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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 on OpenAlexaff
Donna Johnston, Jacqueline Halton, Mylène Bassal, Robert J. Klaassen, Karen Mandel, Raveena Ramphal, Ewurabena Simpson, Li Peckan

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineInternal medicineOutpatient clinicAmbulatory careOncologyFamily medicineEmergency medicineHealth care

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0030.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.336
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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