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Concept
Clinical decision support system
Summary
A clinical decision support system (CDSS) is a health information technology that provides clinicians, staff, patients, and other individuals with knowledge and person-specific information to help health and health care. CDSS encompasses a variety of tools to enhance decision-making in the clinical workflow. These tools include computerized alerts and reminders to care providers and patients, clinical guidelines, condition-specific order sets, focused patient data reports and summaries, documentation templates, diagnostic support, and contextually relevant reference information, among other tools. CDSSs constitute a major topic in artificial intelligence in medicine.
A clinical decision support system is an active knowledge system that uses variables of patient data to produce advice regarding health care. This implies that a CDSS is simply a decision support system focused on using knowledge management.
The main purpose of modern CDSS is to assist clinicians at the point of care. This means that clinicians interact with a CDSS to help to analyze and reach a diagnosis based on patient data for different diseases.
In the early days, CDSSs were conceived to make decisions for the clinician literally. The clinician would input the information and wait for the CDSS to output the "right" choice, and the clinician would simply act on that output. However, the modern methodology of using CDSSs to assist means that the clinician interacts with the CDSS, utilizing both their knowledge and the CDSS's, better to analyse the patient's data than either human or CDSS could make on their own. Typically, a CDSS makes suggestions for the clinician to review, and the clinician is expected to pick out useful information from the presented results and discount erroneous CDSS suggestions.
The two main types of CDSS are knowledge-based and non-knowledge-based:
An example of how a clinician might use a clinical decision support system is a diagnosis decision support system (DDSS). DDSS requests some of the patients' data and, in response, proposes a set of appropriate diagnoses.
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Ontological neighbourhood
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