Bibliographic record
Abstract
What Is The Issue? Canada’s Drug Agency (CDA-AMC) received a request related to the number of hourly MRI exams that can be performed per unit across the country. A recent CDA-AMC report examined hourly MRI exams performed at a site, but did not examine the hourly exam throughput of an individual MRI unit. The last estimates of individual MRI unit exam throughput were from 2005. Increases in MRI units since 2005 and technological advances make these estimates outdated, limiting the evaluation of current exam throughput. What Did We Do? CDA-AMC leveraged data from the 2022–2023 Canadian Medical Imaging Inventory National Survey, including unit specifications and staffing levels. Overall, 111 of 239 sites with MRI units possessed sufficient data to estimate hourly exam throughput. Analyzing variables and potential confounders allowed for a deeper exploration of factors affecting hourly exam throughput per unit. What Did We Find? The national average MRI exam throughput in 2022–2023 was 1.25 exams per unit per hour, with rates ranging from 0.89 to 1.68 across jurisdictions. Sites with MRI units that were 5 to 9 years old and sites with higher staffing levels generally had higher exam throughputs, indicating that technological advances and sufficient staffing supports increased exam volumes. MRI units with a field strength of 1.5 tesla (T) had higher exam throughputs than units with 3.0 T. While higher strength MRI units are more powerful, they are likely reserved for complex exams, reducing hourly exam throughput. Fixed MRI units had higher throughput compared to mobile units. However, expanding imaging access to underserved rural and remote communities is the primary reason for procuring mobile units rather than high exam throughputs. Sites with more units on site tended toward lower per-unit exam throughput, possibly due to challenges such as scheduling inefficiencies, interunit coordination, or allocation of units for specialized studies, diluting overall throughput despite greater capacity. What Does This Mean? These findings provide valuable insights into how MRI exam throughput can be optimized and help guide decision-making in several ways: Exam throughput is not solely dependent on the number of MRI units. Efficient resource use, including equipment maintenance, staffing, and scheduling, can optimize workflows and staffing levels, ensuring that units are effectively used while increasing hourly exam throughput. The analysis of unit age and associated exam throughput suggests older units may be used less or reserved for simpler exams. Higher staffing levels were associated with increased exam throughput, indicating sufficient staffing supports better operational efficiency. Variation in exam throughputs across jurisdictions and sites demonstrates that future solutions must take jurisdiction or site-specific contexts into consideration when trying to reduce wait times and enhance throughput efficiencies. Increasing exam throughput should be balanced with maintaining high-quality, patient-centred care.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".