Optimizing Drug-Drug Interaction Alerts Using a Multidimensional Approach

Daniels, Calvin C.; Burlison, Jonathan D.; Baker, Donald K.; Robertson, Jennifer; Sablauer, András; Flynn, Patricia M.; Campbell, Patrick; Hoffman, James M.

doi:10.1542/peds.2017-4111

Cited by 16 publications

(24 citation statements)

References 30 publications

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“…and Daniels et al . reduced the number of alerts by ~ 50% 12,33 . Improvement in PPV was also demonstrated, i.a., by Eppenga et al ., increasing from 9.9% to 14.8% after introducing a contextualized DDI‐CDS 32,35 .…”

Section: Discussionmentioning

confidence: 82%

“…In practice, however, such DDI alerts are frequently overridden as most alerts are considered to be irrelevant for specific patients. [9][10][11][12][13] Too many irrelevant alerts can lead to alert fatigue, 9,13-15 described as a "mental state being the result of too many irrelevant alerts consuming time and mental energy, which can cause important alerts to be ignored along with clinically unimportant ones." 16 Basic DDI-CDS systems have limited options for suppressing irrelevant DDI alerts, other than turning DDI alerts off for specific drug combinations.…”

Section: Articlementioning

confidence: 99%

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Contextualized Drug–Drug Interaction Management Improves Clinical Utility Compared With Basic Drug–Drug Interaction Management in Hospitalized Patients

Wasylewicz

Burgt

Manten

et al. 2022

Clin Pharma and Therapeutics

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Drug-drug interactions (DDIs) frequently trigger adverse drug events or reduced efficacy. Most DDI alerts, however, are overridden because of irrelevance for the specific patient. Basic DDI clinical decision support (CDS) systems offer limited possibilities for decreasing the number of irrelevant DDI alerts without missing relevant ones. Computerized decision tree rules were designed to context-dependently suppress irrelevant DDI alerts. A crossover study was performed to compare the clinical utility of contextualized and basic DDI management in hospitalized patients. First, a basic DDI-CDS system was used in clinical practice while contextualized DDI alerts were collected in the background. Next, this process was reversed. All medication orders (MOs) from hospitalized patients with at least one DDI alert were included. The following outcome measures were used to assess clinical utility: positive predictive value (PPV), negative predictive value (NPV), number of pharmacy interventions (PIs)/1,000 MOs, and the median time spent on DDI management/1,000 MOs. During the basic DDI management phase 1,919 MOs/day were included, triggering 220 DDI alerts/1,000 MOs; showing 57 basic DDI alerts/1,000 MOs to pharmacy staff; PPV was 2.8% with 1.6 PIs/1,000 MOs costing 37.2 minutes/1,000 MOs. No DDIs were missed by the contextualized CDS system (NPV 100%). During the contextualized DDI management phase 1,853 MOs/day were included, triggering 244 basic DDI alerts/1,000 MOs, showing 9.6 contextualized DDIs/1,000 MOs to pharmacy staff; PPV was 41.4% (P < 0.01), with 4.0 PIs/1,000 MOs (P < 0.01) and 13.7 minutes/1,000 MOs. The clinical utility of contextualized DDI management exceeds that of basic DDI management.

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Section: Discussionmentioning

confidence: 82%

Section: Articlementioning

confidence: 99%

Contextualized Drug–Drug Interaction Management Improves Clinical Utility Compared With Basic Drug–Drug Interaction Management in Hospitalized Patients

Wasylewicz

Burgt

Manten

et al. 2022

Clin Pharma and Therapeutics

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“…A number of studies (n = 26) described optimization strategies, implemented as single-component interventions (Table 2). 15–40 Table 2 summarizes the various single-component optimization strategies used in the included studies, highlighting the stage of the ePrescribing process being optimized and the overall reported effectiveness of the intervention on the primary outcome.…”

Section: Resultsmentioning

confidence: 99%

“…At the prescriber interface of ePrescribing systems, CDS, 17,[19][20][21]23,24 order sets, 25 alerts, 18,22 and a data dashboard 26 were all deployed as optimization strategies. Computerized decision support was often applied as an optimization strategy to aid the management of clinical conditions with standardized treatment algorithms amenable to decision support.…”

Section: Prescribingmentioning

confidence: 99%

Optimizing Hospital Electronic Prescribing Systems: A Systematic Scoping Review

et al. 2022

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Objective: Considerable international investment in hospital electronic prescribing (ePrescribing) systems has been made, but despite this, it is proving difficult for most organizations to realize safety, quality, and efficiency gains in prescribing. The objective of this work was to develop policy-relevant insights into the optimization of hospital ePrescribing systems to maximize the benefits and minimize the risks of these expensive digital health infrastructures.Methods: We undertook a systematic scoping review of the literature by searching MEDLINE, Embase, and CINAHL databases. We searched for primary studies reporting on ePrescribing optimization strategies and independently screened and abstracted data until saturation was achieved. Findings were theoretically and thematically synthesized taking a medicine life-cycle perspective, incorporating consultative phases with domain experts.Results: We identified 23,609 potentially eligible studies from which 1367 satisfied our inclusion criteria. Thematic synthesis was conducted on a data set of 76 studies, of which 48 were based in the United States. Key approaches to optimization included the following: stakeholder engagement, system or process redesign, technological innovations, and education and training packages. Single-component interventions (n = 26) described technological optimization strategies focusing on a single, specific step in the prescribing process. Multicomponent interventions (n = 50) used a combination of optimization strategies, typically targeting multiple steps in the medicines management process.Discussion: We identified numerous optimization strategies for enhancing the performance of ePrescribing systems. Key considerations for ePrescribing optimization include meaningful stakeholder engagement to reconceptualize the service delivery model and implementing technological innovations with supporting training packages to simultaneously impact on different facets of the medicines management process.

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“…Using examples from PDDI clinical decision support, an in situ qualitative study of prescribers’ interaction with electronic medication alerts showed that prescribers bypassed the alert and then searched for patient-specific data that they needed when alerts failed to provide contextual information ( Russ et al, 2012 ). Similarly, quality improvement projects have found that making PDDI alerts more appropriate to clinical context can improve alert acceptance ( Daniels et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

A Minimal Information Model for Potential Drug-Drug Interactions

Hochheiser

Jing

García³

et al. 2021

Front. Pharmacol.

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Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.

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Optimizing Drug-Drug Interaction Alerts Using a Multidimensional Approach

Cited by 16 publications

References 30 publications

Contextualized Drug–Drug Interaction Management Improves Clinical Utility Compared With Basic Drug–Drug Interaction Management in Hospitalized Patients

Contextualized Drug–Drug Interaction Management Improves Clinical Utility Compared With Basic Drug–Drug Interaction Management in Hospitalized Patients

Optimizing Hospital Electronic Prescribing Systems: A Systematic Scoping Review

A Minimal Information Model for Potential Drug-Drug Interactions

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