Digitalization of the healthcare supply chain: A roadmap to generate benefits and effectively support healthcare delivery
Bibliographic record
Abstract
The healthcare supply chain lags far behind supply chains in other industries in terms of performance and the deployment of best practices. Managers could bridge this gap and improve the performance of the healthcare supply chain by implementing digitalization initiatives. However, the erratic, disconnected digitalization of practices already deployed in the healthcare sector makes it difficult to maximize the potential of these initiatives. In order to generate the greatest benefits from digitalization while improving healthcare delivery, this article sets out a roadmap for implementing technologies. Unlike previous studies that focused on the entire supply chain or had been limited to patient flow, this study adopts the perspective of the hospital as a central launching point for digitalization initiatives. The roadmap, which involves both internal and external digitalization trajectories, is based on a research methodology that combines observations with an umbrella review of literature. This methodology enables us to capture the research challenges associated with the healthcare supply chain and show how digitalization initiatives can address them. The digitalization proposals put forward are structured in terms of priority and centered on hospitals. These proposals can help managers make improvements to the supply chain and also clinical flows.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".