OP53 An Actionable And Legible Toolbox For The Appraisal Of Healthcare Innovations Developed Through Nationwide Stakeholder Collaboration
Bibliographic record
Abstract
Introduction In Québec, Canada, decisions about implementing innovations are taken both centrally for province-wide access and locally by healthcare institutions. There is no systematic evaluation process and various stakeholders are involved, notably within a new nationwide governance structure. There was a wish to increase consistency and clarity with the principles and methods used by various bodies across the innovation lifecycle. Methods The starting point was the Institut national d’excellence en santé et services sociaux (INESSS) multidimensional framework, which focuses on the population-level, clinical, economic, organizational, and sociocultural value of drugs, technologies, and interventions. The framework, already under evolution drawing on Responsible Innovation in Health (RIH), evolved through collaborative work between INESSS’ methodological and scientific teams, but also and foremost with diverse groups and institutions within the provincial innovation ecosystem (e.g., university-based incubators, regional hospitals). The first steps were to capture current concepts and practices from different stakeholders, as well as their operational needs in terms of assessment tools. Results This multistakeholder taskforce resulted in the development of an operational toolbox meant to guide the value appraisal of innovations through a lifecycle approach. First aimed at stakeholders involved locally in healthcare institutions, the work conducted was equally beneficial to INESSS by enabling its evaluation teams to contribute to the operational tools needed to enhance clarity and legibility of the agency’s processes and methods. The level of collaboration with stakeholders across the province was also unique and has strengthened the understandability and actionability of the toolbox developed. Some challenges were faced, and related actions will be discussed. Conclusions Both the taskforce process and its output contributed to improving consistency in the assessment of innovations across the province. They made more explicit what may sometimes be perceived as the HTA “black box.” The INESSS value appraisal framework also evolved considering key elements of responsibility from RIH and through this collaboration with stakeholders, and its applicability in different contexts was reinforced.
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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 | 0.178 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".