68 A quality improvement project to enhance the recovery pathway following elective caesarean section at St Thomas’ Hospital
Bibliographic record
Abstract
Aims Our project aimed to enhance the recovery pathway for elective caesarean section (ELCS) at St Thomas’ Hospital by reducing the length of stay by 20% by February 2020 (over a course of 5 months). Methods Quantitative and qualitative data (via feedback forms) was sought. The length of stay following ELCS was calculated from theatre operating times and discharge time obtained via BadgerNet. The baseline data was collected for 4 weeks in September 2019 and the project ran until February 2020. ELCS journeys were shadowed, gaining exposure to patient and staff experiences. This revealed that the ‘to take out medication’ (TTO) pathway was a major contributor to discharge delays and therefore interventions were devised forming three Plan-Do-Study-Act (PDSA) cycles. The first intervention and PDSA cycle consisted of poster and email reminders for junior doctors to increase TTO prescription efficiency. The second saw maternity support workers (MSWs) collect TTO medication from the central pharmacy twice daily at 2:30pm and 4:30pm. An additional collection time of 6pm was added for the third and final intervention. Results The baseline median length of stay of 51.2 hours decreased to 47.4 hours after all interventions. We did not reach our goal of less than 41 hours (20% reduction from baseline). The results, however, are very promising with 59.8% of patients (137 of 229 patients) staying a duration less than our baseline median hours of 51.2. Conclusions Our activities altered the logistics of TTO delivery leading to earlier discharge and are easily reproducible and could be beneficial throughout the hospital setting. The benefits include more positive experiences for women, improved recovery, reduced departmental costs and bed demands. Identifying problems and collating feedback from stakeholders was essential to our success and should be undertaken by those in management before making changes.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".