Clinical Decision Support Systems are "active knowledge systems which use two or more items of patient data to generate case-specific advice" (Wyatt J, Spiegelhalter D, 1991). Some successful systems such as 'Dxplain' and 'QMR' originating in the 1980s were successfully commercialized. There are compelling evidences for the effectiveness of CDSS for improved patient safety and improved quality of care. CDSS has been portrayed in a positive light by majority of reviews.
The basic components of a CDSS include a medical knowledge base and an inference mechanism. It could be based on Expert systems or artificial neural networks or both. The computing techniques that are used to create CDSS are divided in two broad categories:
For a clinically useful CDSS, the knowledge system should be based on best evidence and it should fully cover the problem. Also, it requires the capability to update the knowledge base. The system should be easy to use and its performance should be validated rigorously. A medical practitioner needs to deal with different kinds of data and knowledge and no single DSS model has the ability to manage all of them. In any advanced DSS model, data and knowledge are complementary; both are useful to take an appropriate decision in a complex domain like medicine.
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