Development of medical checklists for improved quality of patient care
Bibliographic record
Abstract
BACKGROUND: Checklists are used in both medical and non-medical industries as cognitive aids to guide users through accurate task completion. Their development requires a systematic and comprehensive approach, particularly when implemented in high intensity fields such as medicine. OBJECTIVE: A narrative review of the literature was conducted to outline the methodology to designing and implementing clear and effective medical checklists. METHODS: We systematically searched for relevant English-language medical and non-medical literature both to describe where checklists have been demonstrated to improve delivery of care and also, how to develop valid checklists. RESULTS: The MEDLINE search yielded 8303 citations of which 1042 abstracts were reviewed. On the basis of criteria for inclusion and subsequent full-manuscript review, 178 sources, including 17 non-medical publications, were included in the narrative review. This information was further supplemented by expert opinion in the area of checklist development and implementation. A small number of strategies for designing effective checklists were referenced in the literature, including utilization of pre-published guidelines, formation of expert panels and repeat pilot-testing of preliminary checklists. CONCLUSION: Despite currently available evidence, a highly effective, standardized methodology for the development and design of medical-specific checklists has not previously been developed and validated, which has likely contributed to their inconsistent use in several key fields of medicine, despite evidence of their fundamental role in error management.
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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 | 0.050 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".