Notice bibliographique
Résumé
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 Increases in MRI units since 2005 and technological advances make these estimates outdated, limiting the evaluation of current exam throughput.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.
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 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».