Objective Alert fatigue is a common issue with off-the-shelf clinical decision support. Most warnings for drug–drug interactions (DDIs) are overridden or ignored, likely because they lack relevance to the patient’s clinical situation. Existing alerting systems for DDIs are often simplistic in nature or do not take the specific patient context into consideration, leading to overly sensitive alerts. The objective of this study is to develop, validate, and test DDI alert algorithms that take advantage of patient context available in electronic health records (EHRs) data. Methods Data on the rate at which DDI alerts were triggered but for which no action was taken over a 3-month period (override rates) from a single tertiary care facility were used to identify DDIs that were considered a high-priority for contextualized alerting. A panel of DDI experts developed algorithms that incorporate drug and patient characteristics that affect the relevance of such warnings. The algorithms were then implemented as computable artifacts, validated using a synthetic health records data, and tested over retrospective data from a single urban hospital. Results Algorithms and computable knowledge artifacts were developed and validated for a total of 8 high priority DDIs. Testing on retrospective real-world data showed the potential for the algorithms to reduce alerts that interrupt clinician workflow by more than 50%. Two algorithms (citalopram/QT interval prolonging agents, and fluconazole/opioid) showed potential to filter nearly all interruptive alerts for these combinations. Conclusion The 8 DDI algorithms are a step toward addressing a critical need for DDI alerts that are more specific to patient context than current commercial alerting systems. Data commonly available in EHRs can improve DDI alert specificity.
Objective: To compare possible differences in the proportion of medication errors associated with high-risk medications that were avoided by the use of automated infusion device (AID) technology in pediatric and adult intensive care unit (ICU) patients. A secondary purpose was to investigate the number of serious adverse drug events (ADEs) identified by root-cause analyses (RCA). Method: The study included pediatric and adult patients receiving high-risk medications by continuous infusion in an academic medical center with mixed medical-surgical ICUs. A retrospective evaluation of 1 year's data collected prospectively in an AID database was used to compare the proportion of medication errors avoided based on reprogramming events (2.5 times limit as a low threshold) and overrides (10 times limit as high). Information obtained from RCAs was used to compare the proportion of serious ADEs that occurred during the 5-year periods before and after AID implementation. Results: The pediatric population was 1.68 times (95% confidence interval [CI], 1.18 to 2.38) more likely to require a reprogramming event than the adult acute care population for all high-risk medications combined. Significantly more reprogramming events occurred in the pediatric patients with potassium (relative risk [RR], 2.77; 95% CI, 1.15 to 6.68) and insulin (RR, 2.73; 95% CI, 1.15 to 6.45) infusions. Additionally, there were more overrides in the pediatric compared to the adult population for the high-risk medications (RR, 1.82; 95% CI, 1.32 to 2.53). The number of serious adverse or sentinel events as identified in RCAs decreased from six before (four deemed preventable by AID technology) to three (zero preventable) after AID implementation. Conclusions: This study demonstrates that AID technology when properly used leads to reductions in medication errors and possibly serious ADEs in critically ill patients receiving high-risk medications. The technology appears to be particularly beneficial in pediatric patients with weightbased dosing strategies. However, the potential for clinicians to override the alerts remains a concern.
A Pyxis ADE reporting mechanism using the tracer drugs D50 and naloxone increased the overall reporting of ADEs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.