187 Improving departmental quality improvement plans through standardization, structured peer-to-peer feedback, and building improvement capacity and culture
Notice bibliographique
Résumé
The lead author has seen and agreed to the license applied to conference abstracts published by BMJ.Introduction Quality Improvement Plans (QIPs) can improve healthcare quality by raising awareness and providing focus around improvement efforts. 1 Meanwhile, physician leadership and participation in an organization’s quality agenda is required to improve patient safety and quality of care, and to attain organizational quality goals.2–8 The physician-led quality committee at our institution set out to improve the previously heterogenous quality and content of medical department QIPs and increase alignment between medical department and hospital quality improvement (QI) priorities. We describe these initiatives and assess their impact on the quality of departmental QIPs.Methods The Physician Quality Committee at our academic tertiary care hospital implemented a series of interventions, including a peer-to-peer feedback mechanism, longitudinal education and coaching, standardized QI project templates, and efforts to facilitate culture change. The QIPs from 13 medical departments were reviewed for the academic years before (2018–2019) and after the interventions (2022–2023) and scored according to a structured rubric, created by consensus from physician quality leads. Data are reported as means and median [interquartile range]. A Wilcoxon signed-rank test was used to evaluate for statistical significance. A Likert-scale survey was used to assess the physician QI leads perception of the impact of the initiatives.Results The mean score on the structured rubric was 4.4/12 for the QIPs from 2018–2019 and 8.0/12 for the QIPs from 2022–2023 (Z=3.06, p=0.0005). The median score [25th, 75th percentile] in 2018–2019 was 4.5 [3.5, 5.13], which increased to 8.5 [7.0, 9.0] in 2022–2023 [ figure 1]. The survey response for physician QI leads was 10/13 (76.9%). The most positive response was the QI lead’s knowledge and understanding of how to structure a QI project (mean score of 4.4/5); the least positive response was related to departmental focus and clarity regarding QI priorities (mean score of 3.9/5) [figure 2].Multifaceted physician-led interventions resulted in improvements in the quality and content of medical department QIPs, improved physician knowledge of QI methodology, enhanced focus and clarity around departmental QI priorities, and improved awareness of hospital-wide improvement efforts.Abstract 187 Figure 1Change in departmental QIP rubric score pre- and post-interventionAbstract 187 Figure 2Mean score on likert scale survey of medical department physician QI leads. Figure Legend: Dots represent mean score; error bars represent range of survey results.References Chan Y-CL, Hsu SH. Target-setting, pay for performance, and quality improvement: a case study of ontario hospitals’ quality-improvement plans. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l’Administration 2019;36(1):128–144.Reinertsen J, Gosfield A, Rupp W, Whittington J. Engaging Physicians in a Shared Quality Agenda. IHI Innovation Series white paper. Cambridge, Massachusetts: Institute for Healthcare Improvement;2007.Hayes C, Yousefi V, Wallington T, Ginzburg A. Case study of physician leaders in quality and patient safety, and the development of a physician leadership network. Healthcare Quarterly 2010;13(Sp):68–73.Fisher E, Berwick D, Davis K. Achieving health care reform--how physicians can help. The New England journal of medicine 2009;360(24):2495–2497.Pronovost PJ, Miller MR, Wachter RM, Meyer GS. Perspective: physician leadership in quality. Academic medicine : journal of the Association of American Medical Colleges 2009;84(12):1651–1656.McGonigal M, Bauer M, Post C. Physician engagement: a key concept in the journey for quality improvement. Crit Care Nurs Q. 2019;42(2):215–219.Dhalla IA, Tepper J. Improving the quality of health care in Canada. Canadian Medical Association Journal 2018;190(39):E1162-E1167.Digby GC DS, Hobbs H. Strategies for emerging physician leaders in quality improvement to advance the quality agenda and increase organizational alignment. Physician Leadership Journal 2021;8(4):30–37.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,033 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,006 | 0,002 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,003 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».