Disponibilité de l’information médicale requise pour la déclaration d’une réaction indésirable médicamenteuse à Santé Canada : une étude exploratoire
Bibliographic record
Abstract
Contexte : Depuis 2019, les établissements de santé doivent déclarer les réactions indésirables graves aux médicaments (RIM graves) à Santé Canada. Objectifs : Décrire la disponibilité de l’information requise pour la déclaration d’une RIM à Santé Canada dans les dossiers médicaux par deux méthodes (soit, systématique et approfondie) et comparer le temps nécessaire pour retrouver l’information. Méthode : Étude descriptive rétrospective et prospective des RIM graves survenues en centre mère-enfant et déclarées entre le 1er avril 2021 et 31 mars 2023. Les variables nécessaires pour remplir le formulaire de déclaration de Santé Canada ont été collectées par deux méthodes distinctes. Résultats : Parmi les 270 RIM graves déclarées en rétrospectif, 140 ont été échantillonnées. La disponibilité moyenne des variables était de 82,3 % (écart-type [SD] 11,3 %), pour une durée de collecte de 50 (SD 25) minutes. Du côté prospectif, 15 RIM graves ont été étudiées, la disponibilité des variables était, pour chacune des méthodes respectivement, de 82,8 (SD 6,9 %) et 91,9 (SD 7,8 %), pour des temps de collecte respectifs de 44 (SD 17) et 130 (SD 33) minutes. Conclusions : La difficulté que représente l’identification de toutes les informations requises dans les dossiers médicaux pour la déclaration d’une RIM à Santé Canada nécessite une recherche approfondie. Toutefois, la recherche approfondie demande trois fois plus de temps qu’une recherche qui se limiterait aux endroits où l’information devrait se retrouver. Il est possible d’envisager de dispenser une formation supplémentaire aux cliniciens pour améliorer la tenue de dossiers et, éventuellement, le recours à un dossier clinique informatisé qui comprend un formulaire dédié à la documentation des RIM. ABSTRACT Background: Since 2019, health care facilities have been required to report serious adverse drug reactions (ADRs) to Health Canada. Objectives: To describe the availability of information required for reporting an ADR to Health Canada from medical records using 2 methods (systematic and in-depth reporting) and to compare the time required to find the information.Methods: This retrospective and prospective descriptive study involved serious ADRs occurring in a mother–child centre and reported between April 1, 2021, and March 31, 2023. The variables needed to complete the Health Canada reporting form were collected using 2 distinct methods. Results: Among the 270 serious ADRs reported retrospectively, 140 were sampled. The average availability of variables was 82.3% (standard deviation [SD] 11.3%), with average data collection time of 50 (SD 25) minutes. For the prospective part of the study, 15 serious ADRs were studied. The availability of variables was 82.8% (SD 6.9%) and 91.9% (SD 7.8%), for systematic and in-depth reporting, respectively, with data collection times of 44 (SD 17) and 130 (SD 33) minutes, respectively. Conclusions: The challenge of finding, in patients’ medical records, all of the information needed for reporting an ADR to Health Canada required an in-depth approach. However, the in-depth method took 3 times as long as a search limited to places in the record where specific information should be found. To improve record keeping, additional training for clinicians could be considered and, potentially, development of a computerized clinical record that includes a dedicated form for documenting ADRs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".