Hospital Procurement with Concentrated Sellers: A Case Study of Hip Prostheses
Bibliographic record
Abstract
Procurement within the NHS is attracting increasing research and policy interest. However, most of the emphasis has been on the buyer (the NHS), with less attention paid to the behaviour of suppliers (often pharmaceutical companies). For medical devices very little is publicly documented about procurement and even less about the supplying industry. This paper uses a case study of artificial hip prostheses to indirectly explore how procurement choices are made within the NHS. We recognise the roles of the various players (patient, surgeon and hospital procurement department) when purchases are made from a potentially highly oligopolistic supplying industry. Using data from the National Joint Registry for England and Wales, we show that the supplying industry is indeed highly oligopolistic, with the potential for the exercise of market seller power. At the national level the NHS as a whole purchases from the equivalent of just four large sellers. However, typically individual hospitals are buying from only two, or in some instances one seller. Given this backdrop, we develop a theoretical framework explaining prosthesis choice, considering the roles and preferences of the patient, surgeon, hospital and supplier. This provides a set of hypotheses tested using an econometric model in which the diversity of prosthesis choice at the hospital level is explained by a vector of patient and hospital characteristics. This reveals little evidence that patient heterogeneity is a major determinant of diversity of procurement choices. More important are hospital size (which will be related to the number of surgeons), status of the hospital, recent NHS reforms and the potential role of the supplier. These findings provide a basis for future survey analysis of surgeons and hospital procurement departments designed to discover more directly how decisions are made and how suppliers bargain with hospitals.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".