Physicians' responses to computerized drug interaction alerts with password overrides

Prevalence of potential drug‒drug interactions and associated factors among elderly patients in Ethiopia: a systematic review and meta-analysis

BACKGROUND

The occurrence of potential drug‒drug interactions (pDDIs) is a serious global issue that affects all age groups, with the elderly population being the most vulnerable. This is due to their relatively high rates of comorbidity and polypharmacy, as well as physiological changes that can increase the potential for DDIs and the likelihood of adverse drug reactions. The aim of this study was to estimate the prevalence of pDDIs and associated factors among elderly patients in Ethiopia.

METHODS

A comprehensive literature search using the preferred reporting items for systematic review and meta-analysis statement was conducted on HINARI, Science Direct, Embase, PubMed/MEDLINE, Google Scholar, and Research Gate. Data were extracted via a Microsoft Excel spreadsheet and analyzed via STATA version 11.0. Egger regression tests and funnel plot analysis were used to check publication bias, and the I2 statistic was used to evaluate statistical heterogeneity. Sensitivity and subgroup analyses were also conducted to identify potential causes of heterogeneity.

RESULTS

Seven articles were analyzed, and a total of 1897 pDDIs were identified in 970 patients, resulting in an average of 1.97 DDIs per patient. The number of DDIs per patient ranged from 0.18 to 5.86. The overall prevalence of pDDIs among elderly patients was 50.69% (95% CI 18.77-82.63%). However, the prevalence of pDDIs ranged widely from 2.80 to 90.1%. When the severity of the interactions was considered, the prevalence of potential DDIs was found to be 28.74%, 70.68%, and 34.20% for major, moderate, and minor pDDIs, respectively. Polypharmacy and long hospital stays were identified as factors associated with pDDIs among elderly patients in Ethiopia.

CONCLUSIONS

The overall prevalence of pDDIs among elderly patients was high, with a wide range of prevalence rates. Moderate-severity interactions were the most prevalent. Polypharmacy and long hospital stays were identified as factors associated with pDDIs among elderly patients. The study suggests that DDIs identification database itself could have modified the DDIs prevalence rate. As a result, a single DDIs identification database needs to be authorized; otherwise, clinical knowledge should be taken into account when interpreting the information obtained.

Override rate of drug-drug interaction alerts in clinical decision support systems: A brief systematic review and meta-analysis

Primary studies have demonstrated that despite being useful, most of the drug-drug interaction (DDI) alerts generated by clinical decision support systems are overridden by prescribers. To provide more information about this issue, we conducted a systematic review and meta-analysis on the prevalence of DDI alerts generated by CDSS and alert overrides by physicians. The search strategy was implemented by applying the terms and MeSH headings and conducted in the MEDLINE/PubMed, EMBASE, Web of Science, Scopus, LILACS, and Google Scholar databases. Blinded reviewers screened 1873 records and 86 full studies, and 16 articles were included for analysis. The overall prevalence of alert generated by CDSS was 13% (CI95% 5-24%, p-value <0.0001, I^2 = 100%), and the overall prevalence of alert override by physicians was 90% (CI95% 85-95%, p-value <0.0001, I^2 = 100%). This systematic review and meta-analysis presents a high rate of alert overrides, even after CDSS adjustments that significantly reduced the number of alerts. After analyzing the articles included in this review, it was clear that the CDSS alerts physicians about potential DDI should be developed with a focus on the user experience, thus increasing their confidence and satisfaction, which may increase patient clinical safety.

The appropriateness of clinical decision support systems alerts in supporting clinical workflows: A systematic review

A CDSS generates a high number of inappropriate alerts that interrupt the clinical workflow. As a result, clinicians silence, disable, or ignore alerts, thereby undermining patient safety. Therefore, the effectiveness and appropriateness of CDSS alerts need to be evaluated. A systematic review was carried out to identify the factors that affect CDSS alert appropriateness in supporting clinical workflow. Seven electronic databases (PubMed, Scopus, ACM, Science Direct, IEEE, Ovid Medline, and Ebscohost) were searched for English language articles published between 1997 and 2018. Seventy six papers met the inclusion criteria, of which 26, 24, 15, and 11 papers are retrospective cohort, qualitative, quantitative, and mixed-method studies, respectively. The review highlights various factors influencing the appropriateness and efficiencies of CDSS alerts. These factors are categorized into technology, human, organization, and process aspects using a combination of approaches, including socio-technical framework, five rights of CDSS, and Lean. Most CDSS alerts were not properly designed based on human factor methods and principles, explaining high alert overrides in clinical practices. The identified factors and recommendations from the review may offer valuable insights into how CDSS alerts can be designed appropriately to support clinical workflow.

Prevalence of potential drug-drug interactions and associated factors among outpatients and inpatients in Ethiopian hospitals: a systematic review and meta-analysis of observational studies

BACKGROUND

Drug-drug interaction is an emerging threat to public health. Currently, there is an increase in comorbid disease, polypharmacy, and hospitalization in Ethiopia. Thus, the possibility of drug-drug interaction occurrence is high in hospitals. This study aims to summarize the prevalence of potential drug-drug interactions and associated factors in Ethiopian hospitals.

METHODS

A literature search was performed by accessing legitimate databases in PubMed/MEDLINE, Google Scholar, and Research Gate for English-language publications. To fetch further related topics advanced search was also applied in Science Direct and HINARI databases. The search was conducted on August 3 to 25, 2019. All published articles available online until the day of data collection were considered. Outcome measures were analyzed with Open Meta Analyst and CMA version statistical software. Der Simonian and Laird's random effect model, I² statistics, and Logit event rate were also performed.

RESULTS

A total of 14 studies remained eligible for inclusion in systematic review and meta-analysis. From the included studies, around 8717 potential drug-drug interactions were found in 3259 peoples out of 5761 patients. The prevalence of patients with potential drug-drug interactions in Ethiopian hospitals was found to be 72.2% (95% confidence interval: 59.1, 85.3%). Based on severity, the prevalence of major, moderate, and minor potential drug-drug interaction was 25.1, 52.8, 16.9%, respectively, also 1.27% for contraindications. The factors associated with potential drug-drug interactions were related to patient characteristics such as polypharmacy, age, comorbid disease, and hospital stay.

CONCLUSIONS

There is a high prevalence of potential drug-drug interactions in Ethiopian hospitals. Polypharmacy, age, comorbid disease, and hospital stay were the risk factors associated with potential drug-drug interactions.

Evaluating the alert appropriateness of clinical decision support systems in supporting clinical workflow

The overwhelming number of medication alerts generated by clinical decision support systems (CDSS) has led to inappropriate alert overrides, which may lead to unintended patient harm. This review highlights the factors affecting the alert appropriateness of CDSS and barriers to the fit of CDSS alert with clinical workflow. A literature review was conducted to identify features and functions pertinent to CDSS alert appropriateness using the five rights of CDSS. Moreover, a process improvement method, namely, Lean, was used as a tool to optimise clinical workflows, and the appropriate design for CDSS alert using a human automation interaction (HAI) model was recommended. Evaluating the appropriateness of CDSS alert and its impact on workflow provided insights into how alerts can be designed and triggered effectively to support clinical workflow. The application of Lean methods and tools to analyse alert efficiencies in supporting workflow in this study provides an in-depth understanding of alert-workflow fit problems and their root cause, which is required for improving CDSS design. The application of the HAI model is recommended in the design of CDSS alerts to support various levels and stages of alert automations, namely, information acquisition and analysis, decision action and action implementation.

Think time: A novel approach to analysis of clinicians' behavior after reduction of drug-drug interaction alerts

OBJECTIVES

Pharmacologic interaction alerting offers the potential for safer medication prescribing, but research reveals persistent concerns regarding alert fatigue. Research studies have tried various strategies to resolve this problem, with low overall success. We examined the effects of targeted alert reduction on clinician behavior in a resource constrained hospital.

METHODS

A physician and a pharmacy informaticist reduced alert levels of several drug-drug interactions (DDI) that clinicians almost always overrode with approval from and knowledge of the medical staff. This study evaluated the behavioral changes in prescribers and non-prescribers as measured by "think time", a new metric for evaluating the resolution time for an alert, before and after suppression of selected DDI alerts.

RESULTS

The user-seen DDI alert rate decreased from 9.98% of all orders to 9.20% (p=0.0001) with an overall volume reduction of 10.3%. There was no statistical difference in the reduction of cancelled (-10.00%) vs. proceed orders (-11.07%). Think time decreased overall by 0.61s (p<0.0001). Think time unexpectedly increased for cancelled orders 1.00s which while not statistically significant (p=0.28) is generally thought to be clinically noteworthy. For overrides, think time decreased 0.67s which was significant (p<0.0001). Think time lowered for both prescribers and non-prescribers. Targeted specialists had shorter think times initially, which shortened more than non-targeted specialists.

CONCLUSIONS

Targeted DDI alert reductions reduce alert burden overall, and increase net efficiency as measured by think time for all prescribers better than for non-prescribers. Think time may increase when cancelling or changing orders in response to DDI alerts vs. a decision to override an alert.