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The emergency department (ED) is a high-risk setting for the occurrence of medication discrepancies (MDs) due to inconsistencies between real and documented chronic medication therapies. A clinical pharmacist (CP) improves medication safety by performing a structured medication reconciliation on ED admission. The main objective was to identify the frequency and type of MDs in the chronic medication therapy by comparing the medication displayed in the home medication module of the electronic medical record and in the genereal practitioner's (GP) referral letter with the best possible medication history by performing a structured medication reconciliation on ED admission. This prospective, monocentric, interventional study was carried out in the ED of a tertiary care university hospital in Brussels, Belgium. Inclusion criteria were patients of at least 65 years, polypharmacy, ED admission between 8 a.m. and 4 p.m. on weekdays, hospitalization and signed informed consent. During 24 days, a CP performed a structured medication reconciliation in order to obtain the best possible medication history and registered all MDs. The CP compared the best possible medication history with the home medication module and the GP's referral letter and registered the different types of MDs. Eighty-three patients were included. The median number of medications in the home medication module and the best possible medication history was significantly different {7.0 [interquartile range (IQR), 5.0-11.0] vs. 8.0 (IQR, 6.0-11.0)/patient; P < 0.0001} with a median of 5.0 (IQR, 3.0-8.0) MDs per patient. Main MDs were omission (38.8%), addition (18.4%) and a deviant administration time (15.2%). Only 22.9% of patients ( N = 19) had a GP's referral letter containing their chronic medication therapy. The median number of medications in the GP's referral letter and the best possible medication history were significantly different [6.0 (IQR, 4.0-9.0) vs. 8.0 (IQR, 7.0-11.0)/patient; P < 0.0001] with a median of 6.0 (IQR, 5.0-11.0) MDs per patient. Main MDs were omissions (39.9%), deviant frequencies (35.3%) and doses (16.7%). A CP, integrated in a multidisciplinary ED team, enhances medication safety by intercepting MDs on ED admission. Few patients possess a GP's referral letter containing their chronic medication therapy and when they do, the accuracy and completeness are poor.

Aims Many clinical decision support systems trigger warning alerts for drug-drug interactions potentially leading to QT prolongation and torsades de pointes (QT-DDIs). Unfortunately, there is overalerting and underalerting because stratification is only based on a fixed QT-DDI severity level. We aimed to improve QT-DDI alerting by developing and validating a risk prediction model considering patient- and drug-related factors. Methods We fitted 31 predictor candidates to a stepwise linear regression for 1000 bootstrap samples and selected the predictors present in 95% of the 1000 models. A final linear regression model with those variables was fitted on the original development sample (350 QT-DDIs). This model was validated on an external dataset (143 QT-DDIs). Both true QTc and predicted QTc were stratified into three risk levels (low, moderate and high). Stratification of QT-DDIs could be appropriate (predicted risk = true risk), acceptable (one risk level difference) or inappropriate (two risk levels difference). Results The final model included 11 predictors with the three most important being use of antiarrhythmics, age and baseline QTc. Comparing current practice to the prediction model, appropriate stratification increased significantly from 37% to 54% appropriate QT-DDIs (increase of 17.5% on average [95% CI +5.4% to +29.6%], padj = 0.006) and inappropriate stratification decreased significantly from 13% to 1% inappropriate QT-DDIs (decrease of 11.2% on average [95% CI −17.7% to −4.7%], padj ≤ 0.001). Conclusion The prediction model including patient- and drug-related factors outperformed QT alerting based on QT-DDI severity alone and therefore is a promising strategy to improve DDI alerting.

In clinical practice, many drug therapies are associated with prolongation of the QT interval. In literature, estimation of the risk of prescribing drug-induced QT prolongation is mainly executed by means of logistic regression; only one paper reported the use of machine learning techniques. In this paper, we compare the performance of both techniques on the same dataset. High risk for QT prolongation was defined as having a corrected QT interval (QTc) ≥ 450 ms or ≥ 470 ms for respectively male and female patients. Both conventional statistical methods (CSM) and machine learning techniques (MLT) were used. All algorithms were validated internally and with a hold-out dataset of respectively 512 and 102 drug-drug interactions with possible drug-induced QTc prolongation. MLT outperformed the best CSM in both internal and hold-out validation. Random forest and Adaboost classification performed best in the hold-out set with an equal harmonic mean of sensitivity and specificity (HMSS) of 81.2% and an equal accuracy of 82.4% in a hold-out dataset. Sensitivity and specificity were both high (respectively 75.6% and 87.7%). The most important features were baseline QTc value, C-reactive protein level, heart rate at baseline, age, calcium level, renal function, serum potassium level and the atrial fibrillation status. All CSM performed similarly with HMSS varying between 60.3% and 66.3%. The overall performance of logistic regression was 62.0%. MLT (bagging and boosting) outperform CSM in predicting drug-induced QTc prolongation. Additionally, 19.2% was gained in terms of performance by random forest and Adaboost classification compared to logistic regression (the most used technique in literature in estimating the risk for QTc prolongation). Future research should focus on testing the classification on fully external data, further exploring potential of other (new) machine and deep learning models and on generating data pipelines to automatically feed the data to the classifier used.

The current drug allergy documentation module in the electronic health record of our institution is in a free-text format. Two versions of a structured and coded drug allergy documentation module were developed. Twenty-five physicians tested the three interfaces via 3x5 test scenarios. The usability was measured for each interface with a system usability scale questionnaire. Both new versions scored significantly better than the current free-text version. User feedback will be used to further optimize the new module.

Ensemble modeling is an increasingly popular data science technique that combines the knowledge of multiple base learners to enhance predictive performance. In this paper, the idea was to increase predictive performance by holding out three algorithms when testing multiple classifiers: (a) the best overall performing algorithm (based on the harmonic mean of sensitivity and specificity (HMSS) of that algorithm); (b) the most sensitive model; and (c) the most specific model. This approach boils down to majority voting between the predictions of these three base learners. In this exemplary study, a case of identifying a prolonged QT interval after administering a drug-drug interaction with increased risk of QT prolongation (QT-DDI) is presented. Performance measures included accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Overall performance was measured by calculating the HMSS. Results show an increase in all performance measure characteristics compared to the original best performing algorithm, except for specificity where performance remained stable. The presented approach is fairly simple and shows potential to increase predictive performance, even without adjusting the default cut-offs to differentiate between high and low risk cases. Future research should look at a way of combining all tested algorithms, instead of using only three. Similarly, this approach should be tested on a multiclass prediction problem.

Background: The quality of allergy documentation in electronic health records is frequently poor. Objective: To compare the usability of 3 graphical user interfaces (GUIs) for drug allergy documentation. Methods: Physicians tested 3 GUIs by means of 5 fictional drug allergy scenarios: the current GUI (GUI 0), using mainly free-text, and 2 new coded versions (GUI 1 and GUI 2) asking information on allergen category, specific allergen, symptom(s), symptom onset, timing of initial reaction, and diagnosis status with a semiautomatic delabeling feature. Satisfaction was measured by the System Usability Scale questionnaire, efficiency by time to complete the tasks, and effectiveness by a task completion score. Posttest interviews provided more in-depth qualitative feedback. Results: Thirty physicians from 7 different medical specialties and with varying degrees of experience participated. The mean System Usability Scale scores for GUI 1 (77.25, adjective rating “Good”) and GUI 2 (78.42, adjective rating “Good”) were significantly higher than for GUI 0 (56.58, adjective rating “OK”) (Z, 6.27, P adj < .001 and Z, 6.62, P adj < .001, respectively). There was no significant difference in task time between GUIs. Task completion scores of GUI 1 and GUI 2 were higher than for GUI 0 (Z, 9.59, P adj < .001 and Z, 11.87, P adj < .001, respectively). Quantitative and qualitative findings were combined to propose a GUI 3 with high usability. Conclusions: The usability and quality of allergy documentation was higher for the newly developed coded GUIs with a semiautomatic delabeling feature without being more time-consuming.

Background: Several benefits and problems of electronic prescribing (ePrescribing) are described in scientific literature, though problems remain in the implementation. In this study, we evaluated the pharmacist's perception of the ePrescription implementation within the community pharmacy software in Belgium, and the frequency and hindrance of encountered problems. Material and methods: A cross-sectional study was conducted among community pharmacists in Belgium to measure satisfaction with the ePrescribing implementation and factors influencing this satisfaction. Results: In total 246 pharmacists (3.3 % response rate) rated the implementation in their software with an average score of 6.46 ± 2.16 (SD) on a scale of 10. In Belgium, French-speaking pharmacists gave a significantly higher satisfaction score compared to Dutch-speaking pharmacists (P = 0.032), whereas Dutch-speaking pharmacists perceived to process significantly more ePrescriptions compared to French-speaking pharmacists (P < 0.001). Satisfaction with the implementation of the ePrescription was significantly associated with the software package (P < 0.001), the knowledge of the ePrescribing workflow (P = 0.036), the frequency of slow responses of the software (P < 0.001) and the perception of unavailability of the system (P = 0.003). Conclusions: The Belgian pharmacist was moderately satisfied with the implementation of the ePrescription. Problems with the availability of Belgian eHealth systems and interoperability issues with national codes used between prescriber and dispenser have to be resolved in the future in order to meet the Belgian community pharmacist's needs.

Background: Worldwide, the burden of allergies—in particular, drug allergies—is growing. In the process of prescribing, dispensing, or administering a drug, a medication error may occur and can have adverse consequences; for example, a drug may be given to a patient with a documented allergy to that particular drug. Computerized physician order entry (CPOE) systems with built-in clinical decision support systems (CDSS) have the potential to prevent such medication errors and adverse events. Objective: The aim of this review is to provide a comprehensive overview regarding all aspects of CDSS for drug allergy, including documenting, coding, rule bases, alerts and alert fatigue, and outcome evaluation. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed as much as possible and searches were conducted in 5 databases using CPOE, CDSS, alerts, and allergic or allergy as keywords. Bias could not be evaluated according to PRISMA guidelines due to the heterogeneity of study types included in the review. Results: Of the 3160 articles considered, 60 met the inclusion criteria. A further 9 articles were added based on expert opinion, resulting in a total of 69 articles. An interrater agreement of 90.9% with a reliability Κ=.787 (95% CI 0.686-0.888) was reached. Large heterogeneity across study objectives, study designs, study populations, and reported results was found. Several key findings were identified. Evidence of the usefulness of clinical decision support for drug allergies has been documented. Nevertheless, there are some important problems associated with their use. Accurate and structured documenting of information on drug allergies in electronic health records (EHRs) is difficult, as it is often not clear to healthcare providers how and where to document drug allergies. Besides the underreporting of drug allergies, outdated or inaccurate drug allergy information in EHRs poses an important problem. Research on the use of coding terminologies for documenting drug allergies is sparse. There is no generally accepted standard terminology for structured documentation of allergy information. The final key finding is the consistently reported low specificity of drug allergy alerts. Current systems have high alert override rates of up to 90%, leading to alert fatigue. Important challenges remain for increasing the specificity of drug allergy alerts. We found only one study specifically reporting outcomes related to CDSS for drug allergies. It showed that adverse drug events resulting from overridden drug allergy alerts do not occur frequently. Conclusions: Accurate and comprehensive recording of drug allergies is required for good use of CDSS for drug allergy screening. We found considerable variation in the way drug allergy are recorded in EHRs. It remains difficult to reduce drug allergy alert overload while maintaining patient safety as the highest priority. Future research should focus on improving alert specificity, thereby reducing override rates and alert fatigue. Also, the effect on patient outcomes and cost-effectiveness should be evaluated.