scholarly journals Evaluation of Clinical Relevance of Drug–Drug Interaction Alerts Prior to Implementation

2018 ◽  
Vol 09 (04) ◽  
pp. 849-855 ◽  
Author(s):  
S. Meslin ◽  
W. Zheng ◽  
R. Day ◽  
E. Tay ◽  
M. Baysari
Keyword(s):  
Drug Interaction ◽  
Clinical Relevance ◽  
Expert Panel ◽  
Multiple Drug ◽  
Poor Agreement ◽  
Computerized Provider Order Entry ◽  
High Agreement ◽  
Alert Fatigue ◽  
Drug Drug Interaction ◽  
Panel Review

Introduction Drug–drug interaction (DDI) alerts are often implemented in the hospital computerized provider order entry (CPOE) systems with limited evaluation. This increases the risk of prescribers experiencing too many irrelevant alerts, resulting in alert fatigue. In this study, we aimed to evaluate clinical relevance of alerts prior to implementation in CPOE using two common approaches: compendia and expert panel review. Methods After generating a list of hypothetical DDI alerts, that is, alerts that would have been triggered if DDI alerts were operational in the CPOE, we calculated the agreement between multiple drug interaction compendia with regards to the severity of these alerts. A subset of DDI alerts (n = 13), with associated patient information, were presented to an expert panel to reach a consensus on whether each alert should be included in the CPOE. Results There was poor agreement between compendia in their classifications of DDI severity (Krippendorff's α: 0.03; 95% confidence interval: –0.07 to 0.14). Only 10% of DDI alerts were classed as severe by all compendia. On the other hand, the panel reached consensus on 12 of the 13 alerts that were presented to them regarding whether they should be included in the CPOE. Conclusion Using an expert panel and allowing them to discuss their views openly likely resulted in high agreement on what alerts should be included in a CPOE system. Presenting alerts in the context of patient cases allowed panelists to identify the conditions under which alerts were clinically relevant. The poor agreement between compendia suggests that this methodology may not be ideal for the evaluation of DDI alerts. Performing preimplementation review of DDI alerts before they are enabled provides an opportunity to minimize the risk of alert fatigue before prescribers are exposed to false-positive alerts.

scholarly journals Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Narjes Rohani ◽  
Changiz Eslahchi
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Side Effect ◽  
Network Architecture ◽  
Selection Process ◽  
Superior Performance ◽  
Multiple Drug ◽  
Interaction Prediction ◽  
Benchmark Datasets ◽  
Drug Drug Interaction

Abstract Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD.

The Effect of Patient-Specific Drug-Drug Interaction Alerting on the Frequency of Alerts: A Pilot Study

2019 ◽  
Vol 53 (11) ◽  
pp. 1087-1092 ◽  
Author(s):  
John Horn ◽  
Stephen Ueng
Keyword(s):  
Drug Interaction ◽  
Medical Center ◽  
Tertiary Care ◽  
Clinical Decision ◽  
Patient Specific ◽  
Specific Drug ◽  
Alert Fatigue ◽  
Modifying Factors ◽  
Tertiary Care Medical Center ◽  
Drug Drug Interaction

Background: False-positive drug-drug interaction alerts are frequent and result in alert fatigue that can result in prescribers bypassing important alerts. Development of a method to present patient-appropriate alerts is needed to help restore alert relevance. Objective: The purpose of this study was to assess the potential for patient-specific drug-drug interaction (DDI) alerts to reduce alert burden. Methods: This project was conducted at a tertiary care medical center. Seven of the most frequently encountered DDI alerts were chosen for developing patient-specific, algorithm-based DDI alerts. For each of the DDI pairs, 2 algorithms featuring different values for modifying factors were made. DDI alerts from the 7 drug pairs were collected over 30 days. Outcome measures included the number of DDI alerts generated before and after patient-specific algorithm application to the same patients over the same time period. Results: A total of 14 algorithms were generated, and each was evaluated by comparing the number of alerts generated by our existing, customized clinical decision support (CDS) software and the patient-specific algorithms. The CDS DDI alerting software generated an average of 185.3 alerts per drug pair over the 30-day study period. Patient-specific algorithms reduced the number of alerts resulting from the algorithms by 11.3% to 93.5%. Conclusion and Relevance: Patient-specific DDI alerting is an innovative and effective approach to reduce the number of DDI alerts, may potentially increase the appropriateness of alerts, and may decrease the potential for alert fatigue.

scholarly journals High-priority drug-drug interaction clinical decision support overrides in a newly implemented commercial computerized provider order-entry system: Override appropriateness and adverse drug events

2020 ◽  
Vol 27 (6) ◽  
pp. 893-900 ◽  
Author(s):  
Heba Edrees ◽  
Mary G Amato ◽  
Adrian Wong ◽  
Diane L Seger ◽  
David W Bates
Keyword(s):  
Drug Interaction ◽  
Adverse Drug Events ◽  
Clinical Decision ◽  
Medication Administration ◽  
Order Entry ◽  
Chi Square ◽  
Computerized Provider Order Entry ◽  
Chi Square Test ◽  
Drug Drug Interaction ◽  
Outpatient Settings

Abstract Objective The study sought to determine frequency and appropriateness of overrides of high-priority drug-drug interaction (DDI) alerts and whether adverse drug events (ADEs) were associated with overrides in a newly implemented electronic health record. Materials and Methods We conducted a retrospective study of overridden high-priority DDI alerts occurring from April 1, 2016, to March 31, 2017, from inpatient and outpatient settings at an academic health center. We studied highest-severity DDIs that were previously designated as “hard stops” and additional high-priority DDIs identified from clinical experience and literature review. All highest-severity alert overrides (n = 193) plus a stratified random sample of additional overrides (n = 371) were evaluated for override appropriateness, using predetermined criteria. Charts were reviewed to identify ADEs for overrides that resulted in medication administration. A chi-square test was used to compare ADE rate by override appropriateness. Results Of 16 011 alerts presented to providers, 15 318 (95.7%) were overridden, including 193 (87.3%) of the highest-severity DDIs and 15 125 (95.8%) of additional DDIs. Override appropriateness was 45.4% overall, 0.5% for highest-severity DDIs and 68.7% for additional DDIs. For alerts that resulted in medication administration (n = 423, 75.0%), 29 ADEs were identified (6.9%, 5.1 per 100 overrides). The rate of ADEs was higher with inappropriate vs appropriate overrides (9.4% vs 4.3%; P = .038). Conclusions The override rate was nearly 90% for even the highest-severity DDI alerts, indicating that stronger suggestions should be made for these alerts, while other alerts should be evaluated for potential suppression.

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

2021 ◽  
Author(s):  
mehrdad Karajizadeh ◽  
Farid Zand ◽  
Roxana Sharifian ◽  
Afsaneh Vazin ◽  
Najmeh Bayati
Keyword(s):  
Drug Interaction ◽  
Cross Sectional Study ◽  
Teaching Hospitals ◽  
Design Development ◽  
Alert System ◽  
Cross Sectional ◽  
Computerized Provider Order Entry ◽  
System Usability Scale ◽  
The Mean ◽  
Drug Drug Interaction

Abstract 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.

scholarly journals Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks

2021 ◽  
Author(s):  
Shichao Liu ◽  
Yang Zhang ◽  
Yuxin Cui ◽  
Yang Qiu ◽  
Yifan Deng ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Drug Interactions ◽  
Data Sources ◽  
Multiple Drug ◽  
Interaction Prediction ◽  
Multiple Data ◽  
Drug Drug Interaction ◽  
Multiple Drugs ◽  
Novel Drug

AbstractDrug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are c o-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. In this paper, we propose a Deep Attention Neural Network based Drug-Drug Interaction prediction framework, abbreviated as DANN-DDI, to predict unobserved drug-drug interactions. First, we construct multiple drug feature networks and learn drug representations from these networks using the graph embedding method; then, we concatenate the learned drug embeddings and design an attention neural network to learn representations of drug-drug pairs; finally, we adopt a deep neural network to accurately predict drug-drug interactions. The experimental results demonstrate that our model DANN-DDI has improved prediction performance compared with state-of-the-art methods. Moreover, the proposed model can predict novel drug-drug interactions and drug-drug interaction-associated events.

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