Drug safety alerting in computerized physician order entry: Unraveling and counteracting alert fatigue

Computerized physician order entry systems (CPOEs) usually generate drug safety alerts to remind physicians to potentially unsafe situations. However, physicians may feel overwhelmed by high numbers of alerts that are not patient-tailored and they may consequently suffer from alert fatigue. Alert fatigue is the mental state that is the result of alerts consuming too much time and mental energy, which can cause relevant alerts to be unjustifiably overridden along with clinically irrelevant ones. Reasonâ?Ts model of accident causation helps to understand how a drug safety alerting system meant to reduce medication errors may provoke them. Low specificity, unclear alert information, unnecessary workflow disruptions and unsafe and inefficient alert handling add to the risk of alert fatigue. Drug safety alerts were overridden in 90% of cases and physicians often justified overriding by using incorrect rules or rules not applicable to the situation. A newly developed comprehensive test showed widely varying quality of drug safety alerting in Dutch hospital CPOEs. Counteracting alert fatigue appeared to be difficult. Turning off frequently overridden alerts hospital-wide was not feasible because of within-hospital differences in drug-related knowledge and routine monitoring practices. Levels of seriousness added to the alert text were perceived helpful but resulted in increased override rates. Directing alerts with respect to drug administration times to nurses was not accepted by the ward management. Preferred alert handling was hampered often because of low specificity, unclear alert recommendations and inefficient handling. This research generated unexpected results as well as insights how to improve drug safety alerting in CPOE

The use of a clinical decision support tool to assess the risk of QT drug–drug interactions in community pharmacies

Introduction: The handling of drug–drug interactions regarding QTc-prolongation (QT-DDIs) is not well defined. A clinical decision support (CDS) tool will support risk management of QT-DDIs. Therefore, we studied the effect of a CDS tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. Methods: An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of three months were included. The impact of the use of a CDS tool to support the handling of QT-DDIs was studied. For each QT-DDI, handling of the QT-DDI and patient characteristics were extracted from the pharmacy information system. Primary outcome was the proportion of QT-DDIs with an intervention. Secondary outcomes were the type of interventions and the time associated with handling QT-DDIs. Logistic regression analysis was used to analyse the primary outcome. Results: Two hundred and forty-four QT-DDIs pre-CDS tool and 157 QT-DDIs post-CDS tool were included. Pharmacists intervened in 43.0% and 35.7% of the QT-DDIs pre- and post-CDS tool respectively (odds ratio 0.74; 95% confidence interval 0.49–1.11). Substitution of interacting agents was the most frequent intervention. Pharmacists spent 20.8 ± 3.5 min (mean ± SD) on handling QT-DDIs pre-CDS tool, which was reduced to 14.9 ± 2.4 min (mean ± SD) post-CDS tool. Of these, 4.5 ± 0.7 min (mean ± SD) were spent on the CDS tool. Conclusion: The CDS tool might be a first step to developing a tool to manage QT-DDIs via a structured approach. Improvement of the tool is needed in order to increase its diagnostic value and reduce redundant QT-DDI alerts. Plain Language Summary: The use of a tool to support the handling of QTc-prolonging drug interactions in community pharmacies Introduction: Several drugs have the ability to cause heart rhythm disturbances as a rare side effect. This rhythm disturbance is called QTc-interval prolongation. It may result in cardiac arrest. For health care professionals, such as physicians and pharmacists, it is diffic

Does a Dose Calculator as an Add-On to a Web-Based Paediatric Formulary Reduce Calculation Errors in Paediatric Dosing? A Non-Randomized Controlled Study

OBJECTIVES: The structured digital dosing guidelines of the web-based Dutch Paediatric Formulary provided the opportunity to develop an integrated paediatric dose calculator. In a simulated setting, we tested the ability of this calculator to reduce calculation errors. METHODS: Volunteer healthcare professionals were allocated to one of two groups, manual calculation versus the use of the dose calculator. Professionals in both groups were given access to a web-based questionnaire with 14 patient cases for which doses had to be calculated. The effect of group allocation on the probability of making a calculation error was determined using generalized estimated equations (GEE) logistic regression analysis. The causes of all the erroneous calculations were evaluated. RESULTS: Seventy-seven healthcare professionals completed the web-based questionnaire: thirty-seven were allocated to the manual group and 40 to the calculator group. Use of the dose calculator resulted in an estimated mean probability of a calculation error of 24.4% (95% CI 16.3-34.8) versus 39.0% (95% CI 32.4-46.1) with use of manual calculation. The mean difference of probability of calculation error between groups was 14.6% (95% CI 3.1-26.2; p =

Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice

BACKGROUND: The exact risk of developing QTc-prolongation when using a combination of QTc-prolonging drugs is still unknown, making it difficult to interpret these QT drug-drug interactions (QT-DDIs). A tool to identify high-risk patients is needed to support healthcare providers in handling automatically generated alerts in clinical practice. The main aim of this study was to develop and validate a tool to assess the risk of QT-DDIs in clinical practice. METHODS: A model was developed based on risk factors associated with QTc-prolongation determined in a prospective study on QT-DDIs in a university medical center inthe Netherlands. The main outcome measure was QTc-prolongation defined as a QTc interval > 450 ms for males and > 470 ms for females. Risk points were assigned to risk factors based on their odds ratios. Additional risk factors were added based on a literature review. The ability of the model to predict QTc-prolongation was validated in an independent dataset obtained from a general teaching hospital against QTc-prolongation as measured by an ECG as the gold standard. Sensitivities, specificities, false omission rates, accuracy and Youden's index were calculated. RESULTS: The model included age, gender, cardiac comorbidities, hypertension, diabetes mellitus, renal function, potassium levels, loop diuretics, and QTc-prolonging drugs as risk factors. Application of the model to the independent dataset resulted in an area under the ROC-curve of 0.54 (95% CI 0.51-0.56) when QTc-prolongation was defined as > 450/470 ms, and 0.59 (0.54-0.63) when QTc-prolongation was defined as > 500 ms. A cut-off value of 6 led to a sensitivity of 76.6 and 83.9% and a specificity of 28.5 and 27.5% respectively. CONCLUSIONS: A clinical decision support tool with fair performance characteristics was developed. Optimization of this tool may aid in assessing the risk associated with QT-DDIs. TRIAL REGISTRATION: No trial registration, MEC-2015-368

A usability study to improve a clinical decision support system for the prescription of antibiotic drugs

Objective A clinical decision support system (CDSS) for empirical antibiotic treatment has the potential to increase appropriate antibiotic use. Before using such a system on a broad scale, it needs to be tailored to the users prefer

The effect of ICU-tailored drug-drug interaction alerts on medication prescribing and monitoring: Protocol for a cluster randomized stepped-wedge trial

Background: Drug-drug interactions (DDIs) can cause patient harm. Between 46 and 90% of patients admitted to the Intensive Care Unit (ICU) are exposed to potential DDIs (pDDIs). This rate is twice as high as patients on general wards. Clinical decision support systems (CDSSs) have shown their potential to prevent pDDIs. However, the literature shows that there is considerable room for improvement of CDSSs, in particular by increasing the clinical relevance of the pDDI alerts they generate and thereby reducing alert fatigue. However, consensus on which pDDIs are clinically relevant in the ICU setting is lacking. The primary aim of this study is to evaluate the effect of alerts based on only clinically relevant interactions for the ICU setting on the prevention of pDDIs among Dutch ICUs. Methods: To define the clinically relevant pDDIs, we will follow a rigorous two-step Delphi procedure in which a national expert panel will assess which pDDIs are perceived clinically relevant for the Dutch ICU setting. The intervention is the CDSS that generates alerts based on the clinically relevant pDDIs. The intervention will be evaluated in a stepped-wedge trial. A total of 12 Dutch adult ICUs using the same patient data management system, in which the CDSS will operate, were invited to participate in the trial. Of the 12 ICUs, 9 agreed to participate and will be enrolled in the trial. Our primary outcome measure is the incidence of clinically relevant pDDIs per 1000 medication administrations. Discussion: This study will identify pDDIs relevant for the ICU setting. It will also enhance our understanding of the effectiveness of alerts confined to clinically relevant pDDIs. Both of these contributions can facilitate the successful implementation of CDSSs in the ICU and in other domains as well. Trial registration: Nederlands Trial register Identifier: NL6762. Registered November 26, 2018

The Development of Practice Recommendations for Drug-Disease Interactions by Literature Review and Expert Opinion

Background: Drug-disease interactions negatively affect the benefit/risk ratio of drugs for specific populations. In these conditions drugs should be avoided, adjusted, or accompanied by extra monitoring. The motivation for many drug-disease interactions in the Summary of Product Characteristics (SmPC) is sometimes insufficiently supported by (accessible) evidence. As a consequence the translation of SmPC to clinical practice may lead to non-specific recommendations. For the translation of this information to the real world, it is necessary to evaluate the available knowledge about drug-disease interactions, and to formulate specific recommendations for prescribers and pharmacists. The aim of this paper is to describe a standardized method how to develop practice recommendations for drug-disease interactions by literature review and expert opinion. Methods: The development of recommendations for drug-disease interactions will follow a six-step plan involving a multidisciplinary expert panel (1). The scope of the drug-disease interaction will be specified by defining the disease and by describing relevant effects of this drug-disease interaction. Drugs possibly involved in this drug-disease interaction are selected by checking the official product information, literature, and expert opinion (2). Evidence will be collected from the official product information, guidelines, handbooks, and primary literature (3). Study characteristics and outcomes will be evaluated and presented in standardized reports, including preliminary conclusions on the clinical relevance and practice recommendations (4). The multidisciplinary expert panel will discuss the reports and will either adopt or adjust the conclusions (5). Practice recommendations will be integrated in clinical decision support systems and published (6). The results of the evaluated drug-disease interactions will remain up-to-date by screening new risk information, periodic literature review, and (re)assessments initiated by health care providers. Actionable Recommendations: The practice recommendations will result in advices for specific DDSI. The content and considerations of these DDSIs will be published and implemented in all Clinical Decision Support Systems in the Netherlands. Discussion: The recommendations result in professional guidance in the context of individual patient care. The professional will be supported in the decision making in concerning pharmacotherapy for the treatment of a medical problem, and the clinical risks of the proposed medication in combination with specific diseases

Clinically relevant potential drug-drug interactions in intensive care patients: A large retrospective observational multicenter study

Purpose: Potential drug-drug interactions (pDDIs) may harm patients admitted to the Intensive Care Unit (ICU). Due to the patient's critical condition and continuous monitoring on the ICU, not all pDDIs are clinically relevant. Clinical decision support systems (CDSSs) warning for irrelevant pDDIs could result in alert fatigue and overlooking important signals. Therefore, our aim was to describe the frequency of clinically relevant pDDIs (crpDDIs) to enable tailoring of CDSSs to the ICU setting. Materials & methods: In this multicenter retrospective observational study, we used medication administration data to identify pDDIs in ICU admissions from 13 ICUs. Clinical relevance was based on a Delphi study in which intensivists and hospital pharmacists assessed the clinical relevance of pDDIs for the ICU setting. Results: The mean number of pDDIs per 1000 medication administrations was 70.1, dropping to 31.0 when considering only crpDDIs. Of 103,871 ICU patients, 38% was exposed to a crpDDI. The most frequently occurring crpDDIs involve QT-prolonging agents, digoxin, or NSAIDs. Conclusions: Considering clinical relevance of pDDIs in the ICU setting is important, as only half of the detected pDDIs were crpDDIs. Therefore, tailoring CDSSs to the ICU may reduce alert fatigue and improve medication safety in ICU patients

Concomitant lamotrigine use is associated with decreased efficacy of the ketogenic diet in childhood refractory epilepsy

Purpose Anti-epileptic drugs (AEDs) and the ketogenic diet (KD) are often used concomitantly in children with refractory epilepsy. It has been hypothesised that certain AEDs may interfere with KD. The purpose of this study was to elucidate relationships between efficacy of KD and use of specific AEDs. Methods A retrospective study was performed in 71 children with refractory epilepsy starting the KD between 2008 and 2014 in Erasmus University Hospital Sophia Children's Hospital. Efficacy of the KD (defined as 50% seizure reduction) was evaluated after three months of treatment and related to the AEDs used. Results The KD was successful after three months in 61% of the children (N = 71). Efficacy was significantly reduced if children (n = 16) used lamotrigine (31%) at diet initiation or in the course of the diet, compared to other antiepileptic drugs (69%) (p = 0.006). In comparison to children using other antiepileptic drugs, the percentage of children that had adequate ketosis was significantly reduced in case of lamotrigine use (p = 0.049). Conclusion Lamotrigine treatment during KD is associated with a decreased efficacy of the KD

Drug-drug interactions that should be noninterruptive in order to reduce alert fatigue in electronic health records

Objective Alert fatigue represents a common problem associated with the use of clinical decision support systems in electronic health records (EHR). This problem is particularly profound with drug–drug interaction (DDI) alerts for which studies have reported override rates of approximately 90%. The objective of this study is to report consensus-based recommendations of an expert panel on DDI that can be safely made non-interruptive to the provider's workflow, in EHR, in an attempt to reduce alert fatigue. Methods We utilized an expert panel process to rate the interactions. Panelists had expertise in medicine, pharmacy, pharmacology and clinical informatics, and represented both academic institutions and vendors of medication knowledge bases and EHR. In addition, representatives from the US Food and Drug Administration and the American Society of Health-System Pharmacy contributed to the discussions. Results Recommendations and considerations of the panel resulted in the creation of a list of 33 class-based low-priority DDI that do not warrant being interruptive alerts in EHR. In one institution, these accounted for 36% of the interactions displayed. Discussion Development and customization of the content of medication knowledge bases that drive DDI alerting represents a resource-intensive task. Creation of a standardized list of low-priority DDI may help reduce alert fatigue across EHR. Conclusions Future efforts might include the development of a consortium to maintain this list over time. Such a list could also be used in conjunction with financial incentives tied to its adoption in EHR