PROTECTED-UK – Clinical pharmacist interventions in the UK critical care unit: exploration of relationship between intervention, service characteristics and experience level
Bibliographic record
Abstract
PURPOSE: Clinical pharmacist (CP) interventions from the PROTECTED-UK cohort, a multi-site critical care interventions study, were further analysed to assess effects of: time on critical care, number of interventions, CP expertise and days of week, on impact of intervention and ultimately contribution to patient care. METHODS: Intervention data were collected from 21 adult critical care units over 14 days. Interventions could be error, optimisation or consults, and were blind-coded to ensure consistency, prior to bivariate analysis. Pharmacy service demographics were further collated by investigator survey. KEY FINDINGS: Of the 20 758 prescriptions reviewed, 3375 interventions were made (intervention rate 16.1%). CPs spent 3.5 h per day (mean, ±SD 1.7) on direct patient care, reviewed 10.3 patients per day (±SD 4.2) and required 22.5 min (±SD 9.5) per review. Intervention rate had a moderate inverse correlation with the time the pharmacist spent on critical care (P = 0.05; r = 0.4). Optimisation rate had a strong inverse association with total number of prescriptions reviewed per day (P = 0.001; r = 0.7). A consultant CP had a moderate inverse correlation with number of errors identified (P = 0.008; r = 0.6). No correlation existed between the presence of electronic prescribing in critical care and any intervention rate. Few centres provided weekend services, although the intervention rate was significantly higher on weekends than weekdays. CONCLUSIONS: A CP is essential for safe and optimised patient medication therapy; an extended and developed pharmacy service is expected to reduce errors. CP services should be adequately staffed to enable adequate time for prescription review and maximal therapy optimisation.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".