Abstract
Purpose
The aim of this study was to perform a systematic review and meta-analysis of studies
performed in primary care centers and hospital facilities that evaluated the effectiveness
of computerized clinical decision support systems (CDSSs) in decision making on the
prescription of any given antibiotic.
Methods
We conducted a search of the MEDLINE and EMBASE databases. A meta-analysis was then
conducted of all variables with results reported in >2 studies.
Findings
A total of 42 of the 46 studies included in the review identified a statistically
significant advantage for CDSSs in ≥1 study variables. The effect of CDSSs on the
percentage accuracy of the antibiotic spectrum prescribed empirically with respect
to the microbial agent's susceptibility, which is one of the most frequently studied
outcome variables, was examined in 7 studies, all undertaken in hospital settings.
In all these studies but one, CDSSs resulted in a statistically significant increase
in percentage accuracy. The other study variables present in >2 studies had more inconsistent
results. Although the results of the meta-analysis of the variables percentage accuracy,
antibiotic prescription rate in hospital, percentage adherence to antibiotic prescription
guidelines in primary care or hospital, and percentage of inappropriate prescriptions
for antibiotics in primary care were statistically significantly favorable to CDSSs;
in the case of hospital length of stay and mortality, they were favorable although
not statistically significantly.
Implications
CDSSs appear to be useful for variables such as the percentage of appropriate empirical
treatment in the hospital setting or to induce changes in antibiotics prescription
rate. Even so, more better quality studies are required to draw clearer conclusions
in respect of morbidity and mortality outcome variables and other settings.
Keywords
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Article info
Publication history
Published online: February 27, 2019
Accepted:
January 30,
2019
Received in revised form:
January 16,
2019
Received:
September 3,
2018
Identification
Copyright
© 2019 Elsevier Inc. All rights reserved.
