[35,36] Three studies used a decision-support algorithm[29] or Ba

[35,36] Three studies used a decision-support algorithm[29] or Bayesian forecasting[30,31] to advise on dosing for heparin. One of these, Mungall et al.,[31] also reported clinical outcomes; the heparin-dosing intervention MK-2206 chemical structure producing a statistically significant reduction in rates of adverse clinical events and a trend towards decreased rates of bleeding. Destache et al.[27] evaluated a clinical pharmacokinetics service for aminoglycoside therapy and assessed both prescribing

measures (dose adjustments and duration of therapy) and clinical outcomes (febrile periods and hospital length of stay). The intervention had statistically significant effects on dosing adjustments and duration of febrile periods, and showed a positive trend towards shorter hospital length of stay. Barenfanger et al.[26] tested the impact of sending electronic antibiotic sensitivity reports and alerts to pharmacists on mortality and hospital length of stay. There was some evidence that the intervention could reduce hospital length of stay. The influence of system versus user-initiation of CDSS, clinical setting (ambulatory versus hospital) and mode

of delivery (CDSS alone or multifaceted intervention) could not be assessed in this group of overwhelmingly positive studies addressing drug safety. The results of this www.selleckchem.com/products/pd-0332991-palbociclib-isethionate.html review suggest that pharmacy CDSSs have a positive effect in changing prescribing outcomes and to a lesser extent clinical outcomes. The effects were most consistent in the context of drug safety; that is, CDSSs involving

alerts of various kinds, addressing monitoring of therapy or dose adjustments for drugs with a narrow therapeutic index. These are traditional areas of activity for pharmacists. Some studies reported that CDSSs resulted in pharmacists intervening more often than would have occurred in the absence of automated systems and electronic alerts; in others the electronic decision support and patient-specific recommendations appeared Edoxaban to free up time from routine dispensing tasks and increased the time pharmacists spent discussing medication-management issues with other health professionals and patients. CDSSs were less effective for QUM interventions; that is, those promoting the choice of specific medicines or preferred medicines in particular patient populations (e.g. care suggestions for treatment of hypertension, asthma or COPD[19,23]). This review shares the limitations of other systematic reviews. While we conducted an extensive literature search, we cannot be sure we have identified all published studies. We did not seek unpublished studies or reports (‘grey literature’). Our requirement that studies include a control group means that we have not captured the experience of CDSSs that have been implemented hospital- or system-wide and not subjected to formal evaluation.

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