Applying improvement science to establish a resident sustained quality improvement (QI) educational model
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Notice bibliographique
Résumé
BACKGROUND: Prior to 2017, internal medicine (IM) residents at the University of Alberta did not have a standardised quality improvement (QI) educational curriculum. Our goal was to use QI principles to develop a resident sustained curriculum using the Evidence-based Practice for Improving Quality (EPIQ) training course. METHODS: Three one-year Plan-Do-Study-Act (PDSA) cycles were conducted. The EPIQ course was delivered to postgraduate year (PGY) 1-3 residents (n=110, PDSA 1) in 2017, PGY-1 residents (n=27, PDSA 2) in 2018 and PGY-1 residents (n=28, PDSA 3) in 2019. Trained residents were recruited as facilitators for PDSA 2 and 3. Residents worked through potential QI projects that were later presented for evaluation. Precourse and postcourse surveys and tests were conducted to assess knowledge acquisition and curriculum satisfaction. Process, outcome and balancing measures were also evaluated. RESULTS: In PDSA 1, 98% felt they had acquired understanding of QI principles (56% increase), 94% of PGY-2 and PGY-3 residents preferred this QI curriculum compared with previous training, and 65% of residents expressed interest in pursuing a QI project (15% increase). In PDSA 2, tests scores of QI principles improved from 77.6% to 80%, and 40% of residents expressed interest in becoming a course facilitator. In PDSA 3, self-rated confidence with QI methodology improved from 53% to 75%. A total of 165 residents completed EPIQ training and 11 residents became course facilitators. CONCLUSIONS: Having a structured QI curriculum and working through practical QI projects provided valuable QI training for residents. Feedback was positive, and with each PDSA cycle there was increased resident interest in QI. Developing this curriculum using validated QI tools highlighted areas of change opportunity thereby enhancing acceptance. As more cycles of EPIQ are delivered and more residents become facilitators, it is our aim to have this curriculum sustained by future residents.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle