Adoption of the National Early Warning Score: a survey of hospital trusts in England, Northern Ireland and Wales, Canadian Association for Health Services and Policy Research
Bibliographic record
Abstract
Objectives: The primary objective was to elicit the uptake of a standardised vital signs early warning score - National Early Warning Score (NEWS) in hospitals in England, Wales and Northern Ireland. In 2012, a Royal College of Physicians’ taskforce developed \nApproach: A short survey was sent to 223 hospitals in July 2014. Hospitals were members of a regional critical care network and had an adult general critical care unit. The hospitals were contacted using the Freedom of Information Act (2000), an act of the United Kingdom parliament that creates a public "right of access" to information held by public authorities. Hospitals were asked if they used NEWS or had plans to adopt it, if they used electronic health records and a computerized vital signs monitoring system in their non-critical care wards. Data received from 217 of the 223 hospitals were analysed. \nResults: 27% of hospitals have some form of electronic health record system in their non-critical care wards and 20% of hospitals use computerized methods to record vital signs. All but one hospital uses a multiple parameter early warning score. Over half (55.5%) of hospitals use NEWS and 17% had plans to adopt it. Some hospitals wished to use it as part of an electronic health record system rollout planned for later in the financial year. Half of the hospitals which had no plans to adopt NEWS (24 of 44) gave explicit reasons as to why with the number one reason being that they already used a similar score. Absence of a parameter used in other scores (urine output) was also an issue for non-adopters of NEWS. \nConclusions: The results suggest that there is a steadily increasing level of acceptance of NEWS. The increased use of electronic health records appears to have helped some hospitals to adopt NEWS. A small number of hospitals disclosed that they had adapted NEWS which is a threat to the standardisation intended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".