Simulation Analysis of Patient Wait Times for Computed Tomography and Magnetic Resonance Imaging considering COVID-19 Impact
Bibliographic record
Abstract
Due to world-wide increasing population and life expectancy, the volume of chronic illnesses and number of patients admitted into hospitals is growing. Hospital services can be extremely important and expensive—particularly for Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) diagnostic imaging. Ontario hospitals follow a public healthcare system with four patient priority levels and wait time targets for each of the four priority levels. Along with increasing needs for diagnostic imaging services, COVID-19 was declared a pandemic virus on March 11th, 2020, by the World Health Organization (WHO), which was followed by the Premier of Ontario declaring a state of emergency. Through this time, hospitals were required to decrease services which were considered non-essential or non-life threatening to reserve volume for pandemic-affected patients and minimize patient flow through hospitals. The impact of COVID-19 took a toll on hospitals and their scheduling methods. This thesis focuses on completing a data analysis of an Ontario hospital’s diagnostic imaging for CT and MRI scans to develop a simulation model to determine potential scheduling formats and show how current and prospective future states of scheduling can be implemented through the impact of COVID-19. There are a small number of available simulation models which provide information on hospital-specific patient scheduling implementation for diagnostic imaging. The data analysis is completed by using hospital data, publicly available data from Health Quality Ontario (HQO), and the county’s local health unit. Results of the models will provide insight to improvements in the system to predict outpatient wait times through the COVID-19 pandemic. Wait time predictions are needed to help provide better outcome data for patients and the length of time they should be expected to wait.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".