To What Extent Can Digital Health Technologies Comply With the Principles of Responsible Innovation? Practice-and Policy-Oriented Research Insights Regarding an Organisational and Systemic Issue
Bibliographic record
Abstract
BACKGROUND: Digital health technologies (DHTs) have expanded exponentially since the COVID-19 crisis and have prompted questions about their impact across all levels of health systems. Because health organisations and systems play a central role in the success or failure of the transition to more equitable and sustainable societies, the concept of Responsible Innovation in Health (RIH), focused on aligning the processes and outcomes of innovation with societal values, is gaining interest in research, policy, and practice. This study aims to explore enablers and constraints to the development, procurement and/or utilisation of responsible DHTs in health organisations. METHODS: Semi-structured interviews were conducted with 29 stakeholders concerned with the development, procurement, and/or utilisation of DHTs in a large Canadian academic health centre. Data were thematically analysed through a mixed deductive-inductive process using the RIH framework. RESULTS: Our findings highlight that the consideration of RIH principles in the development, procurement, and/or utilisation of DHTs depends mainly on organisational and systemic factors and conditions, namely: (1) the presence of an organisational culture that promotes RIH in its innovation-related practices and processes; (2) availability of material and financial resources as well as expertise in certain fields (eg, environmental sustainability); (3) the evolution of health technology assessment (HTA) practices to include other dimensions beyond effectiveness, safety, and costs; (4) the scope of the regulatory and legal frameworks that govern the approval and use of DHTs; and (5) the role of the market (eg, venture capital) in the design of federal and provincial innovation policies. CONCLUSION: This study provides insights on practice, policy, and political issues that health organisations may face in the development, procurement, and/or utilisation of responsible DHTs. It can help scholars, practitioners, decision-makers, and industry to create the conditions for a better integration of RIH principles into health organisations and systems.
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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 | 0.119 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.061 |
| Scholarly communication | 0.033 | 0.028 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".