National standards for computerized prescriber order entry and clinical decision support: The case of drug interactions

2013 ◽  
Vol 70 (1) ◽  
pp. 59-64 ◽  
Author(s):  
David P. Mulherin ◽  
Christopher R. Zimmerman ◽  
Bruce W. Chaffee
Keyword(s):  
Decision Support ◽  
Drug Interactions ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
National Standards ◽  
Order Entry ◽  
Computerized Prescriber Order Entry

Systematic review of computerized prescriber order entry and clinical decision support

2018 ◽  
Vol 75 (23) ◽  
pp. 1909-1921 ◽  
Author(s):  
Manuel Vélez-Díaz-Pallarés ◽  
Covadonga Pérez-Menéndez-Conde ◽  
Teresa Bermejo-Vicedo
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Order Entry ◽  
Computerized Prescriber Order Entry

scholarly journals Implementation Strategies of a Quality Improvement Initiative for Hospital-Acquired Clostridioides difficile Infection Prevention

2020 ◽  
Vol 41 (S1) ◽  
pp. s279-s280
Author(s):  
Nicole Lamont ◽  
Lauren Bresee ◽  
Kathryn Bush ◽  
Blanda Chow ◽  
Bruce Dalton ◽  
...  
Keyword(s):  
Decision Support ◽  
Focus Groups ◽  
Clinical Decision Support ◽  
Implementation Strategy ◽  
Clinical Decision ◽  
Medication Administration ◽  
Quality Improvement Initiative ◽  
Order Entry ◽  
Clostridioides Difficile ◽  
Number Of Patients

Background:Clostridioides difficile infection (CDI) is the most common cause of infectious diarrhea in hospitalized patients. Probiotics have been studied as a measure to prevent CDI. Timely probiotic administration to at-risk patients receiving systemic antimicrobials presents significant challenges. We sought to determine optimal implementation methods to administer probiotics to all adult inpatients aged 55 years receiving a course of systemic antimicrobials across an entire health region. Methods: Using a randomized stepped-wedge design across 4 acute-care hospitals (n = 2,490 beds), the probiotic Bio-K+ was prescribed daily to patients receiving systemic antimicrobials and was continued for 5 days after antimicrobial discontinuation. Focus groups and interviews were conducted to identify barriers, and the implementation strategy was adapted to address the key identified barriers. The implementation strategy included clinical decision support involving a linked flag on antibiotic ordering and a 1-click order entry within the electronic medical record (EMR), provider and patient education (written/videos/in-person), and local site champions. Protocol adherence was measured by tracking the number of patients on therapeutic antimicrobials that received BioK+ based on the bedside nursing EMR medication administration records. Adherence rates were sorted by hospital and unit in 48- and 72-hour intervals with recording of percentile distribution of time (days) to receipt of the first antimicrobial. Results: In total, 340 education sessions with >1,800 key stakeholders occurred before and during implementation across the 4 involved hospitals. The overall adherence of probiotic ordering for wards with antimicrobial orders was 78% and 80% at 48 and 72 hours, respectively over 72 patient months. Individual hospital adherence rates varied between 77% and 80% at 48 hours and between 79% and 83% at 72 hours. Of 246,144 scheduled probiotic orders, 94% were administered at the bedside within a median of 0.61 days (75th percentile, 0.88), 0.47 days (75th percentile, 0.86), 0.71 days (75th percentile, 0.92) and 0.67 days (75th percentile, 0.93), respectively, at the 4 sites after receipt of first antimicrobial. The key themes from the focus groups emphasized the usefulness of the linked flag alert for probiotics on antibiotic ordering, the ease of the EMR 1-click order entry, and the importance of the education sessions. Conclusions: Electronic clinical decision support, education, and local champion support achieved a high implementation rate consistent across all sites. Use of a 1-click order entry in the EMR was considered a key component of the success of the implementation and should be considered for any implementation strategy for a stewardship initiative. Achieving high prescribing adherence allows more precision in evaluating the effectiveness of the probiotic strategy.Funding: Partnerships for Research and Innovation in the Health System, Alberta Innovates/Health Solutions Funding: AwardDisclosures: None

scholarly journals The use of a clinical decision support tool to assess the risk of QT drug–drug interactions in community pharmacies

2021 ◽  
Vol 12 ◽  
pp. 204209862199609
Author(s):  
Florine A. Berger ◽  
Heleen van der Sijs ◽  
Teun van Gelder ◽  
Patricia M. L. A. van den Bemt
Keyword(s):  
Decision Support ◽  
Drug Interactions ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Community Pharmacies ◽  
Support Tool ◽  
Interacting Agents ◽  
Before And After ◽  
Structured Approach

Introduction: The handling of drug–drug interactions regarding QTc-prolongation (QT-DDIs) is not well defined. A clinical decision support (CDS) tool will support risk management of QT-DDIs. Therefore, we studied the effect of a CDS tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. Methods: An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of three months were included. The impact of the use of a CDS tool to support the handling of QT-DDIs was studied. For each QT-DDI, handling of the QT-DDI and patient characteristics were extracted from the pharmacy information system. Primary outcome was the proportion of QT-DDIs with an intervention. Secondary outcomes were the type of interventions and the time associated with handling QT-DDIs. Logistic regression analysis was used to analyse the primary outcome. Results: Two hundred and forty-four QT-DDIs pre-CDS tool and 157 QT-DDIs post-CDS tool were included. Pharmacists intervened in 43.0% and 35.7% of the QT-DDIs pre- and post-CDS tool respectively (odds ratio 0.74; 95% confidence interval 0.49–1.11). Substitution of interacting agents was the most frequent intervention. Pharmacists spent 20.8 ± 3.5 min (mean ± SD) on handling QT-DDIs pre-CDS tool, which was reduced to 14.9 ± 2.4 min (mean ± SD) post-CDS tool. Of these, 4.5 ± 0.7 min (mean ± SD) were spent on the CDS tool. Conclusion: The CDS tool might be a first step to developing a tool to manage QT-DDIs via a structured approach. Improvement of the tool is needed in order to increase its diagnostic value and reduce redundant QT-DDI alerts. Plain Language Summary The use of a tool to support the handling of QTc-prolonging drug interactions in community pharmacies Introduction: Several drugs have the ability to cause heart rhythm disturbances as a rare side effect. This rhythm disturbance is called QTc-interval prolongation. It may result in cardiac arrest. For health care professionals, such as physicians and pharmacists, it is difficult to decide whether or not it is safe to proceed treating a patient with combinations of two or more of these QT-prolonging drugs. Recently, a tool was developed that supports the risk management of these QT drug–drug interactions (QT-DDIs). Methods: In this study, we studied the effect of this tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of 3 months were included. Results: Two hundred and forty-four QT-DDIs pre-implementation of the tool and 157 QT-DDIs post-implementation of the tool were included. Pharmacists intervened in 43.0% of the QT-DDIs before the tool was implemented and in 35.7% after implementation of the tool. Substitution of one of the interacting agents was the most frequent intervention. Pharmacists spent less time on handling QT-DDIs when the tool was used. Conclusion: The clinical decision support tool might be a first step to developing a tool to manage QT-DDIs via a structured approach.

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