S1485 Increasing Inpatient Endoscopy Volumes Using the Model for Continuous Improvement
Bibliographic record
Abstract
Introduction: Inpatient endoscopy volumes can affect patient care at many levels in a hospital system. At our academic center, inpatient procedural volume efficiency is 65% (defined as the number of completed procedures compared during the designated in-patient endoscopy time period). As a consequence, excess procedures are being completed on-call or on weekends. Our aim was to increase procedural volume by one (14%) additional procedure completed/day over a 12-month period. Methods: An interprofessional team of Gastroenterology fellows and staff along with endoscopy nurses and managers was created to investigate throughput concerns. Baseline data was collected via direct observation and completion of a time study over 2 months. Subsequently, a process flow diagram was completed. A combination of root cause analysis tools (ie, Ishikawa diagram and Pareto chart) were then utilized to identify areas for improvement. A delay in procedural start time was identified as a strong culprit for reduced efficiency. Potential PDSA (plan-do-study-act) cycles included: early physician handover start time, constructing a standardized patient procedure list, and improving timeliness of patient transfer to the endoscopy suite. Results: Baseline data identified that the first case start time was delayed by 51 min when our actual start time is 08:00 am. Our first PDSA cycle involved a 15-minute earlier physician handover start time. PDSA cycle #1 reduced our mean procedural start time to 08:49 am [UCL: 104 minutes; LCL: −5.4 minutes]. Our second PDSA cycle, involved the standardization of a planned procedural list and mandate for the first procedure to be esophagogastroduodenoscopy (EGD). PDSA cycle #2 reduced start time reduced to 08:29 am [UCL: 69.6 minutes; LCL: −12.4 minutes]. Our third PDSA cycle, involved utilizing the standardized procedural list to pre-emptively organize timely patient transfer to account for delays secondary to hospital portering services, which reduced the mean start time further to 08:22 am [UCL: 52.0 minutes; LCL: −8.0 minutes] (Figure 1). Conclusion: Using the model for continuous improvement we were able to increase procedural volumes by one (14%) per day. The most effective intervention included developing a standardized procedure list and mandating the first case as an EGD minimizing delays due to inadequate or incomplete bowel preparation.Figure 1.: A statistical process control chart (SPC) demonstrating baseline data on endoscopy procedural start time and interventions (PDSA #1, PDSA #2, PDSA #3) that were effective in reducing the delay in procedure start time and resulted in a subsequent increase in endoscopy unit efficiency.
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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 | 0.001 | 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".