OP79 Incorporating Environmental Impacts Into Health Technology Assessment: An Examination Of Potential Approaches And Challenges
Bibliographic record
Abstract
Introduction In light of government and healthcare system commitments to reducing the carbon footprint of healthcare, health technology assessment (HTA) agencies are increasingly motivated to investigate how to consider environmental sustainability in their assessments and guidance. This constitutes a major departure from the existing remits and objectives of most agencies, which typically focus on improving population health outcomes. This presentation seeks to identify options for incorporating environmental impact data into HTA and to examine the main challenges, focusing on the National Institute for Health and Care Excellence (NICE) as a case study. Methods We present four broad approaches that could be pursued, informed by policy analysis undertaken by NICE. The strengths, weaknesses and implications of each approach are assessed. Results The first option is to act as an ‘information conduit’, aggregating and distributing in a standardized format environmental impact information that is provided voluntarily by health technology manufacturers. The second is to present complementary analyses of environmental impact data, separately but alongside results from established health economic analyses (‘parallel evaluation’ model). The third is to incorporate environmental impact data into health economic analyses, for example by monetizing environmental outcomes, so that quantitative estimates of treatment value are directly affected by environmental benefits and costs (‘integrated evaluation’ model). The fourth is to create new decision-making frameworks for evaluating healthcare interventions that are not expected to improve health-related outcomes, but claim to have relative environmental benefits. Conclusions We conclude that these approaches are not mutually exclusive, and all involve some degree of benefit and risk. We explain why the parallel evaluation model may be the most appropriate approach for NICE as a first response to the increased demand for guidance on the environmental impact of health technologies. We also outline activities being undertaken by NICE and other agencies such as the Canadian Agency for Drugs and Technologies in Health to develop new methodologies for incorporating environmental impact data into their HTAs.
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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 | 0.100 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".