Stakeholder perspectives on the use of telehealth to improve ambulatory care for chemotherapy patients in a large urban cancer centre.
Bibliographic record
Abstract
86 Background: Chemotherapy outpatients are often left in vulnerable positions without direct access to their providers between appointments, which can lead to Emergency Department (ED) visits to address side effects. Improved use of telehealth has been postulated in the literature as a potential low-cost tool to manage this problem. Our objective was to explore, as a case study, how telehealth can be optimized to provide better care to ambulatory chemotherapy patients. Methods: This study was done at Princess Margaret Cancer Centre in Toronto. Semi-structured interviews (n = 21) were conducted to elicit a broad set of perspectives on the feasibility and constraints of implementing new telehealth measures in the breast cancer (BC) clinic. Interviewees included hospital administrators (n = 3), nurse managers (n = 4), BC nurses (n = 3), BC physicians (n = 2), one non-BC nurse, one pharmacist, BC patients (n = 4), and telehealth and technology experts (n = 3). Transcripts were reviewed separately by each author and themes were extracted using content analysis. Key learnings were established based on stakeholder agreement and theme novelty. Results: Provider-initiated proactive calling of chemotherapy patients was felt to bethe most valuable and feasible potential change according to all stakeholders. A number of key considerations emerged regarding the creation of a successful proactive calling system: 1) calls should address symptoms that are predictable, regimen-specific, and most likely to result in ED visits; 2) patients most likely to benefit are those beginning chemotherapy, starting new drugs, or fitting certain high-needs criteria; 3) structured call questionnaires can be valuable, but must be flexible to best meet patient needs; 4) the caller’s expertise is more important than his or her familiarity with the patient; and 5) basic IT support systems are necessary for operationalization. Conclusions: Simple telehealth initiatives such as proactive calls can improve outpatient care for chemotherapy patients, and may reduce ED burden. This study provides key principles that should guide development and implementation of proactive calling programs at cancer care institutions.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".