End-user involvement in health technology assessment (HTA) development: A way to increase impact
Bibliographic record
Abstract
OBJECTIVES: A mechanism to increase the influence of Health Technology Assessments (HTAs) on hospital policy decisions was developed. METHODS: We describe the process and results of an experiment in which a local in-hospital HTA unit was created to provide sound evidence on technology acquisition issues, and to formulate locally appropriate policy recommendations. The Unit consists of a small technical staff that accesses and synthesizes the evidence incorporating local health and economic data, and a Policy Committee that develops policy recommendations based on this evidence. It represents administration, health-care professionals, patients, and representatives of the clinical disciplines affected by each issue. The level of success of the Unit was independently evaluated. RESULTS: To date, 16 reports have been completed, each within 2-4 months. Five recommended unrestricted use, seven recommended rejection, and four recommended very limited use of the technology in question. All have been incorporated into hospital policy. Budget impact is estimated at approximately 3 million dollars of savings per year. CONCLUSIONS: This local in-house HTA agency has had a major impact on the adoption of new technology. Probable reasons for success are (i) relevance (selection of topics by administration with on-site production of HTAs allowing them to incorporate local data and reflect local needs), (ii) timeliness, and (iii) formulation of policy reflecting community values by a local representative committee. Because over one third of all health-care costs are incurred in the hospital, diffusion of this model could have a significant effect on the quantity and quality of health-care spending.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.007 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".