HEALTH TECHNOLOGY REASSESSMENT OF NON-DRUG TECHNOLOGIES: CURRENT PRACTICES
Bibliographic record
Abstract
OBJECTIVES: Obsolescence is a natural phase of the lifecycle of health technologies. Given increasing cost of health expenditures worldwide, health organizations have little choice but to engage in health technology reassessment (HTR); a structured, evidence-based assessment of the medical, social, ethical, and economic effects of a technology, currently used within the healthcare system, to inform optimal use of that technology in comparison to its alternatives. This research was completed to identify and summarize international HTR initiatives for non-drug technologies. METHODS: A systematic review was performed using the terms disinvestment, obsolescence, obsolete technology, ineffective, reassessment, reinvestment, reallocation, program budgeting, and marginal analysis to search PubMED, MEDLINE, EMBASE, and CINAHL until November 2011. Websites of organizations listed as members of INAHTA and HTAi were hand-searched for gray literature. Documents were excluded if they were unavailable in English, if the title/abstract was irrelevant to HTR, and/or if the document made no mention of current practices. All citations were screened in duplicate with disagreements resolved by consensus. RESULTS: Sixty full-text documents were reviewed and forty were included. One model for reassessment was identified; however, it has never been put into practice. Eight countries have some evidence of past or current work related to reassessment; seven have shown evidence of continued work in HTR. There is negligible focus on monitoring and implementation. CONCLUSIONS: HTR is in its infancy. Although health technology reassessments are being conducted, there is no standardized approach. Future work should focus on developing and piloting a comprehensive methodology for completing HTR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.123 | 0.336 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".