Engineering topics
Publications and source records attributed to Schryver, Jack C.
We discuss the problem of fraud detection in healthcare in this chapter. Given the recent scrutiny of the ineciencies in the US healthcare system, identifying fraud has been on the forefront of the eorts towards reducing the healthcare costs. In this chapter we will focus on understanding the issue of healthcare fraud in detail, and review methods that have been proposed in the literature to combat this issue using data driven approach.
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System dynamics models are usually used to investigate aggregate level behavior, but these models can be decomposed into agents that have more realistic individual behaviors. Here we develop a simple model of the STEM workforce to illuminate the impacts that arise from the disaggregation and refinement of system dynamics models via agent-based modeling. Particularly, alteration of Poisson assumptions, adding heterogeneity to decision-making processes of agents, and discrete-time formulation are investigated and their impacts are illustrated. The goal is to demonstrate both the promise and danger of agent-based modeling in the context of a relatively simple model and to delineate the importance of modeling decisions that are often overlooked.
The role of big data in addressing the needs of the present healthcare system in US and rest of the world has been echoed by government, private, and academic sectors. There has been a growing emphasis to explore the promise of big data analytics in tapping the potential of the massive healthcare data emanating from private and government health insurance providers. While the domain implications of such collaboration are well known, this type of data has been explored to a limited extent in the data mining community. The objective of this paper is two fold: first, we introduce the emerging domain of big"healthcare claims data to the KDD community, and second, we describe the success and challenges that we encountered in analyzing this data using state of art analytics for massive data. Specically, we translate the problem of analyzing healthcare data into some of the most well-known analysis problems in the data mining community, social network analysis, text mining, and temporal analysis and higher order feature construction, and describe how advances within each of these areas can be leveraged to understand the domain of healthcare. Each case study illustrates a unique intersection of data mining and healthcare with a common objective of improving the cost-care ratio by mining for opportunities to improve healthcare operations and reducing hat seems to fall under fraud, waste,and abuse.
The system performance metric 'availability' is a central concept with respect to the concerns of a plant's operators and owners, yet it can be abstract enough to resist explanation at system levels. Hence, there is a need for a system-level metric more closely aligned with a plant's (or, more generally, a system's) raison d'etre. Historically, availability of repairable systems - intrinsic, operational, or otherwise - has been defined as a ratio of times. This paper introduces a new concept of availability, called endogenous availability, defined in terms of a ratio of quantities of product yield. Endogenous availability can be evaluated using a discrete event simulation analysis methodology. A simulation example shows that endogenous availability reduces to conventional availability in a simple series system with different processing rates and without intermediate storage capacity, but diverges from conventional availability when storage capacity is progressively increased. It is shown that conventional availability tends to be conservative when a design includes features, such as in - process storage, that partially decouple the components of a larger system.
The authors propose a new online collaborative tool for visually understanding national health indicators, which facilitates the full spectrum of investigation of indicators, from an overview of all the correlation coefficients between variables, to investigation of subsets of selected variables, and to individual data element analysis. this tool is publicly accessible at http://cda.ornl.gov/heat/heatmap.html. In this paper, they discuss the key issues regarding the interface design and implementation. They also illustrate how to use their interface for analyzing the health indicator dataset by showing some key system views. In the end, they introduce and discuss some ongoing research efforts extending this work.