A case study: Applying quality improvement methods to reduce pre-operative length of stay in a resource-constrained setting in Rwanda
Bibliographic record
Abstract
Objective: While several studies have focused on improving the quality of surgery, less attention has been paid to reducing pre-operative delays in care. We undertook a hospital quality improvement (QI) effort to reduce pre-operative delays in a teaching hospital in Rwanda. Without a coordinated admission schedule, many surgical patients arriving at the hospital for admissions were turned away because of unavailable beds. For those admitted for surgery, the pre-operative waits were long.Methods: A pre- and post-intervention study was conducted to examine the impact of a QI effort on two metrics: 1) pre-operative length-of-stay (LOS) for elective surgical patients, and 2) the number of elective surgical patients who were turned away on the scheduled admission date. Intervention: A multi-disciplinary work group utilized a Strategic Problem Solving Approach and implemented a centralized patient wait list and new schedule process utilizing the existing resources available at the hospital.Results: The percentage of elective surgical patients with a pre-operative LOS of more than two days was significantly lower in the post-intervention compared with the pre-intervention period (80% versus 26.8%, p-value < .001). The percentage of scheduled patients who were turned away due unavailable inpatient beds significantly decreased from 63.4% to 5.3%, p-value < .001.Conclusions: By following a methodical strategic problem solving approach, the pre-operative LOS was reduced, elective surgical patients turned away due to unavailable beds was decreased at very low financial cost.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".