Comparing Methodologies for Evaluating Emergency Medical Services Ground Transport Access to Time‐critical Emergency Services: A Case Study Using Trauma Center Care
Notice bibliographique
Résumé
OBJECTIVES: The regionalization of medical services has resulted in improved outcomes and greater compliance with existing guidelines. For certain "time-critical" conditions intimately associated with emergency medicine, early intervention has demonstrated mortality benefits. For these conditions, then, appropriate triage within a regionalized system at first diagnosis is paramount, ideally occurring in the field by emergency medical services (EMS) personnel. Therefore, EMS ground transport access is an important metric in the ongoing evaluation of a regionalized care system for time-critical emergency services. To our knowledge, no studies have demonstrated how methodologies for calculating EMS ground transport access differ in their estimates of access over the same study area for the same resource. This study uses two methodologies to calculate EMS ground transport access to trauma center care in a single study area to explore their manifestations and critically evaluate the differences between the methodologies. METHODS: Two methodologies were compared in their estimations of EMS ground transport access to trauma center care: a routing methodology (RM) and an as-the-crow-flies methodology (ACFM). These methodologies were adaptations of the only two methodologies that had been previously used in the literature to calculate EMS ground transport access to time-critical emergency services across the United States. The RM and ACFM were applied to the nine Level I and Level II trauma centers within the province of Ontario by creating trauma center catchment areas at 30, 45, 60, and 120 minutes and calculating the population and area encompassed by the catchments. Because the methodologies were identical for measuring air access, this study looks specifically at EMS ground transport access. RESULTS: Catchments for the province were created for each methodology at each time interval, and their populations and areas were significantly different at all time periods. Specifically, the RM calculated significantly larger populations at every time interval while the ACFM calculated larger catchment area sizes. This trend is counterintuitive (i.e., larger catchment should mean higher populations), and it was found to be most disparate at the shortest time intervals (under 60 minutes). Through critical evaluation of the differences, the authors elucidated that the ACFM could calculate road access in areas with no roads and overestimates access in low-density areas compared to the RM, potentially affecting delivery of care decisions. CONCLUSIONS: Based on these results, the authors believe that future methodologies for calculating EMS ground transport access must incorporate a continuous and valid route through the road network as well as use travel speeds appropriate to the road segments traveled; alternatively, we feel that variation in methods for calculating road distances would have little effect on realized access. Overall, as more complex models for calculating EMS ground transport access become used, there needs to be a standard methodology to improve and to compare it to. Based on these findings, the authors believe that this should be the RM.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 tête enseignante, 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 ».