Reducing endoscopic procedure backlog by improving efficiency: a predictive model and machine learning-based scheduling approach
Bibliographic record
Abstract
Background: The COVID-19 pandemic led to a significant decrease in endoscopic procedure volumes, resulting in a backlog of patients awaiting investigation. Our study thus aimed to develop a machine learning-based scheduling tool to improve resource utilization, enhance system efficiency, and increase patient throughput, ultimately reducing procedural delays. Methods: In the first phase, machine learning methods were applied to historical data to predict procedure duration based on patient characteristics and environmental factors. In the second phase, a scheduling module was built using a greedy heuristic and a Mixed Integer Programming (MIP) model to optimize resource utilization. Results: We showed that among the tested models, an XGBoost regression model was selected with a mean absolute error of 5.67 minutes on the test set. The simulation results demonstrated that MIP increased the number of patients scheduled by 5.9% while reducing mean waiting time from 19.5 days to 17.3 days over a waiting list of 1,000 patients, evaluated within a 2-week period (10 working days). Simulations using real patient data showed that the MIP scheduled 8 more patients than the baseline. Numerical results confirmed higher resource utilization rates in adaptive schedules. Conclusions: Our study highlights the potential of a machine learning-based scheduling tool to enhance resource allocation, thus helping address backlogs in endoscopic procedures. Real-world clinical validation is now necessary to substantiate the tool's effectiveness. Future work should prioritize prospective data collection to refine the predictive model and seamlessly integrate the tool into clinical workflows, ensuring its practical utility and success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".