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Enregistrement W2473377956 · doi:10.1182/blood.v124.21.6000.6000

Pediatric Oncology Clinic Care Model: How to Care for Patients to Achieve Better Continuity of Care in a Medium Sized Program

2014· article· en· W2473377956 sur OpenAlexaff
Donna Johnston, Jacqueline Halton, Mylène Bassal, Robert J. Klaassen, Karen Mandel, Raveena Ramphal, Ewurabena Simpson, Li Peckan

Notice bibliographique

RevueBlood · 2014
Typearticle
Langueen
DomaineMedicine
ThématiqueChildhood Cancer Survivors' Quality of Life
Établissements canadiensChildren's Hospital of Eastern Ontario
Organismes subventionnairesnon disponible
Mots-clésMedicineInternal medicineOutpatient clinicAmbulatory careOncologyFamily medicineEmergency medicineHealth care

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Providing effective outpatient care to oncology patients is the goal of all programs. There are two potential models of providing this care, a primary physician model which is the model generally employed by large oncology programs, and a team based model which is the model employed by small oncology programs. Medium sized programs (defined as 50-100 newly diagnosed patients per year), face a challenge as to what the best model of oncology outpatient care is to follow given the number of oncologists providing clinical care. We attempted to develop a hybrid model of team based and primary physician model in order to improve care of patients at our medium sized center. Methods: Prior to making any changes from the longstanding team based model of outpatient care, a patient satisfaction survey was conducted. Multiple meetings were held with the physician group to discuss the current model of care (team based model) and the potential ways to change the model given the complexity of patients and protocols. After much discussion it was decided that all patients would have a dedicated oncologist. There would then be two types of weeks of clinical service in the outpatient clinic. The first type was a “Doc of the Day” week where each oncologist would have a specific day in clinic and their assigned patients would be booked to come to clinic on those days. The second type was a “Doc of the Week” week where one physician would be attending in clinic for the week. There would be a 1:1 ratio of the two types of weeks. During vacations or holidays the week would be designated “Doc of the Week”. Results: The patient satisfaction survey done prior to changing the model of care showed that patients were very satisfied with the care they were receiving. A questionnaire to staff 14 months after the change in the model of care showed that the biggest effect was felt to be increased continuity of care to patients, followed by more efficient clinic work flow and increased consistency of care. The responses to what they liked best about the new model of care as members of the health care team, showed that facilitating the planning and delivery of care to patients and having a primary physician assigned to each patient were the most liked, followed by having their patient care questions answered more consistently because they knew which physician to direct the question to and physicians were more aware of their dedicated patients. The patient satisfaction survey post change in model of care showed that patients were still highly satisfied with the care they received. Conclusions: We showed that a model of care with a primary physician for each patient as well as assigned clinic days, alternating with some weeks where one physician covers the outpatient oncology patients for the whole week is a feasible model of care for a medium sized pediatric oncology program. The health care team found this model to be significantly better than a straight team based care model, but in a medium sized program with limited attending physicians, it provided a primary physician model that was felt to be beneficial for patients and other members of the health care team. Disclosures No relevant conflicts of interest to declare.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,048

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0020,001
Communication savante0,0040,003
Science ouverte0,0030,004
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0140,001

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,336
Écart entre enseignants0,319 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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