Leveraging supply chain infrastructure to advance patient safety in community health-care settings
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the opportunity for supply chain processes and infrastructure to reduce the risk of medical error and create traceability of adverse events in community care settings. Patient safety has become an important area of focus over the past few decades, with medical error now accounting for the third most common cause of death in Canada and the USA. The majority of patient safety studies to date have focused specifically on safety in hospital settings; however, deaths and harm experienced by patients in the community (home care, long-term care, complex care and rehabilitation settings) are not well understood. Design/methodology/approach This paper discusses the evidence that adverse events occur at similar, if not more, frequent rates in community care settings. Findings The authors propose that above and beyond current efforts to increase awareness and promote a "safety culture" in health-care settings, system infrastructure should be designed in a way that enables clinicians to provide the safest care possible. There is currently no line of sight across the health-care continuum. The authors suggest that improving system infrastructure would reduce the occurrence of adverse events. Originality/value Such visibility across the continuum of care holds the potential to transform health-care in Canada from a fragmented system, where information is inadequately captured and transferred from provider to provider, to a system that provides complete, accurate and up-to-date information regarding patient care, procedures, medications and outcomes so as to provide the best and safest care possible. System visibility achieves quality and safe care, which is transparent and accountable and achieves value for patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".