Medicare fraud

The rapid growth of digital transactions and the increasing sophistication of fraudulent activities have necessitated the development of robust and efficient fraud detection techniques, particularly in the financial and healthcare sectors. This dissertation focuses on the use of novel data reduction techniques for addressing the unique challenges associated with detecting fraud in highly imbalanced Big Data, with a specific emphasis on credit card transactions and Medicare claims. The highly imbalanced nature of these datasets, where fraudulent instances constitute less than one percent of the data, poses significant challenges for traditional machine learning algorithms. This dissertation explores novel data reduction techniques tailored for fraud detection in highly imbalanced Big Data. The primary objectives include developing efficient data preprocessing and feature selection methods to reduce data dimensionality while preserving the most informative features, investigating various machine learning algorithms for their effectiveness in handling imbalanced data, and evaluating the proposed techniques on real-world credit card and Medicare fraud datasets.
This dissertation covers a comprehensive examination of datasets, learners, experimental methodology, sampling techniques, feature selection techniques, and hybrid techniques. Key contributions include the analysis of performance metrics in the context of newly available Big Medicare Data, experiments using Big Medicare data, application of a novel ensemble supervised feature selection technique, and the combined application of data sampling and feature selection. The research demonstrates that, across both domains, the combined application of random undersampling and ensemble feature selection significantly improves classification performance.

Access to affordable healthcare is a nationwide concern that impacts most of the United States population. Medicare is a federal government healthcare program that aims to provide affordable health insurance to the elderly population and individuals with select disabilities. Unfortunately, there is a significant amount of fraud, waste, and abuse within the Medicare system that inevitably raises premiums and costs taxpayers billions of dollars each year. Dedicated task forces investigate the most severe fraudulent cases, but with millions of healthcare providers and more than 60 million active Medicare beneficiaries, manual fraud detection efforts are not able to make widespread, meaningful impact. Through the proliferation of electronic health records and continuous breakthroughs in data mining and machine learning, there is a great opportunity to develop and leverage advanced machine learning systems for automating healthcare fraud detection.
This dissertation identifies key challenges associated with predictive modeling for large-scale Medicare fraud detection and presents innovative solutions to address these challenges in order to provide state-of-the-art results on multiple real-world Medicare fraud data sets. Our methodology for curating nine distinct Medicare fraud classification data sets is presented with comprehensive details describing data accumulation, data pre-processing, data aggregation techniques, data enrichment strategies, and improved fraud labeling. Data-level and algorithm-level methods for treating severe class imbalance, including a flexible output thresholding method and a cost-sensitive framework, are evaluated using deep neural network and ensemble learners. Novel encoding techniques and representation learning methods for high-dimensional categorical features are proposed to create expressive representations of provider attributes and billing procedure codes.

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.

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.

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.