Digitalization Initiatives of Home Care Medical Supply Chain: A Case-Study-Based Approach
Bibliographic record
Abstract
The exceptional COVID-19 crisis has shown the fragility of the health supply chain and the need for its digital transformation. However, digitization initiatives for healthcare supply chain have focused mainly on the hospital as a central point of consumption as well as on the relationship between the hospital and its suppliers. Thus, unlike previous studies that have focused on the digitalization of the internal supply chain of hospitals, this article is centered on the digitalization of external trajectories, particularly, the home care medical supply chain. This perspective complements the hospital-centric model, in which logistics activities are analyzed only from the point of view of the focal actor, which is the hospital. In order to understand this contemporary phenomenon embedded in a local context, we have conducted an in-depth case study. Based on an enriched model of the digital supply chain developed by Queiroz et al. (2021), this article offers for the first time a deep understanding and digitalization initiatives of the home care medical supply chain. The research proposals can guide logistics managers and nurses on their initiatives of digitalization of the home care medical supply chain, and further strengthen the healthcare development. This article draws the attention of healthcare stakeholders to the importance of efforts in the digitalization of external trajectories, thus allowing the healthcare system to adapt to the growing needs of elderly people and people with physical or mental disabilities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".