MRI Usage Patterns by Clinical Specialty Across Canada: Geographic and Temporal Trends
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,012 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».