Bibliographic record
Abstract
What Is The Issue? Canada’s Drug Agency (CDA-AMC) received a request regarding the number of CT exams that can be performed per scanner per hour across the country. The most recent estimates for CT exam rates are outdated and based on aggregated data and older CT technology, making direct current comparisons challenging. This limits the ability to assess current exam throughput and operational efficiencies, which are essential for addressing wait times and optimizing resource utilization. What Did We Do? CDA-AMC leveraged data from the 2022–2023 Canadian Medical Imaging Inventory National Survey. Of the 394 sites with CT units across Canada, 173 sites provided sufficient data to estimate hourly CT exam throughput and examine how factors such as unit specifications (including equipment age and CT slice count), staffing levels, and the number of CT units per site influence throughput. What Did We Find? The national average CT exam throughput in 2022–2023 was 2.34 exams per scanner per hour, with rates ranging from 1.89 to 3.43 across jurisdictions. Sites with newer CT units (less than 5 years old) and higher staffing levels (measured by full-time medical radiation technologist positions) generally had higher throughputs, indicating that technological advances and sufficient staffing support better operational efficiency. Sites with 3 or more CT units tended to have lower per-unit exam throughputs, possibly due to coordination challenges or allocation of units for specialized use. Higher-slice CT units (e.g., 256 or more slices) had higher throughputs, but this was based on a smaller sample. This indicates that advanced slice technology may enhance throughput, but exam complexity could also reduce the overall number of exams per hour. What Does This Mean? These findings provide insights into how CT exam throughput can be optimized and help guide decision-making in several ways: The data confirm that operational efficiency is not solely dependent on the number of CT units, but also on how well resources are utilized, including equipment maintenance, staffing, and scheduling. Addressing inefficient workflows, optimizing staffing levels, and ensuring CT units are used effectively can increase the number of hourly exams. The analysis of CT unit age and exam throughput suggests that updating technology or properly maintaining older units may improve productivity. Given the limitations on resources and staffing, benchmark values for CT exam throughput can assist with adopting different workflow management strategies and identifying technology adaptations that can reduce wait times. Variations in exam throughput across jurisdictions and sites indicates that solutions must be tailored to local contexts. Decision-makers may wish to consider jurisdiction-specific or site-specific challenges when considering strategies to reduce wait times and enhance efficiency. Increasing exam throughput should be balanced with ensuring exams are of high quality and meet patients’ clinical needs.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".