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)

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

Quantitative Decision Making

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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