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The utilization of clinical decision support (CDS) is increasing among healthcare facilities which have implemented computerized physician order entry or electronic medical records. Formal prospective evaluation of CDS implementations occurs rarely, and misuse or flaws in system design are often unrecognized. Retrospective review can identify failures but is too late to make critical corrections or initiate redesign efforts. Real-time surveillance of user responses and patient outcomes comprises one approach to give immediate feedback to CDS designers and help operate a safety net which intercepts CDS failures. We outline four types of CDS (passive alerts, interruptive alerts, order sets, and complex ordering advisors) and describe common failures with surveillance applications for high-alert medications such as aminoglycosides, anticoagulants, and insulin. We then present a computerized tool for high-alert medication prescriptions which serves developers, clinical pharmacists, and institutional physician leaders. The tool has two views: the surveillance view allows users to scan all CDS failures and prioritize high-risk scenarios, and the patient detail view provides context for understanding CDS failures. Entries on the surveillance tool populate automatically when CDS is used or ignored, allowing verification of prescription safety and accuracy.
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The utilization of clinical decision support (CDS) is increasing among healthcare facilities which have implemented computerized physician order entry or electronic medical records. Formal prospective evaluation of CDS implementations occurs rarely, and misuse or flaws in system design are often unrecognized. Retrospective review can identify failures but is too late to make critical corrections or initiate redesign efforts. Real-time surveillance of user responses and patient outcomes comprises one approach to give immediate feedback to CDS designers and help operate a safety net which intercepts CDS failures. We outline four types of CDS (passive alerts, interruptive alerts, order sets, and complex ordering advisors) and describe common failures with surveillance applications for high-alert medications such as aminoglycosides, anticoagulants, and insulin. We then present a computerized tool for high-alert medication prescriptions which serves developers, clinical pharmacists, and institutional physician leaders. The tool has two views: the surveillance view allows users to scan all CDS failures and prioritize high-risk scenarios, and the patient detail view provides context for understanding CDS failures. Entries on the surveillance tool populate automatically when CDS is used or ignored, allowing verification of prescription safety and accuracy.
Real-Time Surveillance for Rapid Correction of Clinical Decision Support Fail...
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It gives an individual a general Idea about Expert System and the wide variety of it's Applications.We discuss the scope of Expert System in upcoming Future in various Domains and various Challenges.Some examples are also given of a few Expert Systems.
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Unit-IV; Professional Sales Representative (PSR).pptx
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Unit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptx
Revision Expert Systems (Claudia, Federico, Natalia)
1.
2.
3.
4.
steps to the
creation of an expert system
5.
1. programming Informant
(Expert) Knowledge Engineer Knowledge Base
6.
2. Querying User
interface Inference Engine Knowledge base user
7.
8.
9.
10.
Factors in failure
of expert systems Programmers and users 2. Overconfidence Programmers 1. Lack of testing Experts 4. Missing information Experts and users 3. GIGO Stakeholders involved Factors in failure
11.
12.
13.
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