566 Improving Bowel Preparation Quality for Inpatient Colonoscopies at a Tertiary Hospital
Bibliographic record
Abstract
INTRODUCTION: Adequate bowel preparation quality is required for appropriate mucosal visualization during colonoscopy. Several factors impede quality including patient, environmental and process factors. When colonoscopies demonstrate poor-quality preparation, it often delays further management and discharge for patients as they often need to return for a second procedure in order to get adequate visualization. This also results in increased costs to the healthcare system. Our aim was to identify the rate of poor-quality bowel preparation at our tertiary care hospital for inpatient colonoscopies and implement interventions to decrease this rate. METHODS: The study was conducted at University Hospital in London, Ontario from March 2018 - March 2019. In the first PDSA cycle we improved an existing order-set such that split- dose bowel preparation would more reliably be ordered and administered. Our second PDSA cycle focused on teaching junior residents how to order bowel preparation for inpatient colonoscopies. PDSA cycle three involved making bowel preparation quality assessment more objective. Lastly, PDSA cycle four wasaimed at improving patient education surrounding the importance of completing bowel preparation. RESULTS: Poor-quality bowel preparation in the six months prior to intervention was 14.0%. After intervention this came down to 8.0%. Similarly our process measure of patients receiving split-dose bowel preparation administration increased from 81.2% to 94.6% during this period. CONCLUSION: Several factors are involved with poor-quality bowel preparation for inpatient colonoscopies. Simple and sustainable interventions can be implemented to improve quality. We are continuing to identify new factors and interventions to further improve this metric.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".