USING THE HEALTH TECHNOLOGY ASSESSMENT TOOLBOX TO FACILITATE PROCUREMENT: THE CASE OF SMART PUMPS IN A CANADIAN HOSPITAL
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to present the experience of a Canadian hospital-based health technology assessment (HTA) unit that performed the traditional functions of the HTA process along with many other activities to facilitate the choice of smart pumps. METHODS: A rapid literature review was initiated, but little evidence was found. Moreover, the evidence provided was too far from our hospital context. To help our decision makers, we offered them a list of various services based on the skills of our HTA unit staff. RESULTS: To involve our HTA unit in the choice of the new smart pumps led to a strong collaboration between hospital services. After a rapid review on smart pumps, we proceeded to establish the clinical needs, followed by an evaluation of technical features. To ascertain clinical needs, we participated in the establishment of a conformity list for the tender, a failure and mode-effect analysis, an audit on the use of actual smart pumps, and simulation exercises with nurses and doctors to evaluate the ease of use and ergonomics. With regard to technical tests, these were mainly conducted to identify potential dysfunction and to assess the efficiency of the pump. This experience with smart pumps was useful for evidence-based procurement and led to the formulation of a nine-step process to guide future work. CONCLUSIONS: HTA units and agencies are faced with rapid development of new technologies that may not be supported by sufficient amount of pertinent published evidence. Under these circumstances, approaches other than evidence-based selection might provide useful information. Because these activities may be different from those related to classic HTA, this widens the scope of what can be done in HTA to support decision making.
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it