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The recent overturning of the Chevron doctrine by the Supreme Court has created uncertainty in the regulatory landscape and will test the judiciary's expertise in specific subject matters. The Chevron doctrine, established in 1984, gave federal agencies broad interpretative authority, but its overturning will have a significant impact on the highly regulated healthcare industry. This change is expected to result in more legal challenges, less certainty in compliance, and slower rulemaking by agencies. It is important for individuals to stay informed and seek support from industry-specific organizations to navigate this regulatory transformation. Businesses and industries should also consider how this legal shift may benefit them, particularly if they have faced regulatory overreach or disadvantage from agency actions. [Extracted from the article]

On March 28, 2023, in United States ex rel. Martin v. Hathaway, the U.S. Court of Appeals for the Sixth Circuit applied a textualist approach when it held that to bring a False Claims Act (FCA) action “resulting from” a violation of the Anti-Kickback Statute (AKS), a plaintiff must show but-for causation. In 2018, in United States ex rel. Greenfield v. Medco Health Solutions, Inc., the U.S. Court of Appeals for the Third Circuit looked to the legislative history of the AKS, ultimately finding that the same phrase “resulting from” conveyed a more relaxed causation standard. This Comment argues that the Sixth Circuit employed the proper approach to statutory interpretation and that future litigants should plead a but-for causal relationship when bringing such claims under the FCA. By confining statutory interpretation to a textual analysis, lower courts will ideally exercise judicial restraint and defer to the legislature to amend the causal standard should Congress see fit to do so. [ABSTRACT FROM AUTHOR]

Context: The False Claims Act is the US federal government's primary tool for identifying and penalizing pharmaceutical fraud. The Department of Justice uses the False Claims Act to bring civil cases against drug manufacturers that allegedly obtain improper payment from federal programs. Methods: The authors searched the Department of Justice website for press releases published between 2006 and 2022 that announced fraud actions brought against drug companies. They then used the World Health Organization's Anatomical Therapeutic Classification index to identify the classes of prescription drugs implicated in fraud actions. Findings: During fiscal years 2006–2022, payments by six manufacturers amounted to more than 28% of total payments made as a result of federal False Claims Act actions. Nervous system and cardiovascular drugs were the classes of medications most commonly implicated in alleged fraud. Federal officials most frequently alleged that companies improperly promoted nervous system drugs and paid kickbacks to increase revenues from cardiovascular, antineoplastic and immunomodulating, and alimentary tract and metabolism drugs. Conclusions: Despite frequent pharmaceutical fraud settlements and penalties, incidence of alleged fraud among drug companies remains high. Alternative methods for preventing and deterring fraud could help safeguard our health systems and promote public health, and policy makers should ensure that effective fraud enforcement complements preventive public health regulation. [ABSTRACT FROM AUTHOR]

Self-measured blood pressure (SMBP) monitoring has been shown to lead to lower blood pressure and improved hypertension control. However, there are barriers to the widespread use of SMBP, including physicians' lack of confidence in patients' ability to measure their own blood pressure. A study found that patients can learn to accurately measure their blood pressure after watching a 3-minute video. Other barriers include the need for validated home blood pressure monitors, the recommendation to average multiple measurements, and the lack of insurance coverage for blood pressure devices. Despite these challenges, progress is being made in advancing SMBP, and initiatives are working to address these barriers. [Extracted from the article]

Purpose: This study aims to increase awareness and educate the reader about health-care fraud targeting seniors in the USA to help stakeholders better understand, recognize and prevent this type of fraud. Design/methodology/approach: This paper collects statistics on the current state of health care frauds committed against seniors, and examines related cases and laws. Findings: The authors find this type of fraud is highly prevalent and expected to increase. Current laws preventing this fraud from occurring are multifold and complex. While prevention strategies through law enforcement have been somewhat successful, a reduction in resources may put seniors at an increased risk in the years to come. Research limitations/implications: Without additional prevention strategies, the problem will likely escalate with a growing population of older adults. This study encourages further research into effective prevention strategies and methods to fight health-care fraud against seniors. Practical implications: Health-care fraud and its associated costs pose a significant threat to the society and economy of the USA. Reducing this fraud will not only reduce the costs to the US economy but also improve the physical and mental well-being of senior victims, reduce their mortality and hospitalization rates and improve the public trust placed to health-care providers. Originality/value: This study highlights how health-care fraud is committed against seniors. With the projected trend of an aging US population, educating stakeholders, increasing awareness and applying tools to protect seniors will be important to reduce the absolute scope of this problem in the future. [ABSTRACT FROM AUTHOR]

Healthcare fraud has become a common encounter in the healthcare finance industry. The financial security of healthcare payers and providers is seriously impacted by healthcare fraud. When incorrect or exaggerated medical services are invoiced for reimbursement, fraudulent healthcare claims result. The effective operation of the healthcare system depends on the detection of such fraudulent actions. This paper develops a healthcare claim fraud detection method based on ensemble learning. Stack ensemble learning algorithm performance is compared to that of methods such as multi-layer perceptron (MLP) classifier, support vector classifier (SVC), logistic regression (LR), and decision tree (DT) algorithm. Because of the healthcare data imbalance, the normal transaction is significantly higher than the fraudulent transaction. The machine learning (ML) algorithm performs poorly because imbalanced data causes it to be biased toward the majority class. As a result, the data is unsampled using the synthetic minority oversampling technique (SMOTE) technique to provide balanced data. The experimental results show that for the identification of healthcare claim fraud, the ensemble learning strategy greatly outperforms single learning algorithms. The stack ensemble learning outperforms all the area under the curve for the receiver-operating characteristic (AUC ROC) curves from various algorithms, and the AUC-ROC curve is determined to be producing results that are adequate for all approaches. [ABSTRACT FROM AUTHOR]

The following Thematic Issue presents new research on the nature and extent of a wide range of scams, cons, frauds, and deceptive activities, including sextortion, the use of ransomware, phishing, identity theft, Ponzi schemes, online shopping fraud, gift card scams, and health care fraud targeting elderly victims. Are Americans particularly vulnerable to these scams? And does the public actually admire individuals who get involved, in large part due to the positive portrayal of scammers, fraudsters, and con artists in both literature and cinema? The potential impact of literary and cinematic depictions of these scams on the public's view of both offending and victimization are considered, along with the government's ongoing attempts to censure a wide range of books and films that may influence the public's view of morality–and more recently, the public's view of our government– due to their content. [ABSTRACT FROM AUTHOR]

In health care, trust is a foundational concept. Patients must trust that their medical practitioners are competent to treat them. The trustworthiness of medical practitioners encourages patients to disclose intimate facts about their medical issues. Further, patients must trust health care providers to demonstrate impartial concern for the patients' well-being, also known as fidelity. In providing care, the needs of the patients, rather than financial incentives, must drive medical practitioners. Without this trust, patients may not cooperate with diagnosis and treatment. In addition to trusting providers, care outcomes are better if patients trust the health care system as a whole. This Essay examines the importance of the government's role in building and maintaining trust in health care providers and the health care system. Due to programs such as Medicare and Medicaid, the government is a "participant-payer" in the health care system as well as a "regulatorenforcer" of the system. As regulator-enforcer, the government has many laws and regulations aimed at promoting trustworthy conditions between patients, health care providers, and the health care system. For example, the Anti-Kickback Statute prohibits all health care providers that participate in federal health care programs from benefitting financially from referrals to other providers. It is a criminal law that has substantial penalties attached to it. While the government's efforts to promote trustworthy conditions as regulator-enforcer are not without criticism, most of the focus has been on the government's failure (as participant-payer) to design a payment system that properly incentivizes health care providers to deliver cost-efficient quality care that prioritizes the well-being of patients. Historically, Medicare and Medicaid have used a fee-for-service reimbursement mechanism which reimburses providers for every item or service provided. This incentivizes providers to increase the volume of care, which drives up the costs of providing health care without improving patient outcomes. Thus, fee-forservice reimbursement misaligns the incentives of providers because it serves as an enticement for providers to put their financial aspirations above their patients' well-being. The government's newest reimbursement method--value-based reimbursement--requires the government to pay for outcomes rather than volume of services. With value-based reimbursement, providers take on financial risk based on the quality of care they provide. Value-based reimbursement promotes relationships between providers and continuity of care. Thus, it also has the potential to increase trust in health care providers and the system as a whole because it takes away some of the improper financial incentives inherent in fee-for-service reimbursement. While value-based reimbursement is promising, it carries its own fraud risks, such as manipulation of quality data, which are not currently addressed by the fraud and abuse laws. This Essay maintains that if value-based reimbursement is going to be successful at realigning incentives, the government as regulator-enforcer must enact criminal fraud laws and regulations to address the fraud risks in value-based reimbursement. Without assurance that the government is closely monitoring fraud and protecting the interests of patients, patients may not trust value-based reimbursement which could ultimately undermine trust in providers and the health care system. [ABSTRACT FROM AUTHOR]

The article examines healthcare fraud via telehealth and telemedicine before and after the COVID-19 pandemic in the U.S. It describes telehealth fraud takedowns by the Department of Justice, telehealth laws that were implemented prior to the pandemic, as well as actions being taken by the government in response to the increase in the use of telehealth and telemedicine. It also discusses differences between telehealth and telemedicine and forms of telehealth and telemedicine fraud.

As the usage of credit cards has become more common in health care applications of everyday life, banks have found it very difficult to detect credit card fraud (CCF) systematically. The fraudulent activities should be identified and detected using new techniques. As a result, machine learning (ML) can help detect CCF in transactions while reducing the strain on financial institutions. This research aims to improve cybersecurity measures by detecting fraudulent transactions in datasets. The new classifier strategies cluster and classifier-based decision tree (CCDT), cluster and classifier-based logistic regression (CCLR), and cluster and classifier-based random forest (CCRF) are modeled in this research. The proposed strategies are applied to detect fraudulent health care activities. This research performed the preprocessing through the feature extraction, sampling, and transformation stages, and the proposed classifiers are simulated, and the results are analyzed. The significant results expected range of the proposed classifiers over the other methods are accuracy—(99.95%, 99.97%), precision—(99.96%, 99.98%), sensitivity—(99.9%, 100%), specificity—(99.8%, 100%). The parameters μ , location, the binary variable, cluster size, and decision tree sampling observations affect the classifiers' performance. CCRF and CCLR obtain the expected significant results than other existing methods. [ABSTRACT FROM AUTHOR]

Background: With the rapid growth of healthcare services, health insurance fraud detection has become an important measure to ensure efficient use of public funds. Traditional fraud detection methods have tended to focus on the attributes of a single visit and have ignored the behavioural relationships of multiple visits by patients. Methods: We propose a health insurance fraud detection model based on a multilevel attention mechanism that we call MHAMFD. Specifically, we use an attributed heterogeneous information network (AHIN) to model different types of objects and their rich attributes and interactions in a healthcare scenario. MHAMFD selects appropriate neighbour nodes based on the behavioural relationships at different levels of a patient's visit. We also designed a hierarchical attention mechanism to aggregate complex semantic information from the interweaving of different levels of behavioural relationships of patients. This increases the feature representation of objects and makes the model interpretable by identifying the main factors of fraud. Results: Experimental results using real datasets showed that MHAMFD detected health insurance fraud with better accuracy than existing methods. Conclusions: Experiment suggests that the behavioral relationships between patients' multiple visits can also be of great help to detect health care fraud. Subsequent research fraud detection methods can also take into account the different behavioral relationships between patients. [ABSTRACT FROM AUTHOR]

Separately, Dr. Capen was also charged with unnecessary prescriptions of a compounding cream formulation, targeted mostly at workers compensation cases [[3]]. Update: Dr. Capen's indictment was part of a major criminal investigation that involved a number of providers, with ongoing indictments. Although the allegations against Dr. Capen were never tested in a court of law before a jury, press coverage of the case alleged that Dr. Capen received at least USD 5 million in kickbacks to perform spine operations at Pacific Hospital [[1]]. In a document posted online, the Department of Justice (DOJ) said that Dr. Capen had received kickbacks in a "massive healthcare fraud" that entailed payments for medical services primarily through the California workers compensation system [[2]]. [Extracted from the article]

Telemedicine emerged as a lifeline during the COVID-19 pandemic. Although the technology existed long before the pandemic, its use was limited due to strict government regulations that limited reimbursement for telemedicine visits. In response to the pandemic, the Government waived many of its restrictions for the duration of the Public Health Emergency. These changes fueled the growth of telemedicine. The problem, however, is that telemedicine makes it easier to conduct fraud on a large scale because without in-person visits, medical providers can reach many more beneficiaries in a short period of time. Thus, the size and scale of typical health care fraud schemes, such as sending medically unnecessary durable medical equipment, is magnified. This type of fraud has been on the rise since 2016, and, with the relaxed rules for telemedicine reimbursement during the pandemic, there is a serious concern that there will be a sharp increase in telemedicine fraud. This Article examines the fraudulent practices in the telemedicine industry and the conditions that permit them to flourish. This Article critically assesses the changes to telemedicine coverage and their relationship to fraud. It examines the fraudulent practices through the lens of the fraud triangle to determine why telemedicine fraud occurs. After assessing the causes of telemedicine fraud, this Article argues that there is no need for additional criminal statutes to address telemedicine fraud. As the typical telemedicine scam involves the payment of kickbacks and billing for medically unnecessary treatment and services, the existing fraud laws such as the Anti-Kickback statute and the False Claims Act are sufficiently capacious to address the criminality involved in these cases. This Article also argues that in lieu of additional criminal statutes, the Government should focus on additional measures to prevent or detect telemedicine fraud because preventative measures are the best way to safeguard the integrity of federal health care programs. [ABSTRACT FROM AUTHOR]

Health care prescription fraud and abuse result in major financial losses and adverse health effects. The growing budget deficits of health insurance programs and recent opioid drug abuse crisis in the United States have accelerated the use of analytical methods. Unsupervised methods such as clustering and anomaly detection could help the health care auditors to evaluate the billing patterns when embedded into rule-based frameworks. These decision models can aid policymakers in detecting potential suspicious activities. This manuscript proposes an unsupervised temporal learning-based decision frontier model using the real world Medicare Part D prescription data collected over 5 years. First, temporal probabilistic hidden groups of drugs are retrieved using a structural topic model with covariates. Next, we construct combined concentration curves and Gini measures considering the weighted impact of temporal observations for prescription patterns, in addition to the Gini values for the cost. The novel decision frontier utilizes this output and enables health care practitioners to assess the trade-offs among different criteria and to identify audit leads. [ABSTRACT FROM AUTHOR]

This document is a retraction note for an article titled "Cybersecurity enhancement to detect credit card frauds in health care using new machine learning strategies." The publisher has retracted the article due to concerns about compromised editorial handling and peer review process, inappropriate references, and the article not being in scope of the journal or guest-edited issue. The publisher no longer has confidence in the results and conclusions of the article. The authors disagree with the retraction. The original article can be found online. [Extracted from the article]

Purpose: To identify the factors that influence whistleblowing behavior as it relates to health care insurance fraud in Jiangsu Province, China. Methods: To construct a factor model and formulate research hypotheses using the Motivation–Opportunity–Ability framework. We designed a questionnaire containing 24 items and distributed it on-site to 2081 respondents in Jiangsu Province, China. Afterward, we applied structural equation modeling to validate the research hypotheses. Results: Policy awareness negatively contributes to whistleblowing behavior, risk perception does not reduce the incentive to blow the whistle, and an inability to recognize fraud is another critical barrier to converting whistleblowing intentions into behavior. Conclusion: Practices that are likely to promote citizen whistleblowing on insurance fraud may focus on the constraints identified by the comprehensive Motivation–Opportunity–Ability framework. [ABSTRACT FROM AUTHOR]

Health care audits are crucial in managing the government insurance programs that are estimated to have losses amounting to billions of dollars every year. Statistical methods such as sampling have long been used to handle their size and complexity. Sampling from health care claims data can benefit from multi-stage approaches, especially when the evaluation of the tradeoffs between precision and cost is important. The use of decision models could facilitate health care auditors and policy makers make the best use of these sampling outputs. This paper proposes an integrated multi-stage sampling and decision-making framework that enables auditors address the tradeoffs between audit costs and expected overpayment recovery. We illustrate the framework and discuss insights utilizing a variety of overpayment scenarios for payment populations including U.S. Medicare Part B claims payment data. [ABSTRACT FROM AUTHOR]

To create the aura of legality, the Southern California scheme referenced at the top of this column created written agreements to indirectly funnel payments from the vendor to the practitioner. In one instance, the hospital owner sublet office space from a practitioner - ostensibly for storage needs - and disguised the kickback as a rental payment; the payment was well in excess of fair market rental values for comparable space [[10]]. Kickback Schemes and Drivers of Misconduct Kickback payments come in many forms. Starting in 2009, my colleagues and I (SAG) working in Long Beach, CA, USA noticed an alarming increase in the number of instrumented spine fusions performed on industrially injured workers. [Extracted from the article]

Statistical and machine learning methods have become paramount in order to handle large size claims data as part of health care fraud detection frameworks. Among these, predictive methods such as regression and classification algorithms are widely used with labeled data. However, the imbalanced nature of health care claims data and skewness of fraud distributions result with challenges in practical applications. This paper presents the use of various classification algorithms and data pre‐processing methods on claim payment populations and overpayment scenarios with different characteristics. It can help the health care practitioners evaluate the advantages and disadvantages of these analytical methods, and choose the right classification method and apply them properly for their specific circumstances. We utilize publicly available U.S. Medicare Part B health care claims payment data from the hospitals with a number of fraud label scenarios to demonstrate potential fraud patterns. We discuss the computational demand and accuracy of the methods. [ABSTRACT FROM AUTHOR]

Due to numerous healthcare scandals over recent years, the principles of good organizational governance and the introduction of best practices have become increasingly important in the healthcare sector. The concept of governance is no longer a reserve of senior leadership teams. Indeed, every member and level of the organization is, to a certain extent, now influenced by these principles. Organizational governance dilemma are often complex, involve many stakeholder groups and rarely have a clear solution to solve the root of the problem. Despite the increased importance of governance in the healthcare sector, governance continues to play a very small role in healthcare administration and management education. This article presents a module plan setting out the means to empower healthcare managers and leaders with the knowledge and skills necessary to identify and to address organizational governance issues in healthcare organizations. One of the main objectives of this module is to understand how to make organizational governance more effective through the use of best practices at different levels of a healthcare organization and involving diverse stakeholder groups. Additional details are provided on the structure of the module, the assessment methods and the pedagogic approach. [ABSTRACT FROM AUTHOR]

Introduction: The World Health Organization has suggested the solution to health system waste caused by incorrect billing and fraud is policing and prosecution. However, a growing body of evidence suggests leakage may not always be fraudulent or corrupt, with researchers suggesting medical practitioners may sometimes struggle to understand increasingly complex legal requirements around health financing and billing transactions, which may be improved through education. To explore this phenomenon further, we undertook a scoping review of the literature to identify the medical billing education needs of medical practitioners and whether those needs are being met.Methods: Eligible records included English language materials published between 1 January 2000 and 4 May 2020. Searches were conducted on MEDLINE, PubMed, Google Scholar, CINAHL, LexisNexis and Heinonline.Results: We identified 74 records as directly relevant to the search criteria. Despite undertaking a comprehensive, English language search, with no country restrictions, studies meeting the inclusion criteria were limited to three countries (Australia, Canada, US), indicating a need for further work internationally. The literature suggests the education needs of medical practitioners in relation to medical billing compliance are not being met and medical practitioners desire more education on this topic. Evidence suggests education may be effective in improving medical billing compliance and reducing waste in health systems. There is broad agreement amongst medical education stakeholders in multiple jurisdictions that medical billing should be viewed as a core competency of medical education, though there is an apparent inertia to include this competency in medical education curricula. Penalties for non-compliant medical billing are serious and medical practitioners are at risk of random audits and investigations for breaches of sometimes incomprehensible, and highly interpretive regulations they may never have been taught.Conclusion: Despite acknowledged significance of waste in health systems due to poor practitioner knowledge of billing practices, there has been very little research to date on education interventions to improve health system efficiency at a practitioner level. [ABSTRACT FROM AUTHOR]

Our study analyzes data from the Corporate Prosecution Registry of the University of Virginia School of Law and Duke University School of Law. This registry provides information on federal organizational prosecutions in the United States, including detailed information about every federal organizational prosecution since 2001, as well as deferred and non-prosecution agreements with organizations since 1990. We examine a subset of corporate violators, namely those who allegedly committed five types of fraud, namely, accounting fraud, mail fraud and wire fraud prosecutions, health care fraud, securities fraud, and tax fraud. We utilize the framework of bounded rationality of decision making to hypothesize that prosecutors are influenced by internal and external factors that affect the total penalties ultimately levied on corporate violators. Specifically, our results indicate that corporate penalties are significantly more likely to be lower when U.S. Department of Justice sections are involved in the prosecution and the company's country of incorporation has a Free Trade Agreement with the U.S. but significantly more likely to be higher when the violator is a U.S. public company. • Prosecutors are influenced by internal and external factors when imposing corporate penalties. • Penalties on corporate fraud violators are lower when Department of Justice sections prosecute. • Penalties on corporations registered in countries with Free Trade Agreements with the U.S. are significantly lower. • Penalties on U.S. public companies are significantly higher. [ABSTRACT FROM AUTHOR]

Objective: To illustrate a method that accounts for sampling variation in identifying suppliers and counties with outlying rates of a particular pattern of inconsistent billing for ambulance services to Medicare. Data Sources: US Medicare claims for a 20% simple random sample of 2010-2014 fee-for-service beneficiaries. Study Design: We identified instances in which ambulance suppliers billed Medicare for transporting a patient to a hospital, but no corresponding hospital visit appeared in billing claims. We estimated the distributions of outlier supplier and county rates of such "ghost rides" by fitting a nonparametric empirical Bayes model with flexible distributional assumptions to account for sampling variation. Data Collection: We included Basic and advanced life support ground emergency ambulance claims with a hospital destination. Principal Findings: "Ghost ride" rates varied considerably across both ambulance suppliers and counties. We estimated 6.1% of suppliers and 5.0% of counties had rates that exceeded 3.6%, which was twice the national average of "ghost rides" (1.8% of all ambulance transports). Conclusions: Health care fraud and abuse are frequently asserted but can be difficult to detect. Our data-driven approach may be a useful starting point for further investigation. [ABSTRACT FROM AUTHOR]