Evaluation of context-specific alerts for potassium-increasing drug-drug interactions: A pre-post study

Muylle, Katoo M.; Gentens, Kristof; Dupont, Alain; Cornu, Pieter

doi:10.1016/j.ijmedinf.2019.104013

Cited by 14 publications

(12 citation statements)

References 37 publications

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“…In the approach, the development of context-specific alarms for potassium-increasing DDIs and patient-specific risk assessments for hyperkalemia will help reduce the number of unnecessary alerts. This finding broadly supports the work in this area by KM Muylle et al (2020) who found that the optimized CDS, which uses context factors for the individual risk assessment of hyperkalemia, significantly reduced the alert burden by 92.8% [35].…”

Section: Discussionsupporting

confidence: 87%

The Overriding of Computerized Physician Order Entry (CPOE) Drug Safety Alerts Fired by the Clinical Decision Support (CDS) Tool: Evaluation of Appropriate Responses and Alert Fatigue Solutions

Naeem¹,

Alwadie²,

Alshehri³

et al. 2022

Cureus

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IntroductionMost computerized physician order entry (CPOE) software come with clinical decision-support components (CDS) that provide prescribers assistance and notify them about adverse drug reactions. An excessive number of alerts in a repeated and non-relevant manner leads to alert fatigue and enforces physicians and pharmacists to alert overrides. King Abdulaziz Medical City (KAMC) in Jeddah still reports a higher percentage of drug alerts overridden by clinicians and pharmacists. Thus, this study was conducted to evaluate CDS alerts overriding and to determine which alerts are clinically irrelevant and need modifications. MethodsThe study was carried out in the inpatient setting at KAMC in Jeddah, from September 1, 2020, to December 31, 2020. It was designed as a retrospective chart review study that included all red alerts that required comments and were overridden by a physician and pharmacist. ResultsAmong 11350 red alerts, potential drug-drug interaction (pDDI), dose, and allergy alerts represent 57%, 41%, and 2%, respectively, of the total alerts. The most common drug-drug interactions (DDIs) in category X were proton pump inhibitors and clopidogrel (9.9%). The appropriate response by prescribers and pharmacists toward allergy alerts was associated with the highest odds compared with the other alerts (p < 0.05) while there is a significant decrease in the odds of appropriate action being taken by both prescribers and pharmacists in the dose screen alerts (p < 0.05). Among all clinical specialties, there is an increased odds of appropriate action being taken by residents and fellows for allergy and dose alerts, respectively, compared to other groups (p < 0.05). For diminishing the unnecessary alerts, we provided 14 alert refinements strategies and advised turning off four alerts. Applying this will terminate 32% of irrelevant alerts. ConclusionOur study's findings indicated that a substantial number of alerts are ignored, and the rate of appropriateness varies significantly by alert type and prescriber level.

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Section: Discussionsupporting

confidence: 87%

The Overriding of Computerized Physician Order Entry (CPOE) Drug Safety Alerts Fired by the Clinical Decision Support (CDS) Tool: Evaluation of Appropriate Responses and Alert Fatigue Solutions

Naeem¹,

Alwadie²,

Alshehri³

et al. 2022

Cureus

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“…To go further, this taxonomic model hierarchically classifying modulators of alert acceptance has to be understood as a starting point to receive more summarized evidence and to understand context and relationships of individual modulators influencing alert acceptance. The complex intervention reported by Muylle and coworkers consists for example of parameters that can be allocated to various factors (i.e., inclusion of patient-specific context factors, tiering of alert according to severity, and filtering, clustering or deactivation of alerts) in this model 60,64 and as they evaluated several factors at one time and although the intervention had a significant impact overall, the impact of each single factor was only partly sufficient for significance.…”

Section: Further Implications On Alert Acceptancementioning

confidence: 99%

“…This means that bias and a potential risk of inconsistency cannot be discounted, despite two reviewers having assigned the modulators independently and discussed differences until congruency was reached. It is at least as important to mention that extracted parameters from complex interventions composed of different parameters were allocated to various 64 or the most appropriate factors 60 according to the description in the original article. Each quantitative parameter is explained in ►Table 1 so that complex interventions are also presented as transparent as the original article allows.…”

Section: Limitationsmentioning

confidence: 99%

Modulators Influencing Medication Alert Acceptance: An Explorative Review

2022

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Objectives Clinical decision support systems (CDSSs) use alerts to enhance medication safety and reduce medication error rates. A major challenge of medication alerts is their low acceptance rate, limiting their potential benefit. A structured overview about modulators influencing alert acceptance is lacking. Therefore, we aimed to review and compile qualitative and quantitative modulators of alert acceptance and organize them in a comprehensive model. Methods In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline, a literature search in PubMed was started in February 2018 and continued until October 2021. From all included articles, qualitative and quantitative parameters and their impact on alert acceptance were extracted. Related parameters were then grouped into factors, allocated to superordinate determinants, and subsequently further allocated into five categories that were already known to influence alert acceptance. Results Out of 539 articles, 60 were included. A total of 391 single parameters were extracted (e.g., patients' comorbidity) and grouped into 75 factors (e.g., comorbidity), and 25 determinants (e.g., complexity) were consequently assigned to the predefined five categories, i.e., CDSS, care provider, patient, setting, and involved drug. More than half of all factors were qualitatively assessed (n = 21) or quantitatively inconclusive (n = 19). Furthermore, 33 quantitative factors clearly influenced alert acceptance (positive correlation: e.g., alert type, patients' comorbidity; negative correlation: e.g., number of alerts per care provider, moment of alert display in the workflow). Two factors (alert frequency, laboratory value) showed contradictory effects, meaning that acceptance was significantly influenced both positively and negatively by these factors, depending on the study. Interventional studies have been performed for only 12 factors while all other factors were evaluated descriptively. Conclusion This review compiles modulators of alert acceptance distinguished by being studied quantitatively or qualitatively and indicates their effect magnitude whenever possible. Additionally, it describes how further research should be designed to comprehensively quantify the effect of alert modulators.

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“…Several approaches to decreasing over-alerting and alert fatigue have been developed and tested. These include (1) changing the way alerts are displayed [19][20][21][22][23][24][25], (2) refining the alerts' relevance by filtering them according to clinical veracity [10,11,17,20,21,[26][27][28][29] or postalert quality assessment by a group of practitioners [29], and (3) managing chronological aspects [19][20][21]23,24,30]. It has also been suggested that the relevance of alerts can be increased by taking into account the level of evidence for the DDI [20,21] and the seriousness of the outcome [10,17,20,21,27,29,31].…”

Section: Introductionmentioning

confidence: 99%

Towards The Automated, Empirical Filtering of Drug-Drug Interaction Alerts in Clinical Decision Support Systems: Historical Cohort Study of Vitamin K Antagonists

Chazard¹,

Boudry²,

Beeler

et al. 2021

JMIR Med Inform

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Background Drug-drug interactions (DDIs) involving vitamin K antagonists (VKAs) constitute an important cause of in-hospital morbidity and mortality. However, the list of potential DDIs is long; the implementation of all these interactions in a clinical decision support system (CDSS) results in over-alerting and alert fatigue, limiting the benefits provided by the CDSS. Objective To estimate the probability of occurrence of international normalized ratio (INR) changes for each DDI rule, via the reuse of electronic health records. Methods An 8-year, exhaustive, population-based, historical cohort study including a French community hospital, a group of Danish community hospitals, and a Bulgarian hospital. The study database included 156,893 stays. After filtering against two criteria (at least one VKA administration and at least one INR laboratory result), the final analysis covered 4047 stays. Exposure to any of the 145 drugs known to interact with VKA was tracked and analyzed if at least 3 patients were concerned. The main outcomes are VKA potentiation (defined as an INR≥5) and VKA inhibition (defined as an INR≤1.5). Groups were compared using the Fisher exact test and logistic regression, and the results were expressed as an odds ratio (95% confidence limits). Results The drugs known to interact with VKAs either did not have a statistically significant association regarding the outcome (47 drug administrations and 14 discontinuations) or were associated with significant reduction in risk of its occurrence (odds ratio<1 for 18 administrations and 21 discontinuations). Conclusions The probabilities of outcomes obtained were not those expected on the basis of our current body of pharmacological knowledge. The results do not cast doubt on our current pharmacological knowledge per se but do challenge the commonly accepted idea whereby this knowledge alone should be used to define when a DDI alert should be displayed. Real-life probabilities should also be considered during the filtration of DDI alerts by CDSSs, as proposed in SPC-CDSS (statistically prioritized and contextualized CDSS). However, these probabilities may differ from one hospital to another and so should probably be calculated locally.

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Evaluation of context-specific alerts for potassium-increasing drug-drug interactions: A pre-post study

Cited by 14 publications

References 37 publications

The Overriding of Computerized Physician Order Entry (CPOE) Drug Safety Alerts Fired by the Clinical Decision Support (CDS) Tool: Evaluation of Appropriate Responses and Alert Fatigue Solutions

The Overriding of Computerized Physician Order Entry (CPOE) Drug Safety Alerts Fired by the Clinical Decision Support (CDS) Tool: Evaluation of Appropriate Responses and Alert Fatigue Solutions

Modulators Influencing Medication Alert Acceptance: An Explorative Review

Towards The Automated, Empirical Filtering of Drug-Drug Interaction Alerts in Clinical Decision Support Systems: Historical Cohort Study of Vitamin K Antagonists

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