A5040743651- Healthcare Technology and Patient Monitoring
- Pharmaceutical studies and practices
- Pharmacovigilance and Adverse Drug Reactions
- Artificial Intelligence in Healthcare and Education
- Pharmaceutical Practices and Patient Outcomes
- Electronic Health Records Systems
- ECG Monitoring and Analysis
- Biomedical and Engineering Education
- Regulation of Appetite and Obesity
- Biochemical Analysis and Sensing Techniques
- Cardiac electrophysiology and arrhythmias
- Atrial Fibrillation Management and Outcomes
- Asthma and respiratory diseases
- Biomedical Text Mining and Ontologies
- Blood Pressure and Hypertension Studies
- Medical Research and Practices
- Biomedical Ethics and Regulation
- Healthcare cost, quality, practices
- Biochemical effects in animals
- Ethics in Clinical Research
- Pharmaceutical industry and healthcare
- Analytical Methods in Pharmaceuticals
- Mobile Health and mHealth Applications
- Imbalanced Data Classification Techniques
- Health Literacy and Information Accessibility
Vrije Universiteit Brussel
2019-2024
Pharmac
2018
Evaluation of the effect six optimization strategies in a clinical decision support system (CDSS) for drug-drug interaction (DDI) screening on alert burden and acceptance description pharmacist intervention acceptance.Optimizations new CDSS were customization knowledge base (with addition 67 extra DDIs changes severity classification), design, required override reasons most serious alerts, creation DDI-specific intervals, patient-specific alerting, real-time follow-up all alerts by...
<h3>Background and Importance</h3> Poor documentation of drug hypersensitivities in patient records can lead to allergic reactions. Developing tools for accurate hypersensitivity prevent prescription errors. However, there is no consensus on how should be routinely documented electronically. We developed a new structured coded tool with semi-automatic de-labelling feature collaboration end-users<sup>1</sup> implemented it our university hospital May 2022. <h3>Aim Objectives</h3> To evaluate...
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 underalerting because stratification only based on a fixed QT-DDI severity level. We aimed improve alerting by developing validating risk prediction model considering patient- drug-related factors.We fitted 31 predictor candidates stepwise linear regression 1000 bootstrap samples selected the...
Artificial intelligence or machine learning (AI/ML) based systems can be used to help personalize prescribing decisions for individual patients. These AI/ML clinical decision support may provide either specific more open-ended recommendations the most appropriate medications prescribe. must fundamentally relate label of medicines involved. The a medicine is an approved guide that indicates how prescribe drug in safe and effective manner. evolve as new information on safety effectiveness...
Ensemble modeling is an increasingly popular data science technique that combines the knowledge of multiple base learners to enhance predictive performance. In this paper, idea was increase performance by holding out three algorithms when testing classifiers: (a) best overall performing algorithm (based on harmonic mean sensitivity and specificity (HMSS) algorithm); (b) most sensitive model; (c) specific model. This approach boils down majority voting between predictions these learners....
<sec> <title>BACKGROUND</title> Clinical decision support systems (CDSS) for drug-drug interaction (DDI) screening are often overly inclusive following a “better safe than sorry” approach leading to an excessive number of alerts, alert fatigue and high override rates which jeopardizes the effectiveness CDSS. Evidence on effect CDSS patient-specific outcomes is scarce. </sec> <title>OBJECTIVE</title> We investigated whether context-specific alerts potassium-increasing DDIs reduced burden had...
The current drug allergy documentation module in the electronic health record of our institution is a free-text format. Two versions structured and coded were developed. Twenty-five physicians tested three interfaces via 3x5 test scenarios. usability was measured for each interface with system scale questionnaire. Both new scored significantly better than version. User feedback will be used to further optimize module.
<h3>Background and importance</h3> Drug–drug interactions leading to QT prolongation potentially fatal torsades de pointes arrhythmias (QT-DDIs) are very common. Clinical decision support (CDS) triggers alerts for such QT-DDIs warn physicians while prescribing. An additional safeguard mechanism is real-time follow-up of all by clinical pharmacists who intervene telephone when necessary. <h3>Aim objectives</h3> The first objective was the evaluation alert acceptance interventions over a...