Implementation of a checklist before colonoscopy: a quality improvement initiative
Bibliographic record
Abstract
Abstract Background and study aims Checklists can prevent errors and have a positive impact on patient morbidity and mortality in different surgical settings, and possibly also in gastrointestinal endoscopy. The aims of this study were to reinforce commitment in safety culture and better communication among team members in endoscopy, and to prove the feasibility of successful checklist adoption before colonoscopy. Patients and methods The study involved a pre – post quality improvement intervention involving all consecutive patients undergoing a colonoscopy at a single academic endoscopy unit. The first part of the study was a retrospective audit, carried out over a 3-month period (July to September 2016). A checklist developed through a formal validation process was implemented during the intervention period (October to December 2016). Primary outcomes were changes in patient and team satisfaction after the quality improvement intervention, using validated 5-point scale questionnaires. Secondary outcomes included successful procedure completion rates and safety outcomes. Results During the baseline and comparative intervention period, 1317 and 1141 colonoscopies, respectively, were performed. Overall, checklists were fully completed by nurses and physicians for 791 patients (69.3 %). Mean overall patient satisfaction was high at baseline and did not differ following the quality improvement intervention (4.66 vs. 4.63; P = 0.5). Perception of team communication and teamwork was improved after checklist implementation. Comparative analyses of per-procedure and safety outcomes did not differ between the pre- and post-checklist implementation. Conclusion Adoption of an endoscopy checklist before colonoscopy is feasible, and significantly increases perception of team communication and teamwork. Additional studies are needed to assess the generalizability of these results to complex endoscopic procedures and to characterize any improvement in patient safety outcomes.
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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 | 0.042 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".