The population of people ages 65 and older has increased since the 1960s and current estimates indicate it will double by 2060. Medicare is a federal health insurance program for people 65 or older in the United States. Medicare claims fraud and abuse is an ongoing issue that wastes a large amount of money every year resulting in higher health care costs and taxes for everyone. In this study, an empirical evaluation of several unsupervised machine learning approaches is performed which indicates reasonable fraud detection results. We employ two unsupervised machine learning algorithms, Isolation Forest and Unsupervised Random Forest, which have not been previously used for the detection of fraud and abuse on Medicare data. Additionally, we implement three other machine learning methods previously applied on Medicare data which include: Local Outlier Factor, Autoencoder, and k-Nearest Neighbor. For our dataset, we combine the 2012 to 2015 Medicare provider utilization and payment data and add fraud labels from the List of Excluded Individuals/Entities (LEIE) database. Results show that Local Outlier Factor is the best model to use for Medicare fraud detection. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Healthcare is an integral component in peoples lives, especially for the rising elderly population, and must be affordable. The United States Medicare program is vital in serving the needs of the elderly. The growing number of people enrolled in the Medicare program, along with the enormous volume of money involved, increases the appeal for, and risk of, fraudulent activities. For many real-world applications, including Medicare fraud, the interesting observations tend to be less frequent than the normative observations. This difference between the normal observations and those observations of interest can create highly imbalanced datasets. The problem of class imbalance, to include the classification of rare cases indicating extreme class imbalance, is an important and well-studied area in machine learning. The effects of class imbalance with big data in the real-world Medicare fraud application domain, however, is limited. In particular, the impact of detecting fraud in Medicare claims is critical in lessening the financial and personal impacts of these transgressions. Fortunately, the healthcare domain is one such area where the successful detection of fraud can garner meaningful positive results. The application of machine learning techniques, plus methods to mitigate the adverse effects of class imbalance and rarity, can be used to detect fraud and lessen the impacts for all Medicare beneficiaries. This dissertation presents the application of machine learning approaches to detect Medicare provider claims fraud in the United States. We discuss novel techniques to process three big Medicare datasets and create a new, combined dataset, which includes mapping fraud labels associated with known excluded providers. We investigate the ability of machine learning techniques, unsupervised and supervised, to detect Medicare claims fraud and leverage data sampling methods to lessen the impact of class imbalance and increase fraud detection performance. Additionally, we extend the study of class imbalance to assess the impacts of rare cases in big data for Medicare fraud detection. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g. anomaly detection. Modeling such skewed data distributions is often very difficult, and non-standard methods are sometimes required to combat these negative effects. These challenges have been studied thoroughly using traditional machine learning algorithms, but very little empirical work exists in the area of deep learning with class imbalanced big data. Following an in-depth survey of deep learning methods for addressing class imbalance, we evaluate various methods for addressing imbalance on the task of detecting Medicare fraud, a big data problem characterized by extreme class imbalance. Case studies herein demonstrate the impact of class imbalance on neural networks, evaluate the efficacy of data-level and algorithm-level methods, and achieve state-of-the-art results on the given Medicare data set. Results indicate that combining under-sampling and over-sampling maximizes both performance and efficiency. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

A mere 16 percent of the population enjoys the benefits of private healthcare; medical schemes however remain an important contributor to the South African economy with an annual contribution flow of close to R85 billion per annum. Similar to the international scenario, South African healthcare inflation surpassed consumer price inflation. In addition, the medical schemes industry remains riddled with fraud, this coupled with escalating private healthcare costs remain subsequent threats to the sustainability of the industry. It is reported that service provider fraud has surpassed fraud committed by scheme members. Most medical schemes appear to have policies in place to manage and combat fraud, however transparency in terms of information sharing remains elusive. Of greater concern have been the investigation and management ethicality and endgame of medical schemes in terms of fraud risk management amongst medical practitioners. The research problem states that there is currently no standard fraud investigation and management protocol available for the ethical investigation and management of medical schemes fraud committed by medical practitioners. The literature review demonstrated that there has been a paradigm shift regarding the expectations that society has of the modern corporation and emphasised the inclusive stakeholder model theory in favour of the traditional shareholder dictum: pursuit of profit maximisation at any cost. The research design was done by providing a survey questionnaire to private medical practitioners. The literature review and survey findings highlighted the need for medical schemes to pay greater heed to their ethicality and stakeholder issue management practices. Focus areas for the development of an industry standard fraud investigation and management protocol was recommended.

The United States (U.S.) healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when there are considerably less fraudulent transactions than non-fraudulent. Fraud is an extremely important issue for healthcare, as fraudulent activities within the U.S. healthcare system contribute to significant financial losses. In the U.S., the elderly population continues to rise, increasing the need for programs, such as Medicare, to help with associated medical expenses. Unfortunately, due to healthcare fraud, these programs are being adversely affected, draining resources and reducing the quality and accessibility of necessary healthcare services. In response, advanced data analytics have recently been explored to detect possible fraudulent activities. The Centers for Medicare and Medicaid Services (CMS) released several ‘Big Data’ Medicare claims datasets for different parts of their Medicare program to help facilitate this effort. In this dissertation, we employ three CMS Medicare Big Data datasets to evaluate the fraud detection performance available using advanced data analytics techniques, specifically machine learning. We use two distinct approaches, designated as anomaly detection and traditional fraud detection, where each have very distinct data processing and feature engineering. Anomaly detection experiments classify by provider specialty, determining whether outlier physicians within the same specialty signal fraudulent behavior. Traditional fraud detection refers to the experiments directly classifying physicians as fraudulent or non-fraudulent, leveraging machine learning algorithms to discriminate between classes. We present our novel data engineering approaches for both anomaly detection and traditional fraud detection including data processing, fraud mapping, and the creation of a combined dataset consisting of all three Medicare parts. We incorporate the List of Excluded Individuals and Entities database to identify real world fraudulent physicians for model evaluation. Regarding features, the final datasets for anomaly detection contain only claim counts for every procedure a physician submits while traditional fraud detection incorporates aggregated counts and payment information, specialty, and gender. Additionally, we compare cross-validation to the real world application of building a model on a training dataset and evaluating on a separate test dataset for severe class imbalance and rarity. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 74-78). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 74-78). Also available on the Internet.

In the fiscal year 2014, approximately 1,337 health care providers lost their provider license to Medicare/Medicaid fraud. Out of the 1,318 criminal convictions reported by the U.S. Medicaid Fraud Control Units (MFCU), 395 (30%) were home health care aides who claimed to have rendered services not provided. The purpose of this multiple case study was to explore licensed and certified home health care business managers' strategies to mitigate Medicare/Medicaid fraud risk. A purposive sampling of 9 business managers and chief executive officers from 3 licensed and certified home health care businesses in Franklin County, Ohio participated in semistructured face-to-face interviews. Data from the interviews were transcribed, coded, and analyzed to identify themes regarding Medicare/Medicaid fraud risk management strategies. Drawing from the Committee of Sponsoring Organization's internal control framework and fraud management lifecycle theory, 5 themes emerged: the control environment, risk assessment, control activities, information and communication, and monitoring activities. Findings from this study included maintenance of integrity and culture, training and educating both staff and clients about fraud reporting processes and the consequences of fraud, rotating staff on a regular basis, performing fraud risk assessments, implementing remote timekeeping and monitoring system, and compensating shift leaders to coordinate activities in the clients' residences. The implication for positive social change includes reducing healthcare cost for all taxpayers through Medicare/Medicaid fraud reduction.

Healthcare fraud is a major problem within the healthcare industry. The study examined medical fraud, its laws, and punishments on federal and state levels. It compared medical fraud to non-fraud crimes done in the healthcare industry. This comparison will be done on a state level. The study attempted to analyze the severity of fraud against non-fraud and that doctors would commit fraud offenses more often than non-fraud offenses.

The medical scheme industry in South Africa is competitive in relation to international standards. The medical scheme sector, as part of the healthcare industry, is negatively affected by the high rate of fraud perpetrated by providers, members and syndicates, which results in medical schemes funding fraudulent claims. The purpose of the study was to explore strategies to mitigate fraud in medical scheme claims. A qualitative research methodology was followed in this study, which adopted a case study approach. Empirical data was analysed through thematic analysis, with the aid of ATLAS.ti software. The study found that healthcare service providers mainly defraud medical schemes by submitting false claims. A holistic approach should be followed to mitigate fraud in medical scheme claims. This approach should encompass regularly identifying trends in fraudulent claims and implementing appropriate control strategies. Collaboration within the medical scheme industry and with other stakeholders would also help to elevate the fight against medical scheme fraud to a new level. Implementing the recommendations from the study will assist medical schemes to reduce the funds expended on fraudulent claims, thereby improving their financial viability and decreasing the rate of increase in medical scheme contributions for members. / Business Management / M. Com. (Business Management)