Factors That Influence the Efficiency of an Endoscopy Unit: A Patient Flow Analysis
Bibliographic record
Abstract
Introduction: With increasing demand for endoscopic procedures in the context of limited resources, it is imperative to identify factors that can be optimized to influence efficiency of the endoscopy unit. Methods: A prospective study from December 2013 to March 2014 was undertaken in the endoscopy unit at the Hotel-Dieu Hospital, Kingston, Ontario. Time elapsed for all components from patient registration to exit from the endoscopy unit was recorded. The data was collected in three components: individual endoscopy room utilization, pre-procedure room, and overall endoscopy unit room usage. Mean times were analyzed to identify if maximal time consumption was attributed to patient related, endoscopist related, equipment or process related causes. Results: Data were collected for 137 procedures in the endoscopy room, 139 procedures in the preprocedure room, and 143 procedures for overall room usage. The mean time for patient registration was 39.22 minutes ahead (95% CI: -44.76 to -33.68) of their scheduled starting time. The mean time spent in the pre-procedure room was 50.15 minutes (95% CI: 45.84 - 54.47). The mean time delay from the scheduled start time to the actual time the patient was transferred into the endoscopy room was 18.51 minutes (95% CI: 13.44 - 23.58). Whilst, the mean time delay between scheduled starting time to that of endoscopist arrival into the endoscopy suite was 27.05 minutes (95% CI: 20.93 - 33.18). Overall, the mean time spent by the patient in the endoscopy room was found to be 27.15 minutes (95% CI: 24.60 - 29.69) for an EGD, 57.95 minutes (95% CI: 50.38 - 65.53) for a colonoscopy, 71.29 minutes (95% CI: 49.36 - 93.22) for a double procedure, and 29.15 minutes (95% CI: 16.16 - 42.13) for a flexible sigmoidoscopy. The average room turnover time was found to be 7.42 minutes (95% CI: 6.85 - 8.00). Conclusion: There is limited literature on the range of efficiencies or validated methodology for evaluating the endoscopy unit. Nevertheless these findings are consistent with the recognition that individual units have unique operational characteristics and that identifying bottlenecks can lead to optimization of resources appropriately. The patient arrival to the endoscopy unit was ahead of their scheduled registration time. However their entry into the endoscopy suite was much delayed, independent of further delay contributed by endoscopist unavailability. This suggests that delays cannot be attributed to patient related causes and are either endoscopist or process related. Endoscopy efficiency to improve patient throughput is imperative for quality of care. Hence, the next phase underway entails staff interviews to further characterize the impediments and facilitate implementation of targeted quality improvement initiatives.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".