Closing the safety loop: evaluation of the National Patient Safety Agency's guidance regarding wristband identification of hospital inpatients
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Wristbands are essential for accurate patient identification. Some evidence suggests that missing wristbands is not an infrequent occurrence in acute hospitals. The National Patient Safety Agency (NPSA) has developed guidance on patient identification for hospitals in England and Wales. Here we report an evaluation of the uptake of the guidance. METHOD: The evaluation was designed as a 'pre-post' intervention survey. Fifty hospitals (response rate 67%) responded to the 'pre-guidance' part and 40 hospitals (response rate 43%) responded to the 'post-guidance' part. RESULTS: The majority of the hospitals use wristbands to identify inpatients. Fifty-eight per cent of the hospitals in the 'pre-guidance' survey and 50% of them in the 'post-guidance' survey reported not having a patient identification policy before receiving the guidance. Only one hospital reported not having developed such a policy in the 'post-guidance' survey. Ninety-eight per cent of the hospitals reported that their policies are consistent with the guidance. Relevant training to staff is provided in about a quarter of the organizations, both before and after the guidance. Problems in implementing the guidance were reported by 23% of the hospitals, and included difficulties with staff or patient attitudes, or with the guidance itself, or difficulty to identify a lead staff member. CONCLUSION: Overall, implementation of NPSA guidance regarding inpatient identification was satisfactory. The reported problems should be taken into account, as they likely apply to a range of patient safety interventions. Limitations of evaluating intervention uptake, rather than efficacy, and relying on self-report are discussed.
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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 | 0.113 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".