Development of a Hemodialysis Safety Checklist Using a Structured Panel Process
Bibliographic record
Abstract
BACKGROUND: The World Health Organization created a Surgical Safety Checklist with a pause or "time out" to help reduce preventable adverse events and improve communication. A similar tool might improve patient safety and reduce treatment-associated morbidity in the hemodialysis unit. OBJECTIVE: To develop a Hemodialysis Safety Checklist (Hemo Pause) for daily use by nurses and patients. DESIGN: A modified Delphi consensus technique based on the RAND method was used to evaluate and revise the checklist. SETTING: University-affiliated in-center hemodialysis unit. PARTICIPANTS: A multidisciplinary team of physicians, nurses, and administrators developed the initial version of the Hemo Pause Checklist. The evaluation team consisted of 20 registered hemodialysis nurses. MEASUREMENTS: The top 5 hemodialysis safety measures according to hemodialysis nurses. A 75% agreement threshold was required for consensus. METHODS: The structured panel process was iterative, consisting of a literature review to identify safety parameters, individual rating of each parameter by the panel of hemodialysis nurses, an in-person consensus meeting wherein the panel refined the parameters, and a final anonymous survey that assessed panel consensus. RESULTS: The literature review produced 31 patient safety parameters. Individual review by panelists reduced the list to 25 parameters, followed by further reduction to 19 at the in-person consensus meeting. The final round of scoring yielded the following top 5 safety measures: 1) confirmation of patient identity, 2) measurement of pre-dialysis weight, 3) recognition and transcription of new medical orders, 4) confirmation of dialysate composition based on prescription, and 5) measurement of pre-dialysis blood pressure. Revision using human factors principles incorporated the 19 patient safety parameters with greater than or equal to 75% consensus into a final checklist of 17-items. LIMITATIONS: The literature review was not systematic. This was a single-center study, and the panel lacked patient and family representation. CONCLUSIONS: A novel 17-item Hemodialysis Safety Checklist (Hemo Pause) for use by nurses and patients has been developed to standardize the hemodialysis procedure. Further quality improvement efforts are underway to explore the feasibility of using this checklist to reduce adverse events and strengthen the safety culture in the hemodialysis unit.
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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 | 0.222 | 0.246 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".