16 Reducing readmissions from an amputee rehabilitation unit (ARU) to accident and emergency: an MDT approach to service improvement
Bibliographic record
Abstract
Background The ARU is a 12-bed therapy led unit based in London, specialising in post-amputation rehabilitation following hospital discharge. This project focuses on minimising acute readmissions to hospital in the multi-morbid vascular population, aiming to better control diabetes and reduce smoking, risk factors for wound infections and readmissions. Aims To quantify the rate of readmission from ARU to hospital, compared to national data; to identify a primary cause of readmission and subsequently to enact targeted interventions to be re-audited in six months. Methods Data was retrospectively collected from all ARU admissions between 01/06/2023 and 31/12/2023; a total of 57 patients identified using internally recorded admission records and data sourced manually from inpatient notes on the electronic systems. The MDT approach to interventions included: working with the local vascular hub to promote wound care post-operatively, adapt admission criteria to ensure clinical optimisation prior to transfer, embedding health promotion education into therapy and implementing a wound focused admission checklist that includes photography and swabbing of wounds at regular intervals Results Of the 57 ARU patients admitted, many amputations sequalae of uncontrolled diabetes, there were 12 re-admissions between 9 patients. The primary cause of readmissions was wound infections, with 8/12 (66.7%) originating from a stump wound. Of the 8 wound infections, re-admissions were more common in the first 14 days (5/8), those transferred sooner post-operatively (mean 22.6 days vs 39.0 days) and in those with transtibial amputations (83%). Every patient readmitted had a key co-morbidity associated with poor wound healing, 83% were diabetic and 33% smokers. 8.8% of the 12 re-admissions required surgical re-intervention. Conclusion Despite significantly lower readmission, wound infection, and revision surgery rates than expected, the project highlights areas for service improvement, as outlined in methods implemented. This data will be re-audited in 6 months.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".