Clinical decision support systems : theory and practice by Eta S. Berner

By Eta S. Berner

Construction at the good fortune of the former versions, this totally up-to-date publication once more brings jointly all over the world specialists to demonstrate the underlying technology and daily use of choice help platforms in medical and academic settings.

Topics mentioned include:

-Mathematical Foundations of choice help Systems
-Design and Implementation Issues
-Ethical and criminal concerns in determination Support
-Clinical Trials of knowledge Interventions
-Hospital-Based determination Support
-Real international Case Studies 

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Extra resources for Clinical decision support systems : theory and practice

Sample text

Knowledge Base For CDSS to work, they must possess some form of medical knowledge. Obviously, the method of encoding this knowledge must match the inference engine design. For example, a CDSS based on a Bayesian network must contain probabilities—prior, conditional, and posterior—of diseases and findings. A big obstacle to building such a knowledge base is that many relevant probabilities are not known. While the medical literature can surely help with this task, and CDSS developers use the literature to varying degrees in building their knowledge bases, knowledge base developers must resort to estimates of probabilities, based on the clinical judgment of experts, to fill in the needed numbers.

48. Covell DG, Uman GC, Manning PR. Information needs in office practice: Are they being met? Ann Intern Med 1985;103:596–569. 49. Gorman PN, Helfand M. Information seeking in primary care: How physicians choose which clinical questions to pursue and which to leave unanswered. Med Decis Making 1995;15:113–119. 50. Berner ES, Maisiak RS, Phelan ST, Kennedy JI, Heudebert GR, Young KR. Use of a diagnostic decision support system by internal medicine residents. Unpublished study; 2002. 51. Teich JM, Merchia PR, Schmiz JL, Kuperman GJ, Spurr CD, Bates DW.

Since many CDSS begin as funded academic research projects, it is no wonder that development of their knowledge bases often halts after the grant funds cease. Since knowledge base maintenance takes a tremendous amount of time, and since the market for some CDSS is rather small, many CDSS become too expensive to maintain. The knowledge-acquisition bottleneck23 has been recognized as a problem in CDSS research. Output The output of CDSS is usually in the form of a list of possibilities, ranked in some order of probability.

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