Evidence map›Paper›PMID 42481170›Full record

ArticleBMJ open quality2026

From patient to planet: integrating climate impact into a hospital medicine initiative reducing blood urea nitrogen testing.

Pamela Mathura, Xinyu Amy Shi, Sandra Marini, Narmin Kassam

Abstract read
In one paragraph

Article in BMJ open quality, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Pamela MathuraAlberta Health Services, Edmonton, Alberta, Canada pam.mathura@albertahealthservices.ca.
Xinyu Amy ShiMcMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada.
Sandra MariniMedicine, University of Alberta Faculty of Medicine & Dentistry, Edmonton, Alberta, Canada.ORCID http://orcid.org/0009-0000-2980-1416
Narmin KassamMedicine, University of Alberta Faculty of Medicine & Dentistry, Edmonton, Alberta, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPursuing climate-conscious care has become a key focus of quality improvement (QI) efforts. Hospitals across Alberta, Canada, implemented a multicomponent intervention to reduce unnecessary blood urea nitrogen (BUN) testing. This study evaluated the broader environmental impact of the intervention across multiple hospital medicine settings.

methodsUsing a pre-post study design, monthly BUN test counts were compared before and after implementing the initiative across six hospital medicine wards to calculate percentage reductions. A secondary analysis assessed the initiative's environmental impact using the Lab Wisely Carbon Calculator, which converted reduced BUN testing into equivalent travel distances. Avoided carbon dioxide (CO₂) emissions were estimated at 32.1 g/test and tree equivalents were calculated using an annual absorption rate of 21.77 kg CO₂/tree.

resultsHospitals showed monthly reductions in unnecessary BUN testing between 51% and 95%, saving an estimated 5101.92 km of travel and avoiding 169.39 kg of CO₂ emissions monthly, equivalent to planting about 93 trees annually. The climate impact varied by hospital, reflecting differences in the intervention strategies used.

conclusionsIntegrating environmental metrics into QI initiatives offers a strategic path to climate-conscious care, promoting sustainability alongside fiscal responsibility and resource efficiency.

Indexed as

ClimateAlbertaHospitalsHumansQuality ImprovementHealthcare quality improvementHospital medicineLaboratory medicine

Identifiers

PMID42481170
PMCPMC13404617

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.