Understanding interprofessional communication: a content analysis of email communications between doctors and nurses
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Notice bibliographique
Résumé
BACKGROUND: Clinical communication is recognized as a major source of errors in hospitals. The lack of documentation of communication, especially among verbal interactions, often creates hindrances and impedes improvement efforts. By providing smartphones to residents and encouraging nurses to communicate with residents by email shifted much of the communication to emails which permitted analysis of content. OBJECTIVE: Description on the interprofessional email communication between doctors and nurses occurring on the general internal medicine wards at two academic hospitals. DESIGN: A prospective analysis of email communications between doctors and nurses. SETTING: 34 out of the 67 residents who were on the general medicine clinical teaching units consented to allow analysis of their emails over a 6 month period. MAIN MEASURES: Statistical tabulations were performed on the volume and frequency of communications as well the response time of messages. Two physicians coded the content of randomly selected emails for urgency, emotion, language, type of interaction, and subject content. KEY RESULTS: A total of 13,717 emails were available for analysis. Among the emails from nurses, 39.1% were requests for a call back, 18.9% were requests for a response by email and the remaining 42.0% indicated no response was required from physicians. For the messages requesting a response by email, only 50% received an email response. Email responses had a median response time of 2.3 minutes. Content analysis revealed that messages were predominantly non-urgent. The two most frequent purposes for communications were to convey information (91%) and to request action by the physician (36%). CONCLUSIONS: A smartphone-based email system facilitated the description and content analysis of a large amount of email communication between physicians and nurses. Our findings provide a picture of the communication between physicians, nurses and other healthcare professionals. This work may help inform the further development of information and communications technology that can improve clinical communication.
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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 | 0,000 |
| 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) | 0,000 | 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 découle