The amount of data produced within Health Informatics has grown to be quite vast, and analysis of this Big Data grants potentially limitless possibilities for knowledge to be gained. In addition, this information can improve the quality of healthcare offered to patients. However, there are a number of issues that arise when dealing with these vast quantities of data, especially how to analyze this data in a reliable manner. The basic goal of Health Informatics is to take in real world medical data from all levels of human existence to help advance our understanding of medicine and medical practice. This paper will present recent research using Big Data tools and approaches for the analysis of Health Informatics data gathered at multiple levels, including the molecular, tissue, patient, and population levels. In addition to gathering data at multiple levels, multiple levels of questions are addressed: human-scale biology, clinical-scale, and epidemic-scale. We will also analyze and examine possible future work for each of these areas, as well as how combining data from each level may provide the most promising approach to gain the most knowledge in Health Informatics.

Healthcare in the United States (U.S.) is important in the lives of many citizens, but unfortunately the high costs of health-related services leave many patients with limited medical care. In response, the U.S. government has established and funded programs, such as Medicare [1], that provide financial assistance for qualifying people to receive needed medical services [2]. There are a number of issues facing healthcare and

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The United States 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 (documented and readily available) than non-fraudulent. The ability to successfully detect fraudulent activities in healthcare, given such discrepancies, can garner up to $350 billion in recovered monetary losses. In machine learning, when one class has a substantially larger number of instances (majority) compared to the other (minority), this is known as class imbalance. In this paper, we focus specifically on Medicare, utilizing three 'Big Data' Medicare claims datasets with real-world fraudulent physicians. We create a training and test dataset for all three Medicare parts, both separately and combined, to assess fraud detection performance. To emulate class rarity, which indicates particularly severe levels of class imbalance, we generate additional datasets, by removing fraud instances, to determine the effects of rarity on fraud detection performance. Before a machine learning model can be distributed for real-world use, a performance evaluation is necessary to determine the best configuration (e.g. learner, class sampling ratio) and whether the associated error rates are low, indicating good detection rates. With our research, we demonstrate the effects of severe class imbalance and rarity using a training and testing (Train_Test) evaluation method via a hold-out set, and provide our recommendations based on the supervised machine learning results. Additionally, we repeat the same experiments using Cross-Validation, and determine it is a viable substitute for Medicare fraud detection. For machine learning with the severe class imbalance datasets, we found that, as expected, fraud detection performance decreased as the fraudulent instances became more rare. We apply Random Undersampling to both Train_Test and Cross-Validation, for all original and generated datasets, in order to assess potential improvements in fraud detection by reducing the adverse effects of class imbalance and rarity. Overall, our results indicate that the Train_Test method significantly outperforms Cross-Validation.

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