An Investigation of Drug-Drug Interaction Alert Overrides at a Pediatric Hospital

Design, development, implementation, and evaluation of a severe drug-drug interaction alert system in the ICU: An analysis of acceptance and override rates

BACKGROUND AND OBJECTIVE

The override rate of Drug-Drug Interaction Alerts (DDIA) in Intensive Care Units (ICUs) is very high. Therefore, this study aimed to design, develop, implement, and evaluate a severe Drug-Drug Alert System (DDIAS) in a system of ICUs and measure the override rate of this system.

METHODS

This is a cross-sectional study that details the design, development, implementation, and evaluation of a DDIAS for severe interactions into a Computerized Provider Order Entry (CPOE) system in the ICUs of Nemazee general teaching hospitals in 2021. The patients exposed to the volume of DDIAS, acceptance and overridden of DDIAS, and usability of DDIAS have been collected. The study was approved by the local Institutional Review Board (IRB) and; the ethics committee of Shiraz University of Medical Science on date: 2019-11-23 (Approval ID: IR.SUMS.REC.1398.1046).

RESULTS

The knowledge base of the DDIAS contains 9,809 severe potential drug-drug interactions (pDDIs). A total of 2672 medications were prescribed in the population study. The volume and acceptance rate for the DDIAS were 81 % and 97.5 %, respectively. The override rate was 2.5 %. The mean System Usability Scale (SUS) score of the DDIAS was 75.

CONCLUSION

This study demonstrates that implementing high-risk DDIAS at the point of prescribing in ICUs improves adherence to alerts. In addition, the usability of the DDIAS was reasonable. Further studies are needed to investigate the establishment of severe DDIAS and measure the prescribers' response to DDIAS on a larger scale.

An Evaluation of Drug-Drug Interaction Alerts Produced by Clinical Decision Support Systems in a Tertiary Hospital

Introduction Drug-drug interactions (DDIs) have the potential to harm patients. Hence, DDI alerts are meant to prevent harm; as a result, their usefulness is reduced when most alerts displayed to providers are ignored. This study aims to explore the rates and reasons for overriding alerts of DDI. Methods This is a retrospective study of DDI alert overrides that occurred between January 2020 and December 2020 within the inpatient medical records at a tertiary hospital, Medina City, Kingdom of Saudi Arabia. Results A total of 7,098 DDI alerts were generated from inpatient settings, of which 6,551(92.2%) were overridden by the physicians at the point of prescribing. "Will Monitor as Recommended" (33%) was the most common reason for the override, followed by 'Will Adjust the Dose as Recommended (27.1%)," "The Patient Has Already Tolerated the Combination" (25.7%), and "No Overridden Reason Selected" (13.0%). Discussion The DDI alert overriding is still high and is comparable to other studies. However, this study reveals that physicians are ready to deal with the consequences of around 58% of DDI alerts. Additionally, 13% of physicians were not willing to report the reason for overriding. This indicates an urgent need to review and restructure the DDI alert system. Conclusion The DDI alert override rates are high, and this is undesirable. It is recommended to revise the DDI alert system. Future studies should dig deep for real reasons for overriding and seek inputs from all stakeholders, including developing actionable metrics to track and monitor DDI alerting system.

Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts

Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive firings, optimize alert effectiveness, and establish quality governance at our institutions. We also discuss the importance of a holistic view of the alerting ecosystem beyond the electronic health record.

Prescribers’ reported acceptance and use of drug-drug interaction alerts: An Australian survey

Drug-drug interaction (DDI) alerts are frequently included in electronic medical record (eMR) systems to provide users with relevant information and guidance at the point of care. In this study, we aimed to examine views of DDI alerts among prescribers, including junior doctors, registrars and senior doctors, across Australia. A validated survey for assessing prescribers’ reported acceptance and use of DDI alerts was distributed among researcher networks and in newsletters. Fifty useable responses were received, more than half ( n = 28) from senior doctors. Prescribers at all levels expected DDI alerts to improve performance but junior doctors reported that this was at a high cost, with respect to time and effort. Senior doctors and registrars reported rarely reading alerts and rarely changing prescribing decisions based on alerts. Respondents identified a number of problems with current alerts including limited relevance, repetition, and poor design, highlighting some clear areas for alert improvement.

Evaluation of an optimized context-aware clinical decision support system for drug-drug interaction screening

OBJECTIVE

Evaluation of the effect of six optimization strategies in a clinical decision support system (CDSS) for drug-drug interaction (DDI) screening on alert burden and alert acceptance and description of clinical pharmacist intervention acceptance.

METHODS

Optimizations in the new CDSS were the customization of the knowledge base (with addition of 67 extra DDIs and changes in severity classification), a new alert design, required override reasons for the most serious alerts, the creation of DDI-specific screening intervals, patient-specific alerting, and a real-time follow-up system of all alerts by clinical pharmacists with interventions by telephone was introduced. The alert acceptance was evaluated both at the prescription level (i.e. prescription acceptance, was the DDI prescribed?) and at the administration level (i.e. administration acceptance, did the DDI actually take place?). Finally, the new follow-up system was evaluated by assessing the acceptance of clinical pharmacist's interventions.

RESULTS

In the pre-intervention period, 1087 alerts (92.0 % level 1 alerts) were triggered, accounting for 19 different DDIs. In the post-intervention period, 2630 alerts (38.4 % level 1 alerts) were triggered, representing 86 different DDIs. The relative risk forprescription acceptance in the post-intervention period compared to the pre-intervention period was 4.02 (95 % confidence interval (CI) 3.17-5.10; 25.5 % versus 6.3 %). The relative risk for administration acceptance was 1.16 (95 % CI 1.08-1.25; 54.4 % versus 46.7 %). Finally, 86.9 % of the clinical pharmacist interventions were accepted.

CONCLUSION

Six concurrently implemented CDSS optimization strategies resulted in a high alert acceptance and clinical pharmacist intervention acceptance. Administration acceptance was remarkably higher than prescription acceptance.

Drug-drug interactions between salvianolate injection and aspirin based on their metabolic enzymes

BACKGROUND

It is unclear whether the combination of traditional Chinese medicine and Western medicine leads to interactions in pharmacokinetics (PKs) and pharmacodynamics (PDs). In this study, the influence of salvianolate and aspirin on metabolic enzymes, and the relationship between the blood concentration and pharmacodynamic indexes, were determined.

METHOD

In this, randomized, parallel-grouped, single-center clinical trial, 18 patients with coronary heart disease were randomly allocated into three groups: aspirin (AP) group, salvianolate (SV) group, and combination (A + S) group. All treatment courses lasted for 10 days, and blood samples were acquired before and after administration at different timepoints. The expression of catechol-O-methyltransferase (COMT), CD62p, procaspase-activating compound 1 (PAC-1), P2Y12, phosphodiesterase, and mitogen-activated protein kinase 8 (MAPK8) were compared with variance analysis The blood concentrations were analyzed by ultra-performance liquid chromatography-tandem mass spectrometry.

RESULTS

Sixteen subjects completed the study. No significant difference in COMT was found among groups, although there was a decrease in the SV group. The PK results indicated that the absorption time of salicylic acid was shortened and the AUC_0-∞ decreased and the elimination time of salvianolic acid B was prolonged and the AUC_0-∞ decreased. The PD results declined after administration. A significant difference was found in MAPK8, CD62p, and P2Y12 expression. Compared with the SV group, a significant difference in P2Y12 in the A + S group was found.

CONCLUSION

A pharmacokinetic drug-drug interaction was found in the aspirin and salvianolate combination. Pharmacodynamically, there was no difference between the A + S and AP groups. However, P2Y12 expression in the combination group was superior to that in the SV group.

TRIAL REGISTRATION NUMBERS

The trial was registered on October 9, 2017, ClinicalTrials.gov, NCT03306550. https://register.clinicaltrials.gov/prs/app/action/SelectProtocol?sid=S0007D8H&selectaction=Edit&uid=U0003QY8&ts=2&cx=oiuc9g.

Prevalence of Clinically Significant Drug-Drug Interactions Across US Children's Hospitals

BACKGROUND

Little is known about the prescribing of medications with potential drug-drug interactions (DDIs) in the pediatric population. The objective of this study was to determine the prevalence and variation of prescribing medications with clinically significant DDIs across children's hospitals in the United States.

METHODS

We performed a retrospective cohort study of patients <26 years of age who were discharged from 1 of 52 US children's hospitals between January 2016 and December 2018. Fifty-three drug pairings with clinically significant DDIs in children were evaluated. We identified patient-level risk factors associated with DDI using multivariable logistic regression. Adjusted hospital-level rates of DDI exposure were derived by using a generalized linear mixed-effects model, and DDI exposure variations were examined across individual hospitals.

RESULTS

Across 52 children's hospitals, 47 414 (2.0%) hospitalizations included exposure to a DDI pairing (34.9 per 1000 patient-days) during the study period. One-quarter of pairings were considered contraindicated (risk grade X). After adjusting for hospital and clinical factors, there was wide variation in the percentage of DDI prescribing across hospitals, ranging from 1.05% to 4.92%. There was also substantial hospital-level variation of exposures to individual drug pairings. Increasing age, number of complex chronic conditions, length of stay, and surgical encounters were independently associated with an increased odds of DDI exposure.

CONCLUSIONS

Patients hospitalized at US children's hospitals are frequently exposed to medications with clinically significant DDIs. Exposure risk varied substantially across hospitals. Further study is needed to determine the rate of adverse events due to DDI exposures and factors amenable for interventions promoting safer medication use.

Pharmacology of enalapril in children: a review

Enalapril is an angiotensin-converting enzyme (ACE) inhibitor that is used for the treatment of (paediatric) hypertension, heart failure and chronic kidney diseases. Because its disposition, efficacy and safety differs across the paediatric continuum, data from adults cannot be automatically extrapolated to children. This review highlights paediatric enalapril pharmacokinetic data and demonstrates that these are inadequate to support with certainty an age-related effect on enalapril/enalaprilat pharmacokinetics. In addition, our review shows that evidence to support effective and safe prescribing of enalapril in children is limited, especially in young children and heart failure patients; studies in these groups are either absent or show conflicting results. We provide explanations for observed differences between age groups and indications, and describe areas for future research.

Potential drug-drug interactions in hospitalized pediatric patients with respiratory disorders: a retrospective review of clinically important interactions

Background Hospitalized pediatric patients are at an increased risk of experiencing potential drug-drug interactions (pDDIs) due to polypharmacy and the unlicensed and off-label administration of drugs. The aim of this study is to characterize clinically significant pDDIs in pediatric patients hospitalized in a tertiary respiratory center. Methods A retrospective analysis of medications prescribed to pediatric patients admitted to the pediatric ward (PW) and pediatric intensive care unit (PICU) of a respiratory referral center was carried out over a six-month period. The pDDIs were identified using the Lexi-Interact database and considered as clinically relevant according to the severity rating as defined in the database. Frequency, drug classes, mechanisms, clinical managements, and risk factors were recorded for these potential interactions. Results Eight hundred and forty-five pDDIs were identified from the analysis of 176 prescriptions. Of the total pDDIs, 10.2% in PW and 14.6% in PICU were classified as clinically significant. Anti-infective agents and central nervous system drugs were the main drug classes involved in clinically significant pDDIs as object and/or precipitant drugs. A higher number of medications [odds ratio (OR): 4.8; 95% confidence interval (CI): 2.0-11.4; p < 0.001] and the existence of a nonrespiratory disease, which led to a respiratory disorder (OR: 3.8; 95% CI: 1.40-10.4; p < 0.05), were the main risk factors associated with an increased incidence of pDDIs. Conclusions A high and similar risk of pDDIs exists in pediatric patients with respiratory disorders hospitalized in PW and PICU. The patients prescribed a higher number of medications and presenting respiratory symptoms induced by a nonrespiratory disease require extra care and monitoring. Pediatricians should be educated about clinically significant DDIs for highly prescribed medications in their settings in order to take preventive measures and safeguard patient safety.

Reducing Interruptive Alert Burden Using Quality Improvement Methodology

BACKGROUND

Increased adoption of electronic health records (EHR) with integrated clinical decision support (CDS) systems has reduced some sources of error but has led to unintended consequences including alert fatigue. The "pop-up" or interruptive alert is often employed as it requires providers to acknowledge receipt of an alert by taking an action despite the potential negative effects of workflow interruption. We noted a persistent upward trend of interruptive alerts at our institution and increasing requests for new interruptive alerts.

OBJECTIVES

Using Institute for Healthcare Improvement (IHI) quality improvement (QI) methodology, the primary objective was to reduce the total volume of interruptive alerts received by providers.

METHODS

We created an interactive dashboard for baseline alert data and to monitor frequency and outcomes of alerts as well as to prioritize interventions. A key driver diagram was developed with a specific aim to decrease the number of interruptive alerts from a baseline of 7,250 to 4,700 per week (35%) over 6 months. Interventions focused on the following key drivers: appropriate alert display within workflow, clear alert content, alert governance and standardization, user feedback regarding overrides, and respect for user knowledge.

RESULTS

A total of 25 unique alerts accounted for 90% of the total interruptive alert volume. By focusing on these 25 alerts, we reduced interruptive alerts from 7,250 to 4,400 per week.

CONCLUSION

Systematic and structured improvements to interruptive alerts can lead to overall reduced interruptive alert burden. Using QI methods to prioritize our interventions allowed us to maximize our impact. Further evaluation should be done on the effects of reduced interruptive alerts on patient care outcomes, usability heuristics on cognitive burden, and direct feedback mechanisms on alert utility.