scholarly journals Designing Effective Forecasting Decision Support Systems: Aligning Task Complexity and Technology Support

10.5772/51255 ◽  
2012 ◽  
Author(s):  
Monica Adya ◽  
Edward J.
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Task Complexity ◽  
Technology Support

The Effectiveness of Decision Support Systems: The Implications of Task Complexity and Dss Sophistication

1994 ◽  
Vol 9 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Richard Webby ◽  
Marcus O'Connor
Keyword(s):  
Decision Support ◽  
Risk Analysis ◽  
Decision Support Systems ◽  
Support Systems ◽  
Task Complexity ◽  
Business Simulation ◽  
Complex Phase ◽  
Spreadsheet Model ◽  
Number Of Factors ◽  
Better Than

All Decision Support Systems (DSS) are, by their nature, designed to improve decision making effectiveness, yet a review of the experimental literature reveals that achievement of this objective is mixed. We propose that this is because DSS effectiveness is contingent upon a number of factors related to the task and DSS under investigation. This paper reports a longitudinal experiment designed to evaluate the relationship between DSS effectiveness and two such factors: DSS sophistication and task complexity. In comparison to unaided human judgement, two levels of DSS were evaluated: a deterministic spreadsheet model and a probabilistic model with a graphical risk analysis aid. Our subjects made decisions in a business simulation providing two successive phases of increasing task complexity. Initially, when task complexity was low, we found that neither DSS affected subjects’ performance. In the more complex phase, both types of DSS users performed significantly better than unaided subjects. However, risk analysis users performed no better than model-only users. Interestingly, DSS users performed less homogeneously than unaided subjects in the complex phase. DSS users had greater confidence and considered more alternatives than their unaided counterparts. Risk analysis users took longer making decisions in the early stages, while model-only users became the most efficient in the later stages.

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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