Patient-centered approaches to targeting incomplete bowel preparations for inpatient colonoscopies
Bibliographic record
Abstract
BACKGROUND: A high-quality colonoscopy bowel prep is vital to completing the procedure. Adequate inpatient bowel preparation has been consistently difficult to achieve because of multiple factors. Incomplete bowel prep can lead to repeated colonoscopies, poor patient experience, increased costs, and prolonged hospitalization. This study aimed to develop patient-centered interventions to optimize bowel prep for inpatients undergoing colonoscopy. METHODS: The Model for Improvement and Donabedian frameworks guided this project. An interdisciplinary team compiled quality improvement tools that identified areas for improvement. Interventions development included a nursing tip sheet for troubleshooting symptoms, a standardized order label and a patient educational placemat. Plan-Do-Study-Act (PDSA) cycles were carried out to test and analyze the effects of the interventions. The project aim was a 30% reduction in incomplete inpatient colonoscopies from poor bowel prep. Process measures included the number of colonoscopy split prep order labels, and placemats used. The balancing measure was the number of repeat colonoscopies. RESULTS: Prior to the intervention, 44% (44/99) of inpatient colonoscopies had poor bowel prep resulting in 10 repeat procedures (10%). Post intervention, 60% (28/47) of the colonoscopies used the standardized label, 66% of physician orders used 2-L split prep, and 80% of patients were provided with the educational placemat. Of the 47 colonoscopies audited post intervention, there was a significant decrease in poor prep (27.7% [13/47], P=0.038) for colonoscopies. The percentage of repeated colonoscopies decreased to 4% (2/47). CONCLUSION: Developing simple and easy-to-use patient-centered interventions can effectively improve colonoscopy preparation for hospitalized patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".