Quality Improvement: Supporting a hospital in difficulty: experience of a ‘buddying’ agreement to implement a new medical pathway
Bibliographic record
Abstract
Increased NHS regulation has identified many healthcare organisations with operational and/or financial difficulties. Although the causes are often complex, most cases are effectively managed internally with limited input from external agencies. How best to support the few organisations needing additional support has not been established. 'Buddying', in which senior clinical and managerial teams from a well performing organisation work with colleagues from an organisation in difficulty has been proposed as a potential solution. Previous reports suggest that these partnerships are generally valued by the organisation in difficulty but there is a paucity of measured operational benefit. In this article we present our experience of a 'buddying agreement' and its impact on the introduction of a new 'whole system' medical pathway (ie rotas, staffing, process) at an organisation in difficulty. We describe the process, problems, effect on operational performance, staff survey feedback six months post-implementation and the lessons learned. Factors critical to success were good communication; clear responsibilities, common values and strong governance; incorporation into an effective local improvement programme; targeting of specific issues; ability to influence people and foster relationships; adequate 'manpower' and gradual transition to local 'ownership'.
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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 | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".