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)

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

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

Management Support System Effectiveness: Further Empirical Evidence

Modern research has engendered frameworks, such as the management support system (MSS), that are designed to provide comprehensive and integrated support for the decision making process. While one recent study has empirically measured the effects of these frameworks on decision making, there have been few, if any, corroborating or deprecating investigations. This article offers further empirical evidence on MSS effectiveness. The paper begins with a brief overview of the previous research. Next, it assesses the influences of the MSS on the process and outcomes of business policy decision making. The paper also examines the implications of the analyses for information systems research and management practice

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

Quantitative Decision Making

s.l.xx, 901 p.; 21 c

An AHP analysis of quality in AI and DSS journals

Over the years, a number of researchers have assessed the quality of information systems (IS) journals. Most of these studies have assessed general IS journals, but few have specifically examined journals that focus on decision-making support systems. Furthermore, even though there are many factors that measure journal quality, very few gauges have been used in the previous evaluations. Recently, the authors reported a study that utilized the analytical hierarchy process to evaluate 20 top decision-making support system journals. This paper extends the earlier work by providing separate ratings for artificial intelligence and decision support system journals. Initially, the article reviews the criteria and AHP methodology to evaluate decision-making support system journal quality. Next, there is an updated discussion of the data collection process and the resulting multiple criteria evaluation. The paper concludes with a summary of the evaluation and the implications for information systems theory and practice.Information systems meta study Decision support systems AHP applications Multiple criteria decision-making Journal ratings Journal rankings Journal quality