技术具有一定难度。医疗保健欺诈检测的最终目标是为调查人员提供线索，然后可以更仔细地检查这些线索，以便有可能进行追回、收回或转介给适当的当局或机构。本文描述并总结了文献中发现的医疗保健欺诈检测系统和方法。本文还提供了一份在该研究领域中经过同行评审的文章的列表，其中列出了主要目标、结论和数据特征。将讨论在将此类系统应用于实际医疗保健数据时发现的潜在差距。作者为该领域的未来研究人员提出了一些研究课题，以填补这些空白。
