Designing and evaluating contextualized drug–drug interaction algorithms

Chou, Eric; Boyce, Richard D; Balkan, Baran; Subbian, Vignesh; Romero, Andrew; Hansten, Philip D.; Horn, John R.; Gephart, Sheila M.; Malone, Daniel C.

doi:10.1093/jamiaopen/ooab023

Cited by 20 publications

(20 citation statements)

References 31 publications

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“…This study confirms the importance of context in limiting a large proportion of false positives in DDI detection (5). This gives useful guidance to conduct studies that attempt to determine the frequency of DDI on real-world data or for critical appraisal of such studies.…”

Section: Discussionsupporting

confidence: 73%

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When Context Matters for Credible Measurement of Drug-Drug Interactions Based on Real-World Data

Lelong

Dahamna

Berthelot

et al. 2022

Studies in Health Technology and Informatics

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The frequency of potential drug-drug interactions (DDI) in published studies on real world data considerably varies due to the methodological framework. Contextualization of DDI has a proven effect in limiting false positives. In this paper, we experimented with the application of various DDIs contexts elements to see their impact on the frequency of potential DDIs measured on the same set of prescription data collected in EDSaN, the clinical data warehouse of Rouen University Hospital. Depending on the context applied, the frequency of daily prescriptions with potential DDI ranged from 0.89% to 3.90%. Substance-level analysis accounted for 48% of false positives because it did not account for some drug-related attributes. Consideration of the patient’s context could eliminate up to an additional 29% of false positives.

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Section: Discussionsupporting

confidence: 73%

“…For a good detection of potential DDIs, a more contextualized definition of DDIs is also important and limits false positives (5). The context can be divided into drugrelated factors (e.g.…”

Section: Introductionmentioning

confidence: 99%

When Context Matters for Credible Measurement of Drug-Drug Interactions Based on Real-World Data

Lelong

Dahamna

Berthelot

et al. 2022

Studies in Health Technology and Informatics

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“…Similarly to the results of Mille et al [ 30 ], the lack of DDI-specific screening intervals and lack of incorporation of context factors were identified, in both our quantitative evaluation and end-user survey, as the main barriers of our current DDI CDSS system. Recent studies already showed the potential of including patient-specific and context-specific characteristics into DDI CDSS algorithms on reducing the DDI alert burden [ 17 , 18 ]. Chou et al showed a reduction in DDI alerts by more than 50% [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies already showed the potential of including patient-specific and context-specific characteristics into DDI CDSS algorithms on reducing the DDI alert burden [ 17 , 18 ]. Chou et al showed a reduction in DDI alerts by more than 50% [ 17 ]. Alerts were reduced by up to 93.5% in the study of Horn and Ueng [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

Overall performance of a drug–drug interaction clinical decision support system: quantitative evaluation and end-user survey

Sijpe

Quintens

Walgraeve

et al. 2022

BMC Med Inform Decis Mak

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Background Clinical decision support systems are implemented in many hospitals to prevent medication errors and associated harm. They are however associated with a high burden of false positive alerts and alert fatigue. The aim of this study was to evaluate a drug–drug interaction (DDI) clinical decision support system in terms of its performance, uptake and user satisfaction and to identify barriers and opportunities for improvement. Methods A quantitative evaluation and end-user survey were performed in a large teaching hospital. First, very severe DDI alerts generated between 2019 and 2021 were evaluated retrospectively. Data collection comprised alert burden, override rates, the number of alert overrides reviewed by pharmacists and the resulting pharmacist recommendations as well as their acceptance rate. Second, an e-survey was carried out among prescribers to assess satisfaction, usefulness and relevance of DDI alerts as well as reasons for overriding. Results A total of 38,409 very severe DDI alerts were generated, of which 88.2% were overridden by the prescriber. In 3.2% of reviewed overrides, a recommendation by the pharmacist was provided, of which 79.2% was accepted. False positive alerts were caused by a too broad screening interval and lack of incorporation of patient-specific characteristics, such as QTc values. Co-prescribing of a non-vitamin K oral anticoagulant and a low molecular weight heparin accounted for 49.8% of alerts, of which 92.2% were overridden. In 88 (1.1%) of these overridden alerts, concurrent therapy was still present. Despite the high override rate, the e-survey revealed that the DDI clinical decision support system was found useful by prescribers. Conclusions Identified barriers were the lack of DDI-specific screening intervals and inclusion of patient-specific characteristics, both leading to a high number of false positive alerts and risk for alert fatigue. Despite these barriers, the added value of the DDI clinical decision support system was recognized by prescribers. Hence, integration of DDI-specific screening intervals and patient-specific characteristics is warranted to improve the performance of the DDI software.

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“…DDIAs increase the workload of healthcare staff. Nevertheless, previous studies showed several strategies to reduce irrelevant DDIAs, including applying the pharmacist recommendations to review DDIs, considering context factors of diagnosis, patient age and laboratory value (19,20). DDIA systems reached high priority for implementation in ICU settings in Iran based on a previous Karajizadeh and et al's study (21).…”

Section: -Introductionmentioning

confidence: 99%

A Study on Design, Development, Implementation, and Evaluation of Severe Drug-Drug Interaction Alert System in ICU

Karajizadeh

Zand

Sharifian

et al. 2021

Preprint

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Background and objective: The overridden rate of Drug-Drug Interaction Alerts (DDIAs) in the Intensive Care Unit (ICU) is very high. Therefore, this study aimed to design, develop, implement, and evaluate a severe Drug-Drug Alert System (DDIAS) in ICU and measure the override rate of DDIAs. Methods This is a cross-sectional study for the design, development, implementation, and evaluation of severe DDIAs 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. Results The knowledge base of DDIAS contains 9,809 severe DDIs. A total of 2672 medications were prescribed in the population study. The volume and acceptance rate for severe DDIAs were 81 and 97.5%, respectively. However, the override rate was 2.5%. The mean System Usability Scale (SUS) score of the DDIAS was 75. Conclusion This study demonstrated that the implementation of high-risk DDIAs at point of prescribing in ICU improved adherence to alerts. In addition, the usability of DDIAS was reasonable. Further studies are need to investigate the establishment of severe DDIAS and measure the physician's response to DDIAS on a larger scale.

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Designing and evaluating contextualized drug–drug interaction algorithms

Cited by 20 publications

References 31 publications

When Context Matters for Credible Measurement of Drug-Drug Interactions Based on Real-World Data

When Context Matters for Credible Measurement of Drug-Drug Interactions Based on Real-World Data

Overall performance of a drug–drug interaction clinical decision support system: quantitative evaluation and end-user survey

A Study on Design, Development, Implementation, and Evaluation of Severe Drug-Drug Interaction Alert System in ICU

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