Feasibility of a hemodialysis safety checklist for nurses and patients: a quality improvement study
Bibliographic record
Abstract
BACKGROUND: Patients with end-stage renal disease are at high risk for medical errors given their comorbidities, polypharmacy and coordination of care with other hospital departments. We previously developed a hemodialysis safety checklist (Hemo Pause) to be jointly completed by nurses and patients. Our objective was to determine the feasibility of using this checklist during every hemodialysis session for 3 months. METHODS: We conducted a single-center, prospective time series study. A convenience sample of 14 nurses and 22 prevalent in-center hemodialysis patients volunteered to participate. All participants were trained in the administration of the Hemo Pause checklist. The primary outcome was completion of the Hemo Pause checklist, which was assessed at weekly intervals. We also measured the acceptability of the Hemo Pause checklist using a local patient safety survey. RESULTS: There were 799 hemodialysis treatments pre-intervention (13 January-5 April 2014) and 757 post-intervention (5 May-26 July 2014). The checklist was completed for 556 of the 757 (73%) treatments. Among the hemodialysis nurses, 93% (13/14) agreed that the checklist was easy to use and 79% (11/14) agreed it should be expanded to other patients. Among the hemodialysis patients, 73% (16/22) agreed that the checklist made them feel safer and should be expanded to other patients. CONCLUSIONS: The Hemo Pause safety checklist was acceptable to both nurses and patients over 3 months. Our next step is to spread this checklist locally and conduct a mixed methods study to determine mechanisms by which its use may improve safety culture and reduce adverse events.
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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 | 0.055 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".