OP125 A New Collaborative Approach To Assess Innovative Health Technologies
Bibliographic record
Abstract
INTRODUCTION: Decision makers worldwide face the challenge of offering the best health care within a context of scarce resources. Technological developments have multiplied in the past decades, with the lifecycle of technologies becoming shorter. As a result, the traditional Health Technology Assessment (HTA) model is often caught in a too early, too late syndrome. In the province of Québec (Canada), there is no standardized process for assessing non-pharmaceutical technologies for reimbursement purposes, and technologies are therefore introduced via multiple sources. There are concerns that the introduction of some of the most promising technologies is delayed, and on the contrary, that others are introduced without providing a real added value to patients and the health system. METHODS: INESSS (Institut national d'excellence en santé et services sociaux), collectively with stakeholders of the Québec innovation field, has developed a dynamic process for assessing the added value of innovative technologies. POETIS (Processus optimisé d'évaluation des technologies innovantes en santé) aims to identify the technologies with the highest potential for positive impact on patients and the health system, in order to accelerate their implementation and promote their optimal use. RESULTS: POETIS comprises four phases aligned with the lifecycle of technologies: research and development, pre-implementation, limited implementation, and diffusion. It allows a continuum of assessment, from the promise of a technology to its real-world benefit. It differs from other approaches because of the sustained involvement of key stakeholders, including patients, and because it assesses technologies iteratively, therefore fostering their adaptation to better suit patients needs. It is hoped for the first technologies to be assessed in 2017. CONCLUSIONS: HTA has to adapt to the challenges of innovation, and this could be done with a lifecycle approach and an enhanced collaboration with end-users. Developed in Canada, the goals behind POETIS are common to many countries and the process could be adapted by other HTA agencies.
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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 | 0.028 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".