Modulators Influencing Medication Alert Acceptance: An Explorative Review

Abstract

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.

Fig. 1

PRISMA flowchart describing the results of the literature search conducted to identify articles discussing modulators influencing alert acceptance (referred to Moher and coworkers 25 ).

Fig. 2

Overview of all modulators of alert acceptance classified by categories, determinants, and factors. Categories and determinants are ordered by the total number of parameters in parentheses, quantitative factors are shown on the left, and qualitative factors on the right ( green filled squares : quantitative, consistent factor showing positive correlation with alert acceptance; red filled squares : quantitative, consistent factor showing negative correlation with alert acceptance; yellow filled squares : quantitative, inconsistent factor showing positive and negative correlation with alert acceptance; gray filled squares : quantitative, inconclusive factors without significant positive or negative assessment of alert acceptance; white squares : qualitative factors without any quantitative assessment of alert acceptance; number of parameters in parentheses : number labeled with “*” presents the number of parameters with statistically significant effect on alert acceptance; ↑: positive correlation with alert acceptance; ↓: negative correlation with alert acceptance; ↔: no significant correlation with alert acceptance; numbers without “*” describe the number of quantitative, inconclusive (↔), and qualitative parameters within this factor); ^# several modulators were grouped to one single intervention; lab: laboratory.