A Comprehensive Procurement Process for Medical Device Acquisition
Bibliographic record
Abstract
Traditional Request for Proposal (RFP) projects involve Biomedical Engineering (BME) focusing on the evaluation of technical specifications and service support considerations. In a recent RFP in Vancouver, Capital Procurement empowered BME to manage the extensive regional purchase of epidural pumps and their accessories. A comprehensive evaluation committee included and engaged all key stakeholders from Anaesthesiology, Nursing, Pharmacy, Supply Chain, BME and a Human Factors Specialist. The result of the evaluation was the successful selection of an epidural infusion pump based on patient safety and clinical consideration. The fast‐paced project, driven by the impending obsolescence of the existing Epidural pump system, aimed to improve on the patient safety of epidural infusions with the procurement of “smart pump” technology. The evaluation process incorporated safety requirements in the RFP, a heuristic evaluation of all proposed pumps, an enhanced technical evaluation, cognitive walkthroughs with clinical users, and a comprehensive usability study in parallel with real‐time clinical evaluations. The primary focus of the evaluation process was to understand the clinical and technical workflows to isolate deficiencies and the potential for error. The implementation process required the review of the physician order sets and medication labeling practices in Pharmacy to consistently present medication information. The pumps were programmed using a drug error reduction system to mirror the order sets and labels to simplify user interaction for increased safety. This abstract illustrates that a comprehensive evaluation team for medical device acquisitions can have far‐reaching benefits that improve patient safety and system efficiencies in the entire organization.
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 | 0.051 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".