Harmonization of HTA--based reimbursement and regulatory approval activities: a qualitative study.
Bibliographic record
Abstract
BACKGROUND: Regulatory approval and reimbursement are necessary to bring health technologies to market. As both are distinct processes, the lack of interaction between regulators and payers has been criticized for resulting in health system inefficiencies. OBJECTIVES: This study describes the experiences of health system stakeholders on the development and implementation of initiatives to better harmonize HTA-reimbursement and regulatory activities, as well as Canadian perceptions on such initiatives. METHODS: Thematic analysis of semi-structured interviews was conducted. Participants represented those whom have published or worked actively in this area or who held positions in HTA and/or reimbursement agencies that gave them insight into the Canadian healthcare system. Interviews were recorded, transcribed and analyzed to identify emerging themes and relationships. RESULTS: Fourteen individuals from twelve organizations participated, including representatives from Canada, US, UK and Netherlands. Harmonization was identified as either a means to: (i) develop economies of scale in the generation of clinical data; and/or (ii) align the lifecycle of a health technology. For such initiatives to be successfully implemented, the following key issues emerged: (i) fostering healthy relationships; (ii) promoting well-intentions; (iii) defining governance and leadership clearly; and (iv) securing organizational infrastructure. CONCLUSIONS: Attempts to harmonize reimbursement and regulatory activities is in its infancy; although, much can be learned from current Canadian and international experiences. Within Canada there is much interest, although concerns have been raised on the extent to which harmonization is possible. Successful policy implementation would require inter-agency collaborations, with particular attention towards capacity building and strong leadership.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".