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488 results on '"Lebel, Denis"'

1. Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model

Author

Hogue, Sophie-Camille, Chen, Flora, Brassard, Geneviève, Lebel, Denis, Bussières, Jean-François, Durand, Audrey, and Thibault, Maxime

Subjects
Computer Science - Machine Learning
Abstract

The objective of this work was to assess the clinical performance of an unsupervised machine learning model aimed at identifying unusual medication orders and pharmacological profiles. We conducted a prospective study between April 2020 and August 2020 where 25 clinical pharmacists dichotomously (typical or atypical) rated 12,471 medication orders and 1,356 pharmacological profiles. Based on AUPR, performance was poor for orders, but satisfactory for profiles. Pharmacists considered the model a useful screening tool., Comment: Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract

Published
2020

6. Use and impact of technology-assisted workflow (TAWF) systems for drug compounding in pharmacy practice: a scoping literature review

Author

Farcy Elisabeth, Bui Duc Tâm, Lebel Denis, and Bussières Jean-François

Subjects
barcode readers, cameras, compounding, gravimetry, impact, technology-assisted workflow systems (tawf), Therapeutics. Pharmacology, RM1-950, Pharmaceutical industry, HD9665-9675
Abstract

The aim of this study was to review studies describing the use and the impact of technology-assisted workflow (TAWF) systems for drug compounding in hospital pharmacy.

Published
2021
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10. Les centres specialises des colleges et les services comparables: Un tableau de la situation (Specialized Centers at Colleges and Comparable Services: A Profile of the Situation).

Author

Conseil des Colleges, Quebec (Quebec). and Lebel, Denis

Abstract

In 1978, the two-year colleges of general and professional education (CEGEP) of Quebec were charged with taking on a more vigorous role in the economic development of the province. To formalize this new orientation, a number of vocational education and applied research centers were created, each specializing in a particular key technology. Centers have been created focusing on mineral technology, metallurgy, robotics, composite materials, forestry, textile, and fashion, among other areas. While the centers are integrated within the CEGEPs' structure, they are also afforded a margin of autonomy in order to realize their own individual missions. The first two sections of this report describe the origins of the specialized centers and present an overview of their activities, which include applied research, technical assistance, instruction, information dissemination, and a variety of other projects. The next section focuses on the contributions of the centers to the economic development of the province, covering activities in support of small and large businesses and the regional, national, and international impact of the centers. Next, the contributions of the centers to instructional development are detailed, covering professional development, course and program development, laboratories, instructional organization, and services for employed students. The report then examines key factors in the success of the centers and environmental changes which have taken place since their establishment. The final sections identify challenges facing the centers, e.g., becoming institutionalized and integrated within the CEGEP's, exporting technological knowledge, finding stable funding, and exploring various modes of development. (AC)

Published
1993

15. Prospective Descriptive Study of RFID Tag Detection Rates based on Various Exploratory Scenarios Aimed at Identifying Optimal Conditions of Use

Author

Petit Camille, Bergeron Maxime, Atkinson Suzanne, Lebel Denis, and Bussières Jean-François

Subjects
drug resuscitation tray, radio frequency identification, rfid, traceability, Therapeutics. Pharmacology, RM1-950, Pharmaceutical industry, HD9665-9675
Abstract

The main objective is to evaluate RFID tags detection rates using various exploratory scenarios in order to identify optimal conditions of use. The secondary objective is to evaluate RFID tags detection rates based on a real-life scenario involving a cardiorespiratory resuscitation drug tray used within our institution in order to identify optimal conditions of use.

Published
2016
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16. Proof of Concept and Pilot Study on the Development and Implementation of an Electronic Medication Administration Record

Author

Lebel Denis, Mercier Geneviève, Dulermez Thomas, Rousseau Aurélie, and Bussières Jean-François

Subjects
copy of a medication record, doses recording, drug delivery, electronic medication administration., Therapeutics. Pharmacology, RM1-950, Pharmaceutical industry, HD9665-9675
Abstract

Objective: The main objective is to describe the phases of electronic medication administration record development and implementation. The secondary objective is to compare dose information using medication administration record and electronic medication administration record.

Published
2016
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21. Pharmacogenetic testing in pediatric neurology: a pragmatic study evaluating clinician and patient perceptions

Author

Pépin, Marie-Anne, primary, Otis, Anne-Sophie, additional, Tremblay, Zoë, additional, Boulé, Marianne, additional, Lebel, Denis, additional, Major, Philippe, additional, Lortie, Anne, additional, Pinchefsky, Elana, additional, Rossignol, Elsa, additional, Carleton, Bruce, additional, Bussières, Jean-François, additional, and Métras, Marie-Élaine, additional

Published
2022
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36. Soins plus — mise en place d’une intervention à trois volets visant à accroître la visibilité et la disponibilité du pharmacien dans un hôpital pédiatrique : une étude randomisée contrôlée

Author

Mosora, Flaviu, primary, Guèvremont, Myriam, additional, Vézina, Gabriel, additional, Côté, Karine, additional, Boulé, Marianne, additional, Lebel, Denis, additional, Bussières, Jean-François, additional, and Métras, Marie-Élaine, additional

Published
2021
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39. Smart pump use in pediatric patients

Author

Tourel, Julien, Delage, Emmanuelle, Lebel, Denis, Litalien, Catherine, Duval, Stéphanie, Lacroix, Annie, David, Marie-Johanne, and Bussières, Jean-François

Published
2012

49. An application of machine learning to assist medication order review by pharmacists in a health care center

Author

Thibault, Maxime and Lebel, Denis

Subjects
education.field_of_study, Artificial neural network, Computer science, business.industry, Population, Pharmacist, Fleiss' kappa, Machine learning, computer.software_genre, Obstetrics and gynaecology, Test set, Intensive care, Health care, Artificial intelligence, business, education, computer
Abstract

The objective of this study was to determine if it is feasible to use machine learning to evaluate how a medication order is contextually appropriate for a patient, in order to assist order review by pharmacists. A neural network was constructed using as input the sequence of word2vec embeddings of the 30 previous orders, as well as the currently active medications, pharmacological classes and ordering department, to predict the next order. The model was trained with data from 2013 to 2017, optimized using 5-fold cross-validation, and tested on orders from 2018. A survey was developed to obtain pharmacist ratings on a sample of 20 orders, which were compared with predictions. The training set included 1 022 272 orders. The test set included 95 310 orders. Baseline training set top 1, top 10 and top 30 accuracy using a dummy classifier were respectively 4.5%, 23.6% and 44.1%. Final test set accuracies were, respectively, 44.4%, 69.9% and 80.4%. Populations in which the model performed the best were obstetrics and gynecology patients and newborn babies (either in or out of neonatal intensive care). Pharmacists agreed poorly on their ratings of sampled orders with a Fleiss kappa of 0.283. The breakdown of metrics by population showed better performance in patients following less variable order patterns, indicating potential usefulness in triaging routine orders to less extensive pharmacist review. We conclude that machine learning has potential for helping pharmacists review medication orders. Future studies should aim at evaluating the clinical benefits of using such a model in practice.

Published
2019
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