Effectiveness of a new short-stay unit for elective low complexity patients’ admissions to improve patient flow
Bibliographic record
Abstract
Background/Objective: Specialized care in an acute hospital is the highest resource consuming type of health care. Performance improvement in health care should look for a better application of medical knowledge and resource consuming at the same time as managers locally redesigned processes and organizations in order to be more cost-effective at healthcare services delivery. The aim of this study was to demonstrate the effectiveness of creating a short-stay unit for elective patients as an alternative to the elective patients being admitted in an acute ward. Methods: We included all elective patients admitted in a university hospital between the 1st of January and the 31st of December 2007, as well as those admitted during the same period of 2009, after two hospital wards transformation into a short-stay unit. We used the Hospital General Database for collecting information on years 2007 and 2009. Main key performance indicators were length of stay, pre-surgery length of stay, rotation rate, discharge planning rate and cost. For statistical bivariate analysis, we used a Chi-squared for linear trend for qualitative variables and a T-test and a Wilcoxon signed ranks test and a Mann-Whitney test for non-normal continuous variables. Significance at p < .05 was assumed throughout. Results: We included 10,678 patients, 4,423 during 2007 and 6,255 during 2009. Mean length of stay was 4.3 days (IC 95%: 4.09-4.51) in 2007 and 2.8 days (IC 95%: 2.61-3.01) in 2009 (p < .05). Pre-surgery length of stay was reduced from 0.5 days (IC 95%: 0.44-0.56) in 2007 to 0.2 days (IC 95%: 0.17-0.23) in 2009 (p < .05). The rotation rate was of 92 patients/bed in 2007 and of 126 patients/bed in 2009 (p < .05). The median number of planned discharges grew from 43.05% in 2007 to 86.01% in 2009. Closing two hospital wards at weekends has generated savings of €805,376.32 through the reduction of 22 nurse employees. Conclusions: In conclusion, this approach to hospital bed management in a tertiary hospital has proven both possible and efficient. The creation of short-stay units for elective patient admissions allowed an increase in productivity per hospital bed due to a higher rotation rate and a reduction in human resources costs due to closing hospital wards at weekends.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".