Optimisation of hospital resource use: A rapid review of the literature
Bibliographic record
Abstract
Project System Wide Integration for Transformation (SWIFT) 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 optimisation of resource use in hospitals was carried out to support this programme. In total, 36 articles were considered in detail for inclusion in this rapid review, with many other articles considered briefly from title or abstract alone. Of the 36 articles, 24 (66.7%) were ultimately deemed relevant, and included. The review found reasonable evidence that patient length of stay can be reduced by using: (1) collaborative physician/nurse multidisciplinary care management of medical patients with expedited discharge, and assessment following discharge; (2) perioperative anaesthetic and pain management strategies for primary total hip (THA) and total knee arthroplasty (TKA); (3) the use of specialist nurses across a variety of roles, and team midwives who provide care for pregnant women from the beginning of care to the end of the post-natal period. It also found that there is potential for a reduction in adverse cardiac outcomes in hospitals through: (1) prescribing guideline discharge therapies in acute cardiac care, and (2) remote management of heart failure patients implanted with cardioverter defibrillators. The review also suggested that appointment cancellations or instances of non-attendance can be reduced by: (1) the establishment of pre-operative assessment and consultation clinics; (2) distributed access to scheduling systems; (3) preferentially loading appointments onto high-attendance days; and (4) the use of text messaging or automated phone calls to remind patients about appointments.
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 | 0.016 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".