How Procurement Judges The Value of Medical Technologies: A Review of Healthcare Tenders
Bibliographic record
Abstract
OBJECTIVES: Procurement's important role in healthcare decision making has encouraged criticism and calls for greater collaboration with health technology assessment (HTA), and necessitates detailed analysis of how procurement approaches the decision task. METHODS: We reviewed tender documents that solicit medical technologies for patient care in Canada, focusing on request for proposal (RFP) tenders that assess quality and cost, supplemented by a census of all tender types. We extracted data to assess (i) use of group purchasing organizations (GPOs) as buyers, (ii) evaluation criteria and rubrics, and (iii) contract terms, as indicators of supplier type and market conditions. RESULTS: GPOs were dominant buyers for RFPs (54/97) and all tender types (120/226), and RFPs were the most common tender (92/226), with few price-only tenders (11/226). Evaluation criteria for quality were technical, including clinical or material specifications, as well as vendor experience and qualifications; "total cost" was frequently referenced (83/97), but inconsistently used. The most common (47/97) evaluative rubric was summed scores, or summed scores after excluding those below a mandatory minimum (22/97), with majority weight (64.1 percent, 62.9 percent) assigned to quality criteria. Where specified, expected contract lengths with successful suppliers were high (mean, 3.93 years; average renewal, 2.14 years), and most buyers (37/42) expected to award to a single supplier. CONCLUSIONS: Procurement's evaluative approach is distinctive. While aiming to go beyond price in the acquisition of most medical technologies, it adopts a narrow approach to assessing quality and costs, but also attends to factors little considered by HTA, suggesting opportunities for mutual lesson learning.
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 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 | 0.020 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".