32 Mapping the co-benefits of reducing low-value care and the environmental impacts of care: a literature analysis & research agenda
Notice bibliographique
Résumé
Objectives Reducing low-value care and improving healthcare’s climate readiness are critical factors to improve the sustainability and resilience of health systems across the globe. By definition, low-value care generates carbon emissions, waste and pollution without improving patient or population health. Thirty percent of clinical care has been deemed low- or no value and as much as 80% of healthcare carbon emissions arise from clinical care. Little is known about the knowledge, research, interventions and practice change being developed on the co-benefits of reducing low-value care and reducing the environmental impacts of care. The objective of this study was to advance the field by developing foundational knowledge, through a literature analysis (scoping review & bibliometric analysis) and research agenda synthesis, of key aspects of co-benefits research and practice change. Methods We identified, collected and synthesized data from research and practice change publications on reducing low-value care and improving the climate resilience and sustainability of health systems. Four databases, Medline, Embase, Scopus and CINAHL, were searched from inception to January 2023. We followed scoping review methodology to collect and analyze the data. The database searches identified 1794 unique articles for title and abstract screening; 264 articles moved to full-text review. For the bibliometric analysis of the included articles, we analyzed authors, organizations topics, collaborations, citations and journals. Biblioshiny, additional R-based applications, and Microsoft Excel were used for publication, co-authorship and co-word analyses. Results Seventy six articles published 2013-2022 met inclusion criteria, with over 75% of the articles published since 2020. Thirty percent of the articles were empirical studies with the remainder being commentary, editorials or opinion. A quarter of the articles focused equally on the importance of reducing low-value care and improving environmental impact of healthcare; 60% of articles focused on reducing the environmental impact of care; 15% focused on reducing low-value care. The majority of articles focused on healthcare generally (32%), with the remainder focused on practices such as laboratory testing (17%), and surgery and anesthesia (15%). The majority of articles were written by multi-national teams, with first authors predominantly from Australia (42%), UK (23%) and USA (20%). The bibliometric analysis revealed distinct and geographically specific collaborations, in addition to a number of nation-spanning research groups. Reported research and practice priorities included a need for increased resource stewardship, standards, metrics and provider education. The lack of evidence, data, leadership and cohesive strategy were reported as challenges in the field. Directions for future research and practice included increasing transparency on environmental impacts and patient education and communication. Conclusions This work provides foundational knowledge to advance understanding on the co-benefits of reducing low-value care and improving environmental sustainability. This literature synthesis mobilizes existing knowledge on co-benefits research and practice to support the development of solution to address low-value care and the climate resilience and sustainability of health systems. Next steps include consensus meeting to develop a shared research agenda and community of practice.
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 | 0,037 | 0,112 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,111 | 0,124 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,015 | 0,016 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,002 |
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 ».