Integrating environmental considerations in digital health technology assessment and procurement: Stakeholders’ perspectives
Bibliographic record
Abstract
Background: Digital health technologies (DHTs) are promoted as means to reduce the environmental impact of healthcare systems. However, a growing literature is shedding light on the highly polluting nature of the digital industry and how it exacerbates health inequalities. Thus, the environmental footprint of DHTs should be considered when assessing their overall value to healthcare systems. The objectives of this article are to: (1) explore stakeholders' perspectives on integrating the environmental impacts of DHTs in assessment and procurement practices; (2) identify the factors enabling or constraining the operationalisation of such a change; and (3) encourage a constructive dialogue on how environmental issues fit within healthcare systems' push for more DHTs. Methods: Semi-structured interviews were conducted with 29 stakeholders involved in DHTs in a large Canadian academic healthcare centre. Data were collected and analysed through a mixed deductive-inductive process using a framework derived from diffusion of innovations theories. Results: The integration of the environmental impact of DHTs in assessment and procurement is contingent upon key micro-meso-macrosystemic factors that either enable or constrain changes in practices and processes. Innovation (micro) factors include stakeholders' recognition of the environmental issue and the extent to which it is feasible for them to address the environmental impact of DHTs. Organisational (meso) factors include the organisation's culture, leadership, policies, and practices, as well as the expertise and professional skillsets available. Finally, external (macro) factors include political and regulatory (e.g., national strategy, laws, standards, norms), economic (e.g., business models, public procurement), and professional and scientific factors (e.g., evidence, methodologies, clinical guidelines). Conclusion: Considering the environmental impact of DHTs depends on micro-meso-macrosystemic factors involving a variety of stakeholders and levels of governance, sometimes with divergent or even antagonistic objectives and expectations. It highlights the importance of better understanding the complexity inherent in the environmental shift in healthcare.
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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 | 0.027 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".