Ratios d’incidents et d’accidents totaux et médicamenteux par 1000 jours-présence en établissement de santé au Québec : une étude exploratoire
Bibliographic record
Abstract
Contexte : Au Québec, la déclaration de tous les incidents et accidents (IA) en établissement de santé est obligatoire depuis 2002. Depuis 2011, un rapport synthèse de ces IA est publié chaque année. Il est toutefois difficile de comparer les établissements de santé entre eux, sachant qu’aucun dénominateur n’est proposé et qu’aucun ratio n’est calculé. Objectif : L’objectif principal est de calculer les ratios d’IAT (incidents et accidents totaux) et d’IAM (incidents et accidents médicamenteux) par 1000 jours-présence (JP) par mission pour tous les établissements de santé du Québec. Méthodologie : Cette étude descriptive et rétrospective incluait les données extraites sur la période entre le 1er avril 2016 et le 31 mars 2021. Les données ont été extraites du Registre national des incidents et accidents survenus lors de la prestation des soins et services de santé au Québec et de rapports financiers. Les ratios d’IAT/1000 JP et d’IAM/1000 JP moyen ± écart-type et médian [minimum; maximum] ont été calculés. Résultats : Au total, 85 établissements/installations de santé comportaient des données exploitables, soit 33 de courte durée, 45 de longue durée et 7 de réadaptation. Le ratio moyen d’IAT/1000 JP variait de 33 ± 19 à 38 ± 22 en courte durée, de 14 ± 5 à 16 ± 7 en longue durée et de 99 ± 39 à 147 ± 55 en réadaptation. Le ratio moyen d’IAM/1000 JP variait de 11 ± 7 à 12 ± 7 en courte durée, de 3 ± 2 à 4 ± 3 en longue durée et de 24 ± 10 à 40 ± 21 en réadaptation. Conclusions : Cette étude exploratoire démontre la faisabilité de calculer des ratios d’IA à partir du Registre national des incidents et accidents survenus lors de la prestation des soins et services de santé au Québec. Ils permettent de commenter l’évolution et la culture de déclaration des IA au sein du réseau de la santé. Il serait souhaitable que ces ratios soient ajoutés aux prochains rapports annuels québécois. Mots-clés : établissement de santé, incidents, accidents, erreurs médicamenteuses ABSTRACT Background: Since 2022, it has been mandatory in Québec to report all incidents and accidents (I&As) occurring in health-care facilities.Since 2011, a summary report of these I&As has been published each year. However, it is difficult to compare health facilities given that no denominator is specified and ratios are not calculated. Objective: The primary objective was to calculate the ratios of total I&As and medication-related I&As per 1000 inpatient-days per type of facility for all health-care facilities in Québec. Methods: This retrospective descriptive study was based on data from the period of April 1, 2016, to March 31, 2021. Data were extracted from the National Register of Incidents and Accidents Occurring during the Provision of Health Care and Social Services in Québec (Registre national des incidents et accidents survenus lors de la prestation des soins et services de santé au Québec) and financial reports. The ratios of total I&As/1000 inpatient-days and medication-related I&As/1000 inpatient-days, expressed as the mean ± standard deviation and median [minimum; maximum], were calculated. Results: A total of 85 health-care facilities had usable data, specifically 33 acute-care facilities, 45 long-term care facilities, and 7 rehabilitation facilities. The mean ratio for total I&As/1000 inpatient-days varied from 33 ± 19 to 38 ± 22 in acute-care facilities, from 14 ± 5 to 16 ± 7 in long-term care facilities, and from 99 ± 39 to 147 ± 55 in rehabilitation facilities. The mean ratio for medication-related I&As/1000 inpatient- days varied from 11 ± 7 to 12 ± 7 in acute care facilities, from 3 ± 2 to 4 ± 3 in long-term care facilities, and from 24 ± 10 to 40 ± 21 in rehabilitation facilities. Conclusions: This exploratory study demonstrated the feasibility of calculating I&A ratios from the National Register of Incidents and Accidents Occurring during the Provision of Health Care and Social Services in Québec. These ratios facilitate discussion of the reporting culture of I&As within the health-care system. It is hoped that these ratios will be added to future annual reports from the QuébecI&A register. Keywords: health-care facility, incidents, accidents, medication errors"
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".