Clinical decision support system: Difference between revisions
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'''Clinical (or Diagnostic) Decision Support Systems (CDSS)''' are interactive computer programs that directly assist physicians and other health professionals with decision making tasks. These fall under the class of [[Decision support system]]s. | '''Clinical (or Diagnostic) Decision Support Systems (CDSS)''' are interactive computer programs that directly assist physicians and other health professionals with decision making tasks. These fall under the class of [[Decision support system]]s. | ||
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The basic components of a CDSS include a ''dynamic'' (medical) knowledge base and an ''inferencing mechanism'' (usually a set of rules derived from the [[expert]]s and [[evidence-based medicine]]). It could be based on [[Expert system]]s or [[artificial neural networks]] or both ([[Connectionist expert system]]s). | The basic components of a CDSS include a ''dynamic'' (medical) knowledge base and an ''inferencing mechanism'' (usually a set of rules derived from the [[expert]]s and [[evidence-based medicine]]). It could be based on [[Expert system]]s or [[artificial neural networks]] or both ([[Connectionist expert system]]s). | ||
CDSS may be linked to [[Electronic medical records]], for decision making and also they could be used for practicing [[Evidence-based medicine]] in an automated way. However they are not meant to replace [[doctors]], rather empower them to make better and more rational decisions. | |||
The role of an apparently simple search by [[Google]] in pointing towards a better diagnosis has been emphasized by a recent publication [http://www.bmj.com/cgi/content/abstract/bmj.39003.640567.AEv1?hrss=1]. | |||
==External links== | ==External links== | ||
Revision as of 04:29, 13 November 2006
Clinical (or Diagnostic) Decision Support Systems (CDSS) are interactive computer programs that directly assist physicians and other health professionals with decision making tasks. These fall under the class of Decision support systems.
For medical diagnosis, there are scopes for ambiguities in inputs, such as history (patient’s description of the diseased condition), physical examinations (especially in cases of uncooperative or less intelligent patients), and laboratory tests (faulty methods or equipment). Moreover, for treatment, there are chances of drug reactions and specific allergies, and patients' non-compliance of the therapy due to cost or time or adverse reactions.
In all these areas, computers can help the clinician to reach an accurate diagnosis faster. Another new branch of medicine pharmacogenomics is the product of breeding between information technology and biology, leading to individualized treatment.
The basic components of a CDSS include a dynamic (medical) knowledge base and an inferencing mechanism (usually a set of rules derived from the experts and evidence-based medicine). It could be based on Expert systems or artificial neural networks or both (Connectionist expert systems).
CDSS may be linked to Electronic medical records, for decision making and also they could be used for practicing Evidence-based medicine in an automated way. However they are not meant to replace doctors, rather empower them to make better and more rational decisions.
The role of an apparently simple search by Google in pointing towards a better diagnosis has been emphasized by a recent publication [1].
External links
- OpenClinical maintains an extensive archive of Artificial Intelligence systems in routine clinical use.
- Some useful links
- Prerequisites
- A chapter of a book
- Another book chapter
- A slide show
- A systematic review
- A white paper on the use of CDSS for the practice of EBM
- A recent paper showing that the computerized clinical decision support systems can improve existing antimicrobial management programs
- Improving Outcomes with Clinical Decision Support: An Implementer’s Guide is a new resource designed to help healthcare organizations use clinical decision support (CDS) to measurably improve key healthcare outcomes such as the quality, safety, and cost-effectiveness of care delivery.