Logistics outsourcing in the healthcare sector: Lessons from a Canadian experience
Bibliographic record
Abstract
Abstract Logistics activities are seen by many healthcare organizations as an opportunity for financial savings. The level of logistics complexity in these organizations may explain the challenges they face in applying solutions common in other industries. This case study of a medical‐supply distribution outsourcing initiative to a logistics services provider by a group of hospitals in a region of Canada helps elucidate this complexity. The objective of this article is to identify the dissonances between various points of view in order to articulate lessons for managers while taking into account the specifics of the healthcare sector. By examining information from several sources, this study shows the necessity of: 1) setting objectives and managing expectations in order to maintain the interest and participation of stakeholders throughout the project; 2) updating internal logistics processes prior to outsourcing; 3) carefully considering a gradual transition phase by ensuring short‐term benefits for both partners; 4) requiring problem‐solving skills as a selection criterion for the logistics services provider to ensure continuous improvement in the performance of the outsourced activity; and 5) developing a governance accountability framework to support problem solving between all parties involved. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".