Current Issues and Future Trends of Clinical Decision Support Systems (CDSS)

Current Issues and Future Trends of Clinical Decision Support Systems (CDSS)

Omar F. El-Gayar (Dakota State University, USA), Amit Deokar (Dakota State University, USA) and Matthew Wills (Dakota State University, USA)
Copyright: © 2008 |Pages: 7
DOI: 10.4018/978-1-59904-889-5.ch046
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The cost of healthcare is increasing exponentially worldwide. The adoption and diffusion of e-health and the application of Internet and Communication Technology (ICT) in healthcare is growing at a rapid rate in an attempt to find cost effective methods of providing quality healthcare. Both European and US governments are making e-health a priority on their agendas. However, few, if any, discuss the critical issues of the sustainability and feasibility of e-health models. We attempt to fill this critical void by presenting a macro framework that identifies the key components of a generic e-health system and identifying factors playing a role in the assessment of e-health sustainability.

Key Terms in this Chapter

Fuzzy Logic: Based on fuzzy set theory, deals with complex systems where reasoning is approximate, due to complexity or incomplete data.

Passive CDSS: Requires the user to initiate a process by providing a request to the system.

Inference Engine: The component of a CDSS program that formulates inferences from a knowledge repository.

Artificial Neural Networks: A network of simple processing elements which can exhibit complex global behavior, determined by the connections between the processing elements and element parameters.

Heuristic: An algorithmic technique designed to solve a problem that ignores whether the solution can be proven to be correct.

Genetic Algorithms: A genetic algorithm is a search method used in computational intelligence to find true or approximate solutions to optimization and search problems.

Active CDSS: Active CDSSs make decisions and interact without input or request by the user.

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