2nd European Conference on Diagnostic Error in Medicine August 30-31, 2018, Bern, Switzerland
Notice bibliographique
Résumé
The process of formulating a working diagnosis in the inpatient setting requires that diagnosticians working in complex and hectic hospital environments gather, filter, integrate, and interpret substantial amounts of information within a short time. Several years ago, the information needed for diagnosis was gathered primarily at the bedside. More recently and with the advance of new technologies, this information is collected through a series of communication exchanges (e.g., pagers, emails) and interactions with the electronic health record (EHR). In this study, we examined challenges and opportunities for improvement in clinician-to-clinician communication and data sharing during the diagnostic process. Methods: We performed a qualitative, multi-method, focused ethnographic study. Data were gathered between January and May 2016 at two affiliated teaching hospitals. Eight inpatient medicine teams (which included attending physicians, senior residents, interns, and medical students) were observed during morning rounds and in the afternoon on call and non-call days. Focus groups and interviews were then conducted with team members to better understand challenges and opportunities for improvement. Unstructured field notes were taken during observations. All focus groups and interviews were recorded and transcribed. Data were analyzed using qualitative content analysis. Results: Observation data showed that physicians faced a data-gathering and communication environment that made integration and interpretation of information for diagnosis challenging. Notably, data flow and communication for each patient was fragmented over time and diagnostic information was pieced together from multiple sources. Pagers were inefficient and did not support dialogue needed for diagnosis. Suggestions for improvement during interviews and focus groups included: 1) replacing pagers with two-way communication technologies; 2) improving EHR design to support diagnosis by increasing data integration while reducing data overload and information fragmentation; 3) identifying more efficient ways to access the EHR during morning rounds and while in patient rooms; 4) increasing face-to-face communication between clinicians. Conclusion: Teaching hospitals are complex environments. The way patient information is shared and communicated among clinicians has changed with the adoption of electronic health records. Physicians are confronted with data overload, frequent interruptions, fragmented information, and little time to think about diagnosis. Although improvement opportunities suggested by front line physicians for patient diagnosis were identified, how best to implement these ideas remains to be determined.
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 | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,020 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».