Improvement in the diagnosis and practices of emergency healthcare providers for heat emergencies after HEAT (heat emergency awareness & treatment) an educational intervention: a multicenter quasi-experimental study
Notice bibliographique
Résumé
BACKGROUND: The incidence of heat emergencies, including heat stroke and heat exhaustion, have increased recently due to climate change. This has affected global health and has become an issue of consideration for human health and well-being. Due to overlapping clinical manifestations with other diseases, and most of these emergencies occurring in an elderly patient, patients with a comorbid condition, or patients on poly medicine, diagnosing and managing them in the emergency department can be challenging. This study assessed whether an educational training on heat emergencies, defined as heat intervention in our study, could improve the diagnosis and management practices of ED healthcare providers in the ED setting. METHODS: A quasi-experimental study was conducted in the EDs of four hospitals in Karachi, Pakistan. Eight thousand two hundred three (8203) patients were enrolled at the ED triage based on symptoms of heat emergencies. The pre-intervention data were collected from May to July 2017, while the post-intervention data were collected from May to July 2018. The HEAT intervention, consisting of educational activities targeted toward ED healthcare providers, was implemented in April 2018. The outcomes assessed were improved recognition-measured by increased frequency of diagnosing heat emergencies and improved management-measured by increased temperature monitoring, external cooling measures, and intravenous fluids in the post-intervention period compared to pre-intervention. RESULTS: Four thousand one hundred eighty-two patients were enrolled in the pre-intervention period and 4022 in the post-intervention period, with at least one symptom falling under the criteria for diagnosis of a heat emergency. The diagnosis rate improved from 3% (n = 125/4181) to 7.5% (n = 7.5/4022) (p-value < 0.001), temperature monitoring improved from 0.9% (n = 41/4181) to 13% (n = 496/4022) (p-value < 0.001) and external cooling measure (water sponging) improved from 1.3% (n = 89/4181) to 3.4% (n = 210/4022) (p-value < 0.001) after the administration of the HEAT intervention. CONCLUSION: The HEAT intervention in our study improved ED healthcare providers' approach towards diagnosis and management practices of patients presenting with health emergencies (heat stroke or heat exhaustion) in the ED setting. The findings support the case of training ED healthcare providers to address emerging health issues due to rising temperatures/ climate change using standardized treatment algorithms.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 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 ».