Quality Improvement to Address Surgical Burden of Disease at a Large Tertiary Public Hospital in Peru
Bibliographic record
Abstract
BACKGROUND: In resource-limited settings, there is a unique opportunity for using process improvement strategies to address the lack of access to surgical care. By implementing organizational changes in the surgical admission process, we aimed to decrease wait times, increase surgical volume, and improve patient satisfaction for elective general surgery procedures at a public tertiary hospital in Lima, Peru. METHODS: During the first phase of the intervention, Plan-Do-Study-Act (PDSA) cycles were performed to ensure the surgery waitlist included up-to-date clinical information. In the second phase, Lean Six Sigma methodology was used to adapt the admission and scheduling process for elective general surgery patients. After six months, outcomes were compared to baseline data using Wilcoxon rank-sum test. RESULTS: At the conclusion of phase one, 87.0% (488/561) of patients on the new waitlist had all relevant clinical data documented, improved from 13.3% (2/15) for the pre-existing list. Time from admission to discharge for all surgeries improved from 5 to 4 days (p<0.05) after the intervention. Median wait times from admission to operation for elective surgeries were unchanged at 4 days (p=0.076) pre- and post-intervention. There was a trend toward increased weekly elective surgical volume from a median of 9 to 13 cases (p=0.24) and increased patient satisfaction rates for elective surgery from 80.5 to 83.8% (p=0.62), although these were not statistically significant. CONCLUSION: The process for scheduling and admitting elective surgical patients became more efficient after our intervention. Time from admission to discharge for all surgical patients improved significantly. Other measured outcomes improved, though not with statistical significance. Main challenges included gaining buy-in from all participants and disruptions in surgical services from bed shortages.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".