scholarly journals Overview of WP4: extension of atmospheric dispersion and consequence modelling in Decision Support Systems

2016 ◽  
Vol 51 ◽  
pp. S93-S95
Author(s):  
S. Andronopoulos
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Atmospheric Dispersion

scholarly journals From Containing the Atom to Mitigating Residual Risk: The German Imaginary of Nuclear Emergency Preparedness

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1302
Author(s):  
Tudor B. Ionescu
Keyword(s):  
Decision Support ◽  
Emergency Management ◽  
Decision Support Systems ◽  
Support Systems ◽  
Atmospheric Dispersion ◽  
Research Approach ◽  
Dispersion Models ◽  
Nuclear Facilities ◽  
Atmospheric Dispersion Models ◽  
Nuclear Emergency

Grounded in a social scientific research approach, the present case study traces the shift in the German nuclear regulatory culture from prevention to preparedness, the latter of which builds upon decision support systems for nuclear emergency management. These systems integrate atmospheric dispersion models for tracing radioactive materials released accidentally from nuclear facilities. For atmospheric dispersion modelers and emergency managers, this article provides a critical historical perspective on the practical, epistemic, and organizational issues surrounding the use of decision support systems for nuclear emergency management. This perspective suggests that atmospheric dispersion models are embedded within an entire assemblage of institutions, technologies, and practices of preparedness, which are challenged by the uniqueness of each nuclear accident.

Assigning Value to Clinical Information – A Major Limiting Factor in the Implementation of Decision-Support Systems

1996 ◽  
Vol 35 (01) ◽  
pp. 1-4 ◽  
Author(s):  
F. T. de Dombal
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Clinical Information ◽  
Limiting Factor ◽  
Major Difficulty ◽  
Computer Aided ◽  
Computer Aided Decision

AbstractThis paper deals with a major difficulty and potential limiting factor in present-day decision support - that of assigning precise value to an item (or group of items) of clinical information. Historical determinist descriptive thinking has been challenged by current concepts of uncertainty and probability, but neither view is adequate. Four equations are proposed outlining factors which affect the value of clinical information, which explain some previously puzzling observations concerning decision support. It is suggested that without accommodation of these concepts, computer-aided decision support cannot progress further, but if they can be accommodated in future programs, the implications may be profound.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

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