An Intelligent Data Mining System to Detect Health Care Fraud

The chapter begins with an overview of the types of healthcare fraud. Next, there is a brief discussion of issues with the current fraud detection approaches. The chapter then develops information technology based approaches and illustrates how these technologies can improve current practice. Finally, there is a summary of the major findings and the implications for healthcare practice

A Decision Technology System To Advance the Diagnosis and Treatment of Breast Cancer

Geographical variations in cancer rates have been observed for decades. Described spatial patterns and trends have provided clues for generating hypotheses about the etiology of cancer. For breast cancer, investigators have demonstrated that some variation can be explained by differences in the population distribution of known breast cancer risk factors such as menstrual and reproductive variables (Laden, Spiegelman, and Neas, 1997; Robbins, Bescianini, and Kelsey, 1997; Sturgeon, Schairer, and Gail, 1995). However, regional patterns also may reflect the effects of Workshop on Hormones, Hormone Metabolism, Environment, and Breast Cancer (1995): (a) environmental hazards (such as air and water pollution), (b) demographics and the lifestyle of a mobile population, (c) subgroup susceptibility, (d) changes and advances in medical practice and healthcare management, and (e) other factors. To accurately measure breast cancer risk in individuals and population groups, it is necessary to singly and jointly assess the association between such risk and the hypothesized factors. Various statistical models will be needed to determine the potential relationships between breast cancer development and estimated exposures to environmental contamination. To apply the models, data must be assembled from a variety of sources, converted into the statistical models’ parameters, and delivered effectively to researchers and policy makers. A Web-enabled decision technology system can be developed to provide the needed functionality. This chapter will present a conceptual architecture for such a decision technology system. First, there will be a brief overview of a typical geographical analysis. Next, the chapter will present the conceptual Web-based decision technology system and illustrate how the system can assist users in diagnosing and treating breast cancer. The chapter will conclude with an examination of the potential benefits from system use and the implications for breast cancer research and practice

Cancer Surveillance using Data Warehousing, Data Mining, and Decision Support Systems

This article discusses how data warehousing, data mining, and decision support systems can reduce the national cancer burden or the oral complications of cancer therapies, especially as related to oral and pharyngeal cancers. An information system is presented that will deliver the necessary information technology to clinical, administrative, and policy researchers and analysts in an effective and efficient manner. The system will deliver the technology and knowledge that users need to readily: (1) organize relevant claims data, (2) detect cancer patterns in general and special populations, (3) formulate models that explain the patterns, and (4) evaluate the efficacy of specified treatments and interventions with the formulations. Such a system can be developed through a proven adaptive design strategy, and the implemented system can be tested on State of Maryland Medicaid data (which includes women, minorities, and children)

Studying the Value of Information Sharing in E-Business Supply Chain Management

The supply chain management of goods and services involves multiple trading partners such as raw-material suppliers, manufacturers, distributors, and retailers. Every one of these trading partners need to determine their requirements, in terms of merchandise, and match them against availability, pricing, and cost of transportation. At every step of the supply chain economics information retrieval is a crucial and recurring process. In this paper we study information sharing as a strategy for improved decision making that can increase the profitability of the entire supply chain. We describe various different models for information sharing and illustrate the benefits of information sharing using ordering relationships among the trading partners of a simple four-node supply chain. In order to examine the relationship between different variables and the well-known bull-whip ejfect, we develop a simulation system to quantify the variables and generate different results in different scenarios. These results are analyzed in this paper, and implications are presented

BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer

For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical models’ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the system’s use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice

Enhancing Virtual Teams: Relations vs. Communication Technology

Reports of several researchers stress the importance of actual face-to-face presence of people who participate in non-routine activities (Daft & Lengel, 1984; Nohria & Eccles, 1992). Even companies that take advantage of information and communication technology more vigorously then others, consider face-to-face contacts as being essential for communication. Senior managers of geographically dispersed firms are reported to spend much of their time on the road in order to meet face-to-face with their employees in local units. Especially by people in management positions, the electronic media are seen as a supplement, not as a replacement. The question is: how can this often reported need for face-to-face contacts be explained

Mining sensor datasets with spatiotemporal neighborhoods

Many spatiotemporal data mining methods are dependent on how relationships between a spatiotemporal unit and its neighbors are defined. These relationships are often termed the neighborhood of a spatiotemporal object. The focus of this paper is the discovery of spatiotemporal neighborhoods to find automatically spatiotemporal sub-regions in a sensor dataset. This research is motivated by the need to characterize large sensor datasets like those found in oceanographic and meteorological research. The approach presented in this paper finds spatiotemporal neighborhoods in sensor datasets by combining an agglomerative method to create temporal intervals and a graph-based method to find spatial neighborhoods within each temporal interval. These methods were tested on real-world datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b) NEXRAD precipitation data from the Hydro-NEXRAD system. The results were evaluated based on known patterns of the phenomenon being measured. Furthermore the results were quantified by performing hypothesis testing to establish the statistical significance using Monte Carlo simulations. The approach was also compared with existing approaches using validation metrics namely spatial autocorrelation and temporal interval dissimilarity. The results of these experiments show that our approach indeed identifies highly refined spatiotemporal neighborhoods