The financial impact of improved hand hygiene on healthcare-associated infections in the UK
Bibliographic record
Abstract
Background: Though high hand hygiene (HH) levels significantly reduce the risk of healthcare-associated infections (HCAIs), the current cost of HCAIs and the impact of optimal HH practices on HCAIs are poorly defined. The last NHS England financial assessment was in 2009. Methods: The number of HCAIs per bed per year for NHS England were calculated and average costs were attributed using data from three sources; National Audit Office report, a commercially available calculator, and a financial analysis by a specialist paediatric hospital in England. Improved HH compliance for NHS England was based on a sustained rise in compliance rates from 50 to 80% combined with an HCAI reduction of at least 20%. The cost savings based on such improvements were then calculated. Results: In 2020, it is estimated that the number of HCAIs per bed per year ranges from 3.0 to 9.3, with a midpoint of 5.1. The direct costs of HCAI to NHS England were found to lie between £1.6 and £5 billion. Based on a 20% reduction in HCAI rates, this could lead to cost savings of between £322 million and £1 billion per year. Conclusion: Current direct costs of HCAIs consume approximately 1.3% to 4.1% of NHS England’s annual budget. Improving HH compliance among healthcare workers can lead to significant cost savings. There appears to be a strong financial argument for investment into innovative HH compliance technologies that have been historically perceived as too expensive.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".