Telehealth—A Change in a Practice Model in Oncology
Bibliographic record
Abstract
BACKGROUND: A clinical study to examine the barriers to using telehealth for oncologic visits was performed by the British Columbia Cancer Agency's Vancouver Island Centre (BCCAVIC) and the Vancouver Island Health Authority in 2006-2007. One of the major barriers encountered was physician engagement. The current observational study was to determine whether patients' enthusiasm and the introduction of telehealth in a study resulted in telehealth becoming integrated within BCCAVIC. METHODS: Telehealth appointment statistics continued to be kept after the original study was completed. Data were kept on the number of visits, the type of visit (follow-up or new patient), the oncologist seeing the patient, the location of the patient, and the type of cancer. RESULTS: During the study, 106 patients were seen via telehealth. In the years following the trial, the number of telehealth follow-up patients seen markedly increased, so that in 2010-2011, close to 1,200 patients were seen. Medical oncology saw 91.4% of these. CONCLUSIONS: The introduction of oncology telehealth in BCCSVIC/Vancouver Island Health Authority was in an ethics-approved study. Following the completion of the trial, there was a 10-fold increase in follow-up patients seen using this modality. Reluctance to see new patients through telehealth probably relates to the necessity to change the patient encounter paradigm. There is a need to develop a model where patients who are a distance from specialists concentrated in larger centers have reasonable access to the same standard of care, without incurring the time and financial burdens. Telehealth would be a part of that model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".