Reconciling Technology and People: Quality of Care During End-of-Rotation Transfers
Notice bibliographique
Résumé
To the Editor: Beginning my inpatient internal medicine rotation, I struggled with my first case: A 75-year-old male, nonverbal and hospitalized for five weeks. What could I do for him? What was the plan moving forward? The daily brief notes concluded, “Stable, continue the same.” What was I to make of this? Electronic tools provided a summary of his hospital stay, but I wished the resident who had been in charge before I began my rotation was with me that morning. So, at the start of my internal medicine rotation, technology abounded. But it was not enough. Later during the rotation, our team split into two to take turns at taking time off for the holidays. Again, written handoffs and technology seemed insufficient to facilitate effective transfers of care. Further, end-of-rotation transfers disrupt continuity of care, prolong hospital stays, and increase mortality.1 Yet, their effect on care quality appears underappreciated. Despite doctors’ enthusiasm for technology, such tools might limit communication of thoughts and feelings, reducing insight into patients’ health status. Instead, person-to-person transfer is reflective, including unwritten thoughts shared verbally. I have noticed that barriers to transferring care at rotation’s end include not only insufficient person-to-person communication but also lack of protected time, inability to clarify notes, insufficient training among residents and students, a mismatch between admissions and staff schedules, and inconsistent transfer methods. The time required to understand complex cases often delays care and contributes to management errors—despite the availability of electronic tools. New teams’ expectations might not match patients’ expectations, so care negotiations start from “scratch,” delaying medical and other arrangements and prolonging hospital stay. The lack of information and experience at transfers can negatively affect discharge decisions.2 Students, residents, and fellows play important roles during transfers of care; regrettably, actions are too often hasty, and finding time to prepare for these transfers can be challenging. Transfer of care requires more than handoff training and electronic platforms for notes. Starting rotations one day early, allowing trainees to take transfers directly from departing teams, and/or facilitating check-ins by phone or video conferencing at the end of the day could all improve transfers. Voice memo applications in hospital computers could overcome barriers at the end of rotations, supporting reflective practice. Despite the wonders of technology, both reflection and personal interaction during transfer of care in a complex, vulnerable environment are essential. Medical trainees must be able to prioritize care coordination with one another. Person-to-person communication will help close the gap between technology and its physician users, thus offering patients the best of both. Acknowledgments: The author would like to thank Dr. Peter Nugus for his support and encouragement. Diana Ramos Torres, MD, MAHPEPostgraduate year 2 family medicine resident and PhD student, Family Medicine–Medical Education, Department of Family Medicine, McGill University, Montreal, Quebec, Canada; [email protected] First published online February 13, 2018
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,083 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,012 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».