Initiatives for improving delayed discharge from a hospital setting: a scoping review
Bibliographic record
Abstract
OBJECTIVE: The overarching objective of the scoping review was to examine peer reviewed and grey literature for best practices that have been developed, implemented and/or evaluated for delayed discharge involving a hospital setting. Two specific objectives were to review what the delayed discharge initiatives entailed and identify gaps in the literature in order to inform future work. DESIGN: Scoping review. METHODS: Electronic databases and websites of government and healthcare organisations were searched for eligible articles. Articles were required to include an initiative that focused on delayed discharge, involve a hospital setting and be published between 1 January 2004 and 16 August 2019. Data were extracted using Microsoft Excel. Following extraction, a policy framework by Doern and Phidd was adapted to organise the included initiatives into categories: (1) information sharing; (2) tools and guidelines; (3) practice changes; (4) infrastructure and finance and (5) other. RESULTS: Sixty-six articles were included in this review. The majority of initiatives were categorised as practice change (n=36), followed by information sharing (n=19) and tools and guidelines (n=19). Numerous initiatives incorporated multiple categories. The majority of initiatives were implemented by multidisciplinary teams and resulted in improved outcomes such as reduced length of stay and discharge delays. However, the experiences of patients and families were rarely reported. Included initiatives also lacked important contextual information, which is essential for replicating best practices and scaling up. CONCLUSIONS: This scoping review identified a number of initiatives that have been implemented to target delayed discharges. While the majority of initiatives resulted in positive outcomes, delayed discharges remain an international problem. There are significant gaps and limitations in evidence and thus, future work is warranted to develop solutions that have a sustainable impact.
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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 | 0.100 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.030 | 0.030 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".