Pediatric Oncology Clinic Care Model: How to Care for Patients to Achieve Better Continuity of Care in a Medium Sized Program
Notice bibliographique
Résumé
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.
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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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,001 |
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.
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 ».