The climate impacts of healthcare digitalization: A scoping review
Bibliographic record
Abstract
Objective: The rapid digitalization of healthcare has implications for its carbon footprint. The goal of this scoping review was to identify how digitalization is proceeding in healthcare and the mechanisms through which it can affect the climate impacts of healthcare. Methods: The scoping review was conducted following PRISMA guidelines and utilized the databases Web of Science and PubMed to identify literature on the climate impacts of digitalization in healthcare. The literature search identified 32 relevant studies. In addition, diagnostics, overdiagnosis, self-tracking technologies, and artificial intelligence (AI) were identified as key topics not sufficiently represented in the literature review, and related articles were added into the material. Results: Most carbon footprint analyses focused on telemedicine solutions, but research topics also included health informatics, education, diagnoses, overdiagnosis, treatments, medical appliances, robotics, and AI. Regarding telemedicine, the carbon footprint of the virtual consultations ranged between 0·005 and 3 kgCO2e, while the in-person healthcare clinic visits ranged between 0·57 and 178 kgCO2e depending on the mode of transport used, the difference in the carbon footprint being 79-99%. Although the transparency of carbon footprint analyses was often low, system boundaries were often different, and the evidence for digitalization decreasing or increasing climate impacts was limited. Conclusions: The findings highlight the need for future research on these topics and that leadership and setting strategic goals in particular were missing in the literature. Our scoping review also presents a framework for digitalization-related topics and means for advancing a lower carbon footprint in healthcare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".