MRI Usage Patterns by Clinical Specialty Across Canada: Geographic and Temporal Trends
Bibliographic record
Abstract
What Is the Issue? Canada’s Drug Agency received a request related to the use of MRI by clinical specialty at medical imaging sites across Canada and how they compare between urban and rural settings. There are limited data on MRI use by clinical specialty across Canada, particularly when comparing urban and rural settings This presents a challenge for health system leaders seeking to make evidence-informed decisions around MRI resource allocation, infrastructure investment, workforce planning, and service delivery. These insights can guide funding, staffing, and equipment allocation decisions. They can also support targeted training programs and promote equitable and effective planning across jurisdictions. What Did We Do? In response, Canada’s Drug Agency leveraged data from the 2022–2023 Canadian Medical Imaging Inventory (CMII) National Survey to conduct an analysis on the uses of MRI by broad clinical specialty. Data were drawn from 56 medical imaging facilities with MRI capacity across 9 provinces. Comparisons were made across provinces, over time, and between rural and urban imaging sites to examine trends in use. Data from previous iterations of the CMII national survey (2017 and 2019–2020) were included for historical comparisons. What Did We Find? Our analysis revealed the following key findings: Neurologic exams accounted for the highest proportion of MRI use nationally, followed by musculoskeletal exams and oncologic exams. Differences were seen in MRI usage patterns by clinical specialty, both between provinces and between sites located in urban and rural settings. Little change was noted in MRI usage rates over 3 iterations of the CMII survey. However, a 6% increase was observed for inflammatory and infectious disease exams in the 2022–2023 survey compared to the 2017 and 2019–2020 It is important to note that the sample’s size and distribution may limit the generalizability of these findings across all regions and practice settings in Canada. What Does This Mean? These findings highlight key differences in MRI use across jurisdictions and urban versus rural imaging centres. Establishing baseline use proportions can help identify trends in MRI use and support resource planning. Understanding how MRI is being used can influence wait time strategies, can help improve resource utilization efficiency and support cost-effective health care technology deployment, and may help improve patient outcomes. Establishing baseline MRI usage rates may also inform the optimal allocation of resources such as funding, staffing, equipment, and training programs. Comparisons between urban and rural areas highlight the differences in regional imaging demands and clinical practices. Tracking usage over time can reveal emerging clinical needs and shifts in demand, enabling more proactive and responsive health care planning.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".