Metrics used in quality improvement publications addressing environmental sustainability in healthcare: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Quality improvement (QI) practices and scholarship are increasingly concerned with environmental sustainability given the negative health outcomes caused by the ecological crisis, as well as the environmental impacts of healthcare delivery itself. A core component of QI activities is measuring change. How sustainability metrics have been used in QI is unclear. We conducted a scoping review of metrics used in published sustainability-focused QI initiatives. DATA SOURCES: MEDLINE, EMBASE, CINAHL and Scopus from 2000 to 2023. ELIGIBILITY CRITERIA: Published healthcare QI initiatives intended to address environmental sustainability with at least one quantitative sustainability metric. DATA ANALYSIS: Publication, study, measurement and QI intervention characteristics were charted from included studies. Data items were synthesised and presented narratively as well as quantitatively. RESULTS: We screened 6294 studies and included 90 full-text publications. The studies were published from 2000 to 2023, with the majority (61%, 55/90) published since 2020. Publications originated from a wide range of clinical disciplines with most QI projects situated in the inpatient setting (78%, 70/90). Environmental sustainability metrics were subcategorised into activity data and environmental impact indicators. Some papers included more than one category of activity data, with the most common being cost (88%, 79/90), hospital waste (52%, 47/90), anaesthetic gases (49%, 44/90), disposable use (24%, 22/90) and distance travelled (14%, 13/90). Fewer publications included environmental impact indicators, with global warming potential dominating this category (53%, 48/90). DISCUSSION: There is a need to align QI efforts with environmental sustainability. However, there is limited guidance specific to healthcare QI on how to measure environmental impacts of these efforts. This review illuminates that sustainability-focused QI efforts to date have used a relatively narrow set of sustainability metrics. QI scholars and practitioners can benefit from further education, measurement frameworks and guidelines to effectively incorporate environmental sustainability metrics into QI efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.114 | 0.377 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.078 | 0.079 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".