Current status of health technology reassessment of non-drug technologies: survey and key informant interviews
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 health care system, to inform optimal use of that technology in comparison to its alternatives. Little is known about current international HTR practices. The objective of this research was to summarize experience-based information gathered from international experts on the development, initiation and implementation of a HTR program. METHODS: A mixed methods approach, using a survey and in-depth interviews, was adopted. The survey covered 8 concepts: prioritization/identification of potentially obsolete technologies; program development; implementation; mitigation; program championing; stakeholder engagement; monitoring; and reinvestment. Members of Health Technology Assessment International (HTAi) and the International Network of Agencies for Health Technology Assessment (INAHTA) formed the sampling frame. Participation was solicited via email and the survey was administered online using SurveyMonkey. Survey results were analyzed using descriptive statistics. To gather more in-depth knowledge, semi-structured interviews were conducted among organizations with active HTR programs. Interview questions were developed using the same 8 concepts. The hour-long interviews were recorded, transcribed and analyzed using constant comparative analysis. RESULTS: Ninety-five individuals responded to the survey: 49 were not discussing HTR, 21 were beginning to discuss HTR, nine were imminently developing a program, and 16 participants had programs and were completing reassessments. The survey results revealed that methods vary widely and that although HTR is a powerful tool, it is currently not being used to its full potential. Of the 16 with active programs, nine agreed to participate in follow-up interviews. Interview participants identified early and extensive stakeholder engagement as the most important factors for success. A lack of top-down support and financial and human resources are inhibiting program development. DISCUSSION: HTR is in its infancy. Although HTRs are being conducted, there are no standardized approaches. However, much can be learned from current international work. Future work should focus on developing a comprehensive methodology, reporting the processes of reassessments and sharing successes and challenges in a common platform.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.053 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".