ArticleLearning health systems2025
Advancing environmentally sustainable learning health systems: Perspectives from a Canadian health center.
Article in Learning health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Advancing environmentally sustainable learning health systems: Perspectives from a Canadian health center.Learning health systems · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: There is increasing demand for health systems to reduce greenhouse gas emissions and invest in climate-resilient health care. Coordinating organizational structures and processes for reducing health system emissions presents challenges. Learning health systems, defined as systems that seek to continuously generate and apply evidence, innovation, quality, and value in health care, can guide health systems with planning organizational structures and processes to advance environmentally sustainable healthcare. The purpose of this research is to gather in-depth insight from key health system leaders and healthcare professionals to identify challenges and recommendations for planning environmentally sustainable learning health systems. Methods: Environmental scan methods were used, comprising jurisdictional literature review and informal discussions with key informants at one tertiary care center in Nova Scotia, Canada. Key informants were asked to describe challenges of coordinating environmentally sustainable health system structures and processes, and recommendations to advance planning for environmentally sustainable learning health systems. Deductive thematic analysis was used to categorize challenges and recommendations into seven characteristics of a learning health system framework. Results: Informal discussions with 16 key informants provide detailed descriptions of 7 challenges and recommendations for planning and coordinating organizational structures and processes to advance environmentally sustainable learning health systems. Health system challenges include limited patient and community engagement, no systematic approach to measuring and monitoring emissions data, and limited knowledge of sustainability co-benefits and strategies for mobilizing sustainable organizational change. Recommendations include engaging patients and communities in co-creation of sustainable healthcare, monitoring of emissions data identifying high-impact areas for action, and well-coordinated leadership supporting sustainable policies, procedures, and decision-making in practice. Conclusion: Learning health systems provide structure for establishing critical processes to adapt to routinely collected data through rapid cycle improvements, and operationalization of value-based health care that prioritizes health outcomes, reduction of costs, and mitigating environmental impacts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.