HEALTH TECHNOLOGY REASSESSMENT: THE ART OF THE POSSIBLE
Bibliographic record
Abstract
BACKGROUND: Health technology reassessment (HTR) is "a structured, evidence-based assessment of the clinical, social, ethical, and economic effects of a technology currently used in the healthcare system, to inform optimal use of that technology in comparison to its alternatives." The purpose of this study is to describe the key themes in the context of current HTR activities and propose a way forward for this newly emerging field. METHODS: Data were gathered from a workshop held as part of the 2012 Canadian Agency for Drugs and Technology in Health (CADTH) symposium. The workshop consisted of two panel presentations followed by discussion; data gathered, including presentations and rich audience discussion transcripts, were analyzed for key themes emerging in the field of HTR using constant comparative analysis. RESULTS: The language chosen to describe HTR will set the tone for engagement. The identification of champions at multiple levels and political will are essential. Key lessons from international experience are: disinvestment is difficult, focus on clinical areas not specific technologies, identify clear goals of the HTR agenda. Six key themes were identified to move the HTR agenda forward: emphasize integration over segregation, focus on development of HTR methods and processes, processes are context-specific but lessons must be shared, build capacity in synergistic interdisciplinary fields, develop meaningful stakeholder engagement, strengthen postimplementation monitoring and evaluation. CONCLUSIONS: To move this field forward, we must continue to build on international experiences with a focus on developing novel methodological approaches to generating, incorporating, and implementing evidence into policy and practice.
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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 | 0.136 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.119 |
| Scholarly communication | 0.033 | 0.054 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".