USING THE HEALTH TECHNOLOGY ASSESSMENT TOOLBOX TO FACILITATE PROCUREMENT: THE CASE OF SMART PUMPS IN A CANADIAN HOSPITAL
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.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".