Novel quality improvement method to reduce cost while improving the quality of patient care: retrospective observational study
Bibliographic record
Abstract
BACKGROUND: Healthcare cost management strategies are limited in number and resource intensive. Budget constraints in the National Health Service Scotland (NHS Scotland) apply pressure on regional health boards to improve efficiency while preserving quality. METHODS: We developed a technical method to assist health systems to reduce operating costs, called continuous value management (CVM). Derived from lean accounting and employing quality improvement (QI) methods, the approach allows for management to reduce or repurpose resources to improve efficiency. The primary outcome measure was the cost per patient admitted to the ward in British pounds (£). INTERVENTIONS: The first step of CVM is developing a standard care model. Teams then track system performance weekly using a tool called the 'box score', and improve performance using QI methods with results displayed on a visual management board. A 29-bed inpatient respiratory ward in a mid-sized hospital in NHS Scotland pilot tested the method. RESULTS: We included 5806 patients between October 2016 and May 2018. During the 18-month pilot, the ward realised a 21.8% reduction in cost per patient admitted to the ward (from an initial average level of £807.70 to £631.50 as a new average applying Shewhart control chart rules, p<0.0001), and agency nursing spend decreased by 30.8%. The ward realised a 28.9% increase in the number of patients admitted to the ward per week. Other quality measures (eg, staff satisfaction) were sustained or improved. CONCLUSION: CVM methods reduced the cost of care while improving quality. Most of the reduction came by way of reduced bank nursing spend. Work is under way to further test CVM and understand leadership behaviours supporting scale-up.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".