Responsible innovation in health and health system sustainability: Insights from health innovators’ views and practices
Bibliographic record
Abstract
Although healthcare managers make increasingly difficult decisions about health innovations, the way they may interact with innovators to foster health system sustainability remains underexplored. Drawing on the Responsible Innovation in Health (RIH) framework, this paper analyses interviews (n=37) with Canadian and Brazilian innovators to identify: how they operationalize inclusive design processes; what influences the responsiveness of their innovation to system-level challenges; and how they consider the level and intensity of care required by their innovation. Our qualitative findings indicate that innovators seek to: 1) engage stakeholders at an early ideation stage through context-specific methods combining both formal and informal strategies; 2) address specific system-level benefits but often struggle with the positioning of their solution within the health system; and 3) mitigate staff shortages in specialized care, increase general practitioners’ capacity or patients and informal caregivers’ autonomy. These findings provide empirical insights on how healthcare managers can promote and organize collaborative processes that harness innovation towards more sustainable health systems. By adopting a RIH-oriented managerial role, they can set in place more inclusive design processes, articulate key system-level challenges, and help innovators adjust the level and intensity of care required by their innovation.
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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 | 0.054 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.051 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".