Predictors of Prehospital On-Scene Time in an Australian Emergency Retrieval Service
Notice bibliographique
Résumé
INTRODUCTION: Prehospital physicians balance the need to stabilize patients prior to transport, minimizing the delay to transport patients to the appropriate level of care. Literature has focused on which interventions should be performed in the prehospital environment, with airway management, specifically prehospital intubation (PHI), being a commonly discussed topic. However, few studies have sought additional factors which influence scene time or quantify the impact of mission characteristics or therapeutic interventions on scene time.Hypothesis/Problem:The goal of this study was to identify specific interventions, patient demographics, or mission characteristics that increase scene time and quantify their impact on scene time. METHODS: A retrospective, database model-building study was performed using the prehospital mission database of South Australian Ambulance Service (SAAS; Adelaide, South Australia) MedSTAR retrieval service from January 1, 2015 through August 31, 2016. Mission variables, including patient age, weight, gender, retrieval platform, physician type, PHI, arterial line placement, central line placement, and finger thoracostomy, were assessed for predictors of scene time. RESULTS: A total of 506 missions were included in this study. Average prehospital scene time was 34 (SD = 21) minutes. Four mission variables significantly increased scene time: patient age, rotary wing transport, PHI, and arterial line placement increased scene time by 0.09 (SD = 0.08) minutes, 13.6 (SD = 3.2) minutes, 11.6 (SD = 3.8) minutes, and 34.4 (SD = 8.4) minutes, respectively. CONCLUSION: This study identifies two mission characteristics, patient age and rotary wing transport, and two interventions, PHI and arterial line placement, which significantly increase scene time. Elderly patients are medically complex and more severely injured than younger patients, thus, may require more time to stabilize on-scene. Inherent in rotary wing operations is the time to prepare for the flight, which is shorter during ground transport. The time required to safely execute a PHI is similar to that in the literature and has remained constant over the past two years; arterial line placement took longer than envisioned. The SAAS MedSTAR has changed its clinical practice guidelines for prehospital interventions based on this study's results. Retrieval services should similarly assess the necessity and efficiency of interventions to optimize scene time, knowing that the time required to safely execute an intervention may reach a minimum duration. Defining the scene time enables mission planning, team training, and audit review with the aim of improved patient care.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».