Notice bibliographique
Résumé
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 The most recent estimates for CT exam rates are outdated and based on aggregated data and older CT technology, making direct current comparisons challenging.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.
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 | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 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 ».