Quackery Research Articles

Fraud Detection in Healthcare Insurance Claims Using Machine Learning

Healthcare fraud is intentionally submitting false claims or producing misinterpretation of facts to obtain entitlement payments. Thus, it wastes healthcare financial resources and increases healthcare costs. Subsequently, fraud poses a substantial financial challenge. Therefore, supervised machine and deep learning analytics such as random forest, logistic regression, and artificial neural networks are successfully used to detect healthcare insurance fraud. This study aims to develop a health model that automatically detects fraud from health insurance claims in Saudi Arabia. The model indicates the greatest contributing factor to fraud with optimal accuracy. The labeled imbalanced dataset used three supervised deep and machine learning methods. The dataset was obtained from three healthcare providers in Saudi Arabia. The applied models were random forest, logistic regression, and artificial neural networks. The SMOT technique was used to balance the dataset. Boruta object feature selection was applied to exclude insignificant features. Validation metrics were accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC). Random forest classifiers indicated policy type, education, and age as the most significant features with an accuracy of 98.21%, 98.08% precision, 100% recall, an F1 score of 99.03%, specificity of 80%, and an AUC of 90.00%. Logistic regression resulted in an accuracy of 80.36%, 97.62% precision, 80.39% recall, an F1 score of 88.17%, specificity of 80%, and an AUC of 80.20%. ANN revealed an accuracy of 94.64%, 98.00% precision, 96.08% recall, an F1 score of 97.03%, a specificity of 80%, and an AUC of 88.04%. This predictive analytics study applied three successful models, each of which yielded acceptable accuracy and validation metrics; however, further research on a larger dataset is advised.

31. Health care fraud in spine surgery: a comprehensive review of medicolegal actions brought by the US Department of Justice

31. Health care fraud in spine surgery: a comprehensive review of medicolegal actions brought by the US Department of Justice

Towards a Secure Technology-Driven Architecture for Smart Health Insurance Systems: An Empirical Study.

Health insurance has become a crucial component of people's lives as the occurrence of health problems rises. Unaffordable healthcare problems for individuals with little income might be a problem. In the case of a medical emergency, health insurance assists individuals in affording the costs of healthcare services and protects them financially against the possibility of debt. Security, privacy, and fraud risks may impact the numerous benefits of health insurance. In recent years, health insurance fraud has been a contentious topic due to the substantial losses it causes for individuals, commercial enterprises, and governments. Therefore, there is a need to develop mechanisms for identifying health insurance fraud incidents. Furthermore, a large quantity of highly sensitive electronic health insurance data are generated on a daily basis, which attracts fraudulent users. Motivated by these facts, we propose a smart healthcare insurance framework for fraud detection and prevention (SHINFDP) that leverages the capabilities of cutting-edge technologies including blockchain, 5G, cloud, and machine learning (ML) to enhance the health insurance process. The proposed framework is evaluated using mathematical modeling and an industrial focus group. In addition, a case study was demonstrated to illustrate the SHINFDP's applicability in enhancing the security and effectiveness of health insurance. The findings indicate that the SHINFDP aids in the detection of healthcare fraud at early stages. Furthermore, the results of the focus group show that SHINFDP is adaptable and simple to comprehend. The case study further strengthens the findings and also describes the implications of the proposed solution in a real setting.

Learning from Highly Imbalanced Big Data with Label Noise

This study explores the effects of class label noise on detecting fraud within three highly imbalanced healthcare fraud data sets containing millions of claims and minority class sizes as small as 0.1%. For each data set, 29 noise distributions are simulated by varying the level of class noise and the distribution of noise between the fraudulent and non-fraudulent classes. Four popular machine learning algorithms are evaluated on each noise distribution using six rounds of five-fold cross-validation. Performance is measured using the area under the precision-recall curve (AUPRC), true positive rate (TPR), and true negative rate (TNR) in order to understand the effect of the noise level, noise distribution, and their interactions. AUPRC results show that negative class noise, i.e. fraudulent samples incorrectly labeled as non-fraudulent, is the most detrimental to model performance. TPR and TNR results show that there are significant trade-offs in class-wise performance as noise transitions between the positive and the negative class. Finally, results reveal how overfitting negatively impacts the classification performance of some learners, and how simple regularization can be used to combat this overfitting and improve classification performance across all noise distributions.

Tripartite Evolutionary Game and Simulation Analysis of Healthcare Fraud Supervision under the Government Reward and Punishment Mechanism.

This study aims to provide useful insights for the Chinese government in dealing with healthcare fraud by creating an evolutionary game model that involves hospitals, third-party entities, and the government based on the government reward and punishment mechanism. This paper analyzes the evolutionary stability of each participant's strategy choice, discusses the influence of each element on the tripartite strategy choice, and further analyzes the stability of the equilibrium point in the tripartite game system. The results show that (1) the government increasing fines on hospitals is conducive to compliant hospital operations, and the incentive mechanism has little effect on such operations; (2) the lack of an incentive mechanism for third parties results in false investigations by third parties; and (3) rewards from higher levels of government promote strict supervision by local governments, but that the high cost of supervision and rewards for hospitals inhibits the probability of strict supervision. Finally, Matlab 2020a is used for simulation analysis to provide a reference for the government to improve the supervision of healthcare fraud.

Understanding Health Fraud Offenders in China: An Emotional Labour Perspective

AbstractExisting literature has highlighted the importance of recognizing health fraud offenders’ intentional manipulations of victims’ vulnerabilities. However, the manipulation tactics of health fraud have received little attention. This study aims to extend the current understanding of health fraud by incorporating the concept of emotional labour. Nested in the Chinese context, which owns the most populous ageing population, three sets of qualitative data were collected, including 13 semi-structured interviews, 233 judicial documents, and 197 media reports. The findings suggest that health fraud offenders utilized three types of emotional labour as means of committing crime: including the labour of anxiety relief, the labour of filial piety, and the labour of social networking. This article not only provides novel insights into understanding health fraud, but also contributes to introducing the concept of emotional labour in criminological and socio-legal studies.

Health Fraud against the Elderly in China: The Perspective of Vulnerability Manipulation

ABSTRACT The aging-related vulnerabilities have long been regarded as salient factors affecting older adults’ health fraud victimization. Traditional wisdom tends to focus on how victims’ vulnerability facilitates victimization. Despite its insights, such a focus neglects the importance of offenders’ active manipulation and exploitation of victims’ vulnerability, which may lead to victim blaming. Using data collected from 233 Chinese judgment documents (the court records of the crime) and 13 interviews with secondary victims – family members or close relatives of the (primary) victims, this paper examines how three aging-related vulnerabilities, declined ability in information processing, comfort-driven social preference, and positivity-driven emotional preference, were deliberately manipulated by health fraud offenders. This study provides the latest empirical evidence on how health fraud against the elderly occurs in China and sheds new light on vulnerability manipulation from the socio-emotional selectivity theory.

Data-Centric AI for Healthcare Fraud Detection.

Automated methods for detecting fraudulent healthcare providers have the potential to save billions of dollars in healthcare costs and improve the overall quality of patient care. This study presents a data-centric approach to improve healthcare fraud classification performance and reliability using Medicare claims data. Publicly available data from the Centers for Medicare & Medicaid Services (CMS) are used to construct nine large-scale labeled data sets for supervised learning. First, we leverage CMS data to curate the 2013-2019 Part B, Part D, and Durable Medical Equipment, Prosthetics, Orthotics, and Supplies (DMEPOS) Medicare fraud classification data sets. We provide a review of each data set and data preparation techniques to create Medicare data sets for supervised learning and we propose an improved data labeling process. Next, we enrich the original Medicare fraud data sets with up to 58 new provider summary features. Finally, we address a common model evaluation pitfall and propose an adjusted cross-validation technique that mitigates target leakage to provide reliable evaluation results. Each data set is evaluated on the Medicare fraud classification task using extreme gradient boosting and random forest learners, multiple complementary performance metrics, and 95% confidence intervals. Results show that the new enriched data sets consistently outperform the original Medicare data sets that are currently used in related works. Our results encourage the data-centric machine learning workflow and provide a strong foundation for data understanding and preparation techniques for machine learning applications in healthcare fraud.

Medicare Advantage? If You Say So. Fraud, Waste, And Abuse In Medicare Part C

Debates about health care often emphasize distributive justice. How should society allocate finite resources? Who will get access to them, who will not? Is the allocation fair? Who decides and by what standard? Answers to these questions rarely consider health care fraud, waste, and abuse. This is a material omission. Fraud alone costs the health care system billions of dollars annually, dissipating limited funds and degrading quality of care. This paper considers how fraud, waste, and abuse occur in Medicare Part C – better known as Medicare Advantage – an increasingly popular coverage option. Medicare Advantage experiences unique challenges that undermine the program and squander the public’s investment in it. Only by accounting for these programmatic vulnerabilities can we then go on to assess whether Medicare Advantage is an effective, cost-efficient, and equitable mechanism for delivering health insurance coverage.

Deceptive Tricks in Artificial Intelligence: Adversarial Attacks in Ophthalmology.

The artificial intelligence (AI) systems used for diagnosing ophthalmic diseases have significantly progressed in recent years. The diagnosis of difficult eye conditions, such as cataracts, diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity, has become significantly less complicated as a result of the development of AI algorithms, which are currently on par with ophthalmologists in terms of their level of effectiveness. However, in the context of building AI systems for medical applications such as identifying eye diseases, addressing the challenges of safety and trustworthiness is paramount, including the emerging threat of adversarial attacks. Research has increasingly focused on understanding and mitigating these attacks, with numerous articles discussing this topic in recent years. As a starting point for our discussion, we used the paper by Ma et al. "Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems". A literature review was performed for this study, which included a thorough search of open-access research papers using online sources (PubMed and Google). The research provides examples of unique attack strategies for medical images. Unfortunately, unique algorithms for attacks on the various ophthalmic image types have yet to be developed. It is a task that needs to be performed. As a result, it is necessary to build algorithms that validate the computation and explain the findings of artificial intelligence models. In this article, we focus on adversarial attacks, one of the most well-known attack methods, which provide evidence (i.e., adversarial examples) of the lack of resilience of decision models that do not include provable guarantees. Adversarial attacks have the potential to provide inaccurate findings in deep learning systems and can have catastrophic effects in the healthcare industry, such as healthcare financing fraud and wrong diagnosis.

The diffusion of health care fraud: A bipartite network analysis

Many studies have examined the diffusion of health care innovation but less is known about the diffusion of health care fraud. In this paper, we consider the diffusion of potentially fraudulent Medicare home health care billing in the United States during 2002–16, with a focus on the 21 hospital referral regions (HRRs) covered by local Department of Justice (DOJ) anti-fraud “strike force” offices. We hypothesize that patient-sharing across home health care agencies (HHAs) provides a mechanism for the rapid diffusion of fraudulent strategies. We measure such activity using a novel bipartite mixture (or BMIX) network index, which captures patient sharing across multiple agencies and thus conveys more information about the diffusion process than conventional unipartite network measures. Using a complete population of fee-for-service Medicare claims data, we first find a remarkable increase in home health care activity between 2002 and 2009 in many regions targeted by the DOJ; average billing per Medicare enrollee in McAllen TX and Miami increased by $2127 and $2422 compared to just an average $289 increase in other HRRs not targeted by the DOJ. Second, we establish that the HRR-level BMIX (but not other network measures) was a strong predictor of above-average home health care expenditures across HRRs. Third, within HRRs, agencies sharing more patients with other agencies were predicted to increase billing. Finally, the initial 2002 BMIX index was a strong predictor of subsequent changes in HRR-level home health billing during 2002–9. These results highlight the importance of bipartite network structure in diffusion and in infection and contagion models more generally.

Health insurance fraud detection by using an attributed heterogeneous information network with a hierarchical attention mechanism

BackgroundWith 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.MethodsWe 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.ResultsExperimental results using real datasets showed that MHAMFD detected health insurance fraud with better accuracy than existing methods.ConclusionsExperiment 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.

Mitigate Long-lasting Ethical Issues in the Healthcare Industry with Blockchain-based Solutions

As one of the most promising innovations in the recent computing paradigm, blockchain technology has been introduced into many fields such as finance. With the outbreak of the epidemic and the intensification of the global public health crisis, medical care has also undergone rapid revolutionary changes in replacing traditional systems with new technologies. Among them, the ethical issues exposed in the process of transformation are under intense debate. Traditional research and surveys focus on the privacy of patient data management and only include some roles in the medical ecosystem, which cannot meet the existing needs. To this end, based on system thinking, this paper studies blockchain technology and emphasizes its application potential in the field of health care to alleviate some long-standing ethical problems, mainly including manipulation of organ transplantation, counterfeit drugs, and medical insurance fraud. It also discussed the technical challenges and limitations of the implementation of the blockchain-based system and ended with the possibility of reimagining the future.

An abnormal surgical record recognition model with keywords combination patterns based on TextRank for medical insurance fraud detection

An abnormal surgical record recognition model with keywords combination patterns based on TextRank for medical insurance fraud detection

A Survey on the Effective Machine Learning Approaches for Medical Images Forgery Detection

In today’s scenario medical image forgery has turned out to be unsophisticated because of capable PCs, propelled image editing softwares and high resolution capturing gadgets. Insurance fraud is a deliberate deception perpetrated against or by an insurance company or agent for financial gain. The only downside that the health insurance industry has faced is the rise in the number of health insurance frauds. An endeavor is prepared to review the current improvements in the research area of advanced medical image fraud detection and comprehensive reference index has been exhibited on different methods for medical forgery identification. Key Words: CNN, Forgery detection, medical images

Feature Selection Based on the Discriminative Significance for Sparse Binary-Valued and Imbalanced Dataset

Identifying the significant, or dominant, features is important to reveal the cause-and-effect relations in many pattern recognition applications, such as medical diagnosis, gene analysis, cyber security, finance and insurance fraud detection, etc. Samples that are sparsely populated and binary-valued in highly imbalanced datasets pose a challenge to the identification of these features. This paper explores an approach based on the confusion matrix measurement of the feature values with respect to their potential classification outcomes. The approach is able to compute the Discriminative Significances of the features and rank the features unbiasedly with respect to the imbalance ratios of the datasets. Experiment results on real-world and experimental datasets show that the approach made consistent evaluations of the features and identified the most significant ones accordingly on the sparse and binary-valued samples of the class-imbalanced datasets.

MENTAL AND BEHAVIOURAL DISORDERS IN “THE PROPOSAL” BY ANTON CHEKHOV

Based on the comparative approach, narrative medicine and pathology diagnostics, the article examines the personal psychological deviations of the characters in Anton Chekhov. The article reveals the main stages in the diagnosis and the interpretation of hypochondria, melancholy, timidity from antiquity, where pathological features of personality and types of temperament were associated with the balance of the elements and basic fluids in the human body, to the Enlightenment and the modernity, where personal pathologies were dictated by mental predisposition and social environment. Molière’s main goal is to identify the type of “imaginary patient” as an expression of hypochondria symptom and to ridicule the type of a quack doctor, while for Chekhov («Proposal»), the main character’s hypochondria is complicated by timidity and procrastination, designed to enhance the comic effect of the described situation. This allows us to consider in a new way the difference between the typological approach to the human being as a combination of passions in the classical comedy and in the mature realism of the 19th century. The conclusion is made about the importance of a literary typological study of psychological deviations and pathologies in the history of human civilisation and intellectual culture.

Private Blockchain in the Field of Health Services

Blockchain is a technology that is quite popular and has been adopted in various fields in recent years. This technology has caught the attention of researchers in the health sector because of its innovation which is considered capable of providing the necessary guarantees for the safe processing, sharing, and management of sensitive patient data. There are many problems with falsifying reports and withholding important information from patients, which is considered medical fraud. Hyperledger, a type of private Blockchain, is very suitable for healthcare applications. A private blockchain is a restricted type of blockchain network created by an entity. This type of network is limited to those with access permissions. In addition, private blockchains usually use a centralized verification system and are controlled by the network's creators. Hyperledger Fabric is one example of a permissioned blockchain that can play a role in implementing patient-centric, interoperable healthcare systems

Understanding medical corruption in China: a mixed-methods study.

Medical corruption is a significant obstacle to achieving health-related Sustainable Development Goals. However, the understanding of medical corruption is limited, especially in developing countries. As the largest developing country, China is also plagued by medical corruption. By employing a mixed-methods design and combining data from three resources, this study attempts to examine patterns of medical corruption in China, explore its key drivers and investigate the perceived effectiveness of recent anti-corruption interventions. Using extracted data from 3546 cases on the China Judgments Online website between 2013 and 2019, we found that bribery, embezzlement and insurance fraud accounted for 68.1%, 22.8% and 9.1% of all medical corruption cases, respectively. Bribery was the major form of medical corruption. Approximately 80% of bribe-takers were healthcare providers, and most bribe-givers were suppliers of pharmaceuticals, medical equipment and consumables. Using a nationally representative household survey, we further found that the prevalence of informal payments from patients remained at a low level between 2011 and 2018. In 2018, only 0.4% of outpatients and 1.4% of inpatients reported that they had ever given 'red envelopes' to physicians in the past. Finally, we conducted interviews with 17 key informants to explore drivers of medical corruption and investigated the perceived effectiveness of recent anti-corruption interventions in China. Interview results showed that financial pressure and weak oversight were two main reasons for corrupt behaviours. Interview results also suggested that the anti-corruption campaign since 2012, the national volume-based procurement, and the special campaign against medical insurance fraud had reduced opportunities for medical corruption, implying China's positive progress in combating medical corruption. These findings hold lessons for anti-corruption interventions in China as well as other developing countries.

A non-manufacturer-sponsored, retrospective study to assess 2-year safety outcomes of the BellaGel® SmoothFine as compared with its competitors in the context of the first Korean case of a medical device fraud.

We conducted this study to assess preliminary 2-year safety outcomes of an implant-based augmentation mammaplasty using the BellaGel® SmoothFine in the context of the first Korean case of a medical device fraud. Our clinical series of the patients (n = 579; 1,158 breasts) received augmentation using the BellaGel® SmoothFine, Naturgel™, Motiva Ergonomix™, Eurosilicone Round Collection™, Natrelle® INSPIRA™, Natrelle® 410, Mentor® MemoryGel Xtra or Microthane®. The patients were evaluated for incidences of postoperative complications and Kaplan-Meier survival and hazards. Overall, there were a total of 101 cases (17.4%) of postoperative complications; these include 31 cases (5.4%) of shape deformity, 21 cases (3.6%) of CC, 18 cases (3.1%) of early seroma, 8 cases (1.4%) of infection, 5 cases (0.9%) of early hematoma, 1 case (0.2%) of delayed hematoma, 1 case (0.2%) of rupture and 1 case (0.2%) of ripping. Moreover, there were also 15 cases (2.6%) of other complications. There were significant differences in incidences of postoperative complications between the breast implants from different manufacturers (P = 0.034). The Natrelle® 410 showed the longest survival (333.3±268.2 [141.5-525.1] days). A subgroup analysis showed that there were no significant differences in incidences of postoperative complications between the breast implants (P = 0.831). Moreover, the Natrelle® INSPIRA™ showed the longest survival (223.7±107.1 [-42.3-489.6] days). Here, we describe preliminary 2-year safety outcomes of an implant-based augmentation mammaplasty using the BellaGel® SmoothFine in the context of the first Korean case of a medical device fraud.