Coordination of care in hospitals: A rapid review of the literature
Bibliographic record
Abstract
PProject SWIFT (System Wide Integration for Transformation) is a programme of work supported by developments in technology, that aims to improve the health of people in Counties Manukau through initiatives focused on community-based care and improving hospital systems. A “rapid review” of literature focussing on coordination of care in hospitals was carried out to support this project. Rapid review is a literature review methodology that is “streamlined” by limiting: the number of databases searched, the types of study design included, the languages that articles are written in, the dates when articles were written, and the level of inclusion of “grey” literature. In total, 30 articles were considered in detail for inclusion in this rapid review, with many other articles considered briefly from title or abstract alone. Of the 30 articles, 12 (40%) were ultimately deemed relevant, and included. In total, 112 unique articles contributed to the literature review, if all of the articles considered by three systematic reviews that contributed to this rapid review are included. The review found reasonable evidence that implementation of patient care delivery models focussing on coordination of care and efficiency can contribute to reductions in length of stay for hospital patients. In addition, reasonable evidence was found indicating that that the use of preoperative briefings and surgical safety checklists by operating teams can improve patient safety outcomes. However, it was not possible to draw firm conclusions from many of the other articles that were reviewed: these tended not to describe measurable improvements to patient outcomes or efficiency, and instead focussed on results that were process rather than outcome oriented, subjective, reported improvements that were not compared against any other measure, or were non-significant.
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 | 0.040 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.043 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".