Try out personalized alert features The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems Read more The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems DSSs.
A systematic review did not find a benefit in terms of risk of death when the CDSS was combined with the electronic health record. Sustainable CDSSs features associated with improved practitioner performance include the following: However, other systematic reviews are less optimistic about the effects of CDS, with one from stating "There is a large gap between the postulated and empirically demonstrated benefits of [CDSS and other] eHealth technologies In the early days, CDSSs were conceived of as being used to literally make decisions for the clinician.
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. Typically, a CDSS makes suggestions for the clinician to look through, and the clinician is expected to pick out useful information from the presented results and discount erroneous CDSS suggestions.
An example of how a clinical decision support system might be used by a clinician is a specific type of CDSS, a DDSS diagnosis decision support systems.
A DDSS requests some of the patients data and in response, proposes a set of appropriate diagnoses. The doctor then takes the output of the DDSS and determines which diagnoses might be relevant and which are not,  and if necessary orders further tests to narrow down the diagnosis.
Doctors use these systems at point of care to help them as they are dealing with a patient, with the timing of use being either pre-diagnosis, during diagnosis, or post diagnosis.
CDSS used during diagnosis help review and filter the physician's preliminary diagnostic choices to improve their final results.
Post-diagnosis CDSS systems are used to mine data to derive connections between patients and their past medical history and clinical research to predict future events. The suggestion, which may be disregarded by either the patient or the phone operative if common sense or caution suggests otherwise, is based on the known information and an implicit conclusion about what the worst-case diagnosis is likely to be which is not always revealed to the patient, because it might well be incorrect and is not based on a medically-trained person's opinion - it is only used for initial triage purposes.
The knowledge base contains the rules and associations of compiled data which most often take the form of IF-THEN rules.
Using another interface, an advanced user could edit the knowledge base to keep it up to date with new drugs. The inference engine combines the rules from the knowledge base with the patient's data.
The communication mechanism allows the system to show the results to the user as well as have input into the system. This eliminates the need for writing rules and for expert input.
However, since systems based on machine learning cannot explain the reasons for their conclusions they are so-called "black boxes", because no meaningful information about how they work can be discerned by human inspectionmost clinicians do not use them directly for diagnoses, for reliability and accountability reasons.
Three types of non-knowledge-based systems are support vector machines, artificial neural networks and genetic algorithms. Genetic algorithms are based on simplified evolutionary processes using directed selection to achieve optimal CDSS results.
The selection algorithms evaluate components of random sets of solutions to a problem. The solutions that come out on top are then recombined and mutated and run through the process again.
This happens over and over until the proper solution is discovered. They are functionally similar to neural networks in that they are also "black boxes" that attempt to derive knowledge from patient data.
Non-knowledge-based networks often focus on a narrow list of symptoms, such as symptoms for a single disease, as opposed to the knowledge based approach which cover the diagnosis of many different diseases.
Through these initiatives, more hospitals and clinics are integrating electronic medical records EMRs and computerized physician order entry CPOE within their health information processing and storage. Consequently, the Institute of Medicine IOM promoted usage of health information technology including clinical decision support systems to advance quality of patient care.
This statistic attracted great attention to the quality of patient care.
A definition of "Meaningful use" is yet to be published. With recent effective legislations related to performance shift payment incentives, CDSS are becoming more attractive. However, with the complexity of clinical workflows and the demands on staff time high, care must be taken by the institution deploying the support system to ensure that the system becomes a fluid and integral part of the clinical workflow.
Some CDSSs have met with varying amounts of success, while others have suffered from common problems preventing or reducing successful adoption and acceptance.Computing and Communications.
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