Why digital innovation may not reduce healthcare’s environmental footprint
Bibliographic record
Abstract
Digital innovations come with their own environmental cost and should not be seen as a simple fix for healthcare emissions, argue Gabrielle Samuel and colleagues Healthcare is becoming increasingly digitalised through innovations in information and communication technologies as well as advances in machine learning and artificial intelligence (AI).1 Advocates enthuse that this digitalisation—including monitoring devices, streaming, and data storage—will improve key aspects of healthcare delivery such as safety, accessibility, quality of care, effectiveness, and efficiency.2 Others debate whether these promises can be met because of complex social, cultural, economic, and political implementation challenges.3 More recently, digital innovation has been promoted as a means to reduce the environmental harms associated with healthcare delivery.4 Healthcare systems contribute to roughly 5% of a country’s total greenhouse gas emissions, with this figure often being higher in high income countries.5 Although digitalisation can reduce environmental harms, technologies could also be implemented in ways that do not lead to reductions. Indeed, given the paradoxical increase in energy use associated with the introduction of energy saving technologies—the so called rebound effect—digital innovation may increase resource use with little change to health outcomes. Digital innovations have the potential to decrease the environmental harm from health systems in several ways (box 1). First, digital innovations are expected to help reduce the greenhouse gas emissions associated with existing healthcare facilities by improving their efficiency. In the UK the NHS has predicted carbon savings through the use of realtime monitoring, including artificial intelligence, to better control buildings (eg, lights, heating, and cooling) and to forecast resource allocation more effectively.6 Use of digital technologies to predict electricity and water consumption across various healthcare facilities has allowed hospital managers to identify variation in usage and deal with the causes.7 Box 1 ### How digital technologies might reduce the environmental harms of healthcare #### Improving the operational efficiency of existing healthcare infrastructureRETURN TO TEXT
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".