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Clinical decision support systems

Authors: Beeler Patrick Emanuel; Bates David Westfall; Hug Balthasar Luzius;

Clinical decision support systems

Abstract

Clinical decision support (CDS) systems link patient data with an electronic knowledge base in order to improve decision-making and computerised physician order entry (CPOE) is a requirement to set up electronic CDS. The medical informatics literature suggests categorising CDS tools into medication dosing support, order facilitators, point-of-care alerts and reminders, relevant information display, expert systems and workflow support. To date, CDS has particularly been recognised for improving processes. CDS successfully fostered prevention of deep-vein thrombosis, improved adherence to guidelines, increased the use of vaccinations, and decreased the rate of serious medication errors. However, CDS may introduce errors, and therefore the term “e-iatrogenesis” has been proposed to address unintended consequences. At least two studies reported severe treatment delays due to CPOE and CDS. In addition, the phenomenon of “alert fatigue” – arising from a high number of CDS alerts of low clinical significance – may facilitate overriding of potentially critical notifications. The implementation of CDS needs to be carefully planned, CDS interventions should be thoroughly examined in pilot wards only, and then stepwise introduced. A crucial feature of CPOE in combination with CDS is speed, since time consumption has been found to be a major factor determining failure. In the near future, the specificity of alerts will be improved, notifications will be prioritised and offer detailed advice, customisation of CDS will play an increasing role, and finally, CDS is heading for patient-centred decision support. The most important research question remains whether CDS is able to improve patient outcomes beyond processes.

Country
Switzerland
Keywords

medicine, Point-of-Care Systems, Reminder Systems, R, 610 Medicine & health, 11359 Institute for Regenerative Medicine (IREM), 2700 General Medicine, Decision support systems, Decision Support Systems, Clinical, Medical Order Entry Systems, Workflow, health IT, 570 Life sciences; biology, Medicine, Humans, Medication Errors

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    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    103
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
103
Top 1%
Top 10%
Top 10%
Green
Published in a Diamond OA journal