Achieving optimal technology use: A proposed model for health technology reassessment
Bibliographic record
Abstract
BACKGROUND: Healthcare providers, managers and policy-makers in many jurisdictions are focused on a common goal: optimizing value and quality of care provided to their citizens within a resource envelope. Health technology reassessment 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 with its alternatives. There are, however, few practical experiences with health technology reassessment and, as such, a nascent theoretical and methodological base. Health technology reassessment is a key strategy to achieve optimal healthcare resource utilization, and establishing a model for health technology reassessment is a required methodological step. METHODS AND RESULTS: The purpose of this article is to answer three formative questions: (1) What is health technology reassessment? (2) When should a health technology reassessment be implemented? (3) What is the role of health technology reassessment in evidence-informed health policy? Finally, we propose a conceptual framework for health technology reassessment, which others can modify, adapt, or adopt in their own context. The model consists of three broad phases and six iterative stages: (1) identification, (2) prioritization, (3) evidence synthesis, (4) determine policy/practice recommendation, (5) policy/practice implementation and (6) monitoring and evaluation. Two foundational components (meaningful stakeholder engagement and ongoing knowledge exchange and utilization) are represented across all stages. CONCLUSION: This description of health technology reassessment and the proposed model can be used by healthcare policy-makers and researchers to advance the field of technology management, with the goal of achieving optimal use throughout a technology's lifecycle.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".