Translating Strategies into Tactical Actions: The Role of Sourcing Levers in Healthcare Procurement
Bibliographic record
Abstract
Expensive medical devices, especially in the areas of orthopedics, and cardiology, have a significant impact on hospital costs and the delivery of high-quality services. These medical supplies are known as physician preference items (PPIs), as they act as “surrogate buyers”—impacting the selection and sourcing of products. There is a gap between the purchasing strategy and the adoption of tactical activities for these complex medical supplies. In the context of the healthcare exceptionalism thesis, this research investigates how healthcare organizations can successfully adopt suitable sourcing levers aiming to achieve different purchasing results. This research conducts a multi-case study in 15 healthcare organizations in nine countries. Three new sourcing levers specific to the healthcare sector emerged, based on the healthcare exceptionalism thesis. It was possible to identify five main sourcing levers clusters. The fit between strategy and tactical level can be allowed by the implementation of suitable sourcing levers—facilitating the achievement of the desired objectives. Healthcare procurement practitioners should assess the fit between strategy and the tactical level by employing suitable sourcing levers. Organizations wishing to move towards a value-based procurement approach should adopt a set of supporting sourcing levers to enable this transition.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".