The future of quality improvement research
Notice bibliographique
Résumé
Presentation The history of quality improvement research (QIR) demonstrates the large growth in improvement activities from early quality assessment and small area variation work through the adoption of industrial quality improvement methods in healthcare operations to the recent opportunities inherent in the Affordable Care Act of 2010. But after 40 years of development, significant growth in these scholarly activities has not produced comparable growth in insights, practical guidance, or progress toward better care. Five challenges influence the trajectory of improvement work and implementation science. The first challenge relates to the innovations and evidence base to improve healthcare. The focus of innovation and research in improvement has been on strategies, facilities and systems that are leaders in performance and quality improvement – the organizational equivalents of healthy white males. This makes differentiation and generalizability to the range of organizational settings difficult. A concerted effort must be made to focus on research conducted within, and with relevance to, the broader practice environment. The second challenge includes multiple logistical barriers such as access to study sites, the limited funding opportunities for QIR, and the lack of consistent IRB guidance and interpretation of regulations. Underlying these barriers is a considerable lack of clarity surrounding the nature of QIR relative to other types of health research. With the exception of the recent statement on cluster randomized trials by the Ottawa Ethics of Cluster Randomized Trials Consensus Group (2012)[1], the absence of consistent guidelines for QIR poses challenges for researchers trying to obtain local IRB review as well as investigators competing with others using more traditional research methods during the grant review process. QIR researchers need to embrace the IRB process and develop explicit, consensus-based guidance to facilitate more consistent reviews at the funding and IRB stages. The third challenge includes the professional differences that stem from the diversity of academic disciplines and types of institutions from which people involved in this work emerge. The concepts and definitions arising from diverse disciplinary roots make it difficult to achieve progress and move forward collectively. These factors pose barriers to the scholarly QIR community and, more importantly, contribute to confusion and decreased credibility among external stakeholders and scientists in the more traditional fields of study surrounding QIR. Lack of consensus and clarity impede the advance of this science because researchers cannot explain the work consistently to funding agencies, editorial review boards and other stakeholders. The fourth challenge is the need to strengthen the theoretical foundations for this work. There is an urgent need to assess whether we have the right theories, too many theories or simply a lack of guidance in using theories to build the science of improvement. The weak theoretical basis for QIR contributes to the fifth and final challenge to the future, which is advancing the science through robust and appropriate research approaches, designs and methods. The field has failed to reach consensus about the major research questions and goals for QIR, and continues to debate the appropriate methods for improvement and implementation work. Different views regarding the value and need for various research approaches and methods for conducting QIR limits the production of practical and effective insights and tools for researchers, clinicians, organizations, and policy decision makers.
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 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 | 0,039 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».